From fc003deea0fd1c88eb75e337fcc87b5139b841c3 Mon Sep 17 00:00:00 2001 From: charleschile Date: Sat, 16 Nov 2024 19:38:48 +0800 Subject: [PATCH 01/36] fastapi init --- fastapi4modelcache.py | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 fastapi4modelcache.py diff --git a/fastapi4modelcache.py b/fastapi4modelcache.py new file mode 100644 index 0000000..ae9058c --- /dev/null +++ b/fastapi4modelcache.py @@ -0,0 +1,3 @@ +import uvicorn +import asyncio +from fastapi import FastAPI, Request \ No newline at end of file From 642e1ca85fbd17c3008dd4a60d6a972af7fd4be5 Mon Sep 17 00:00:00 2001 From: charleschile Date: Sat, 16 Nov 2024 21:33:17 +0800 Subject: [PATCH 02/36] add fastapi4modelcache --- fastapi4modelcache.py | 151 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 150 insertions(+), 1 deletion(-) diff --git a/fastapi4modelcache.py b/fastapi4modelcache.py index ae9058c..58374c1 100644 --- a/fastapi4modelcache.py +++ b/fastapi4modelcache.py @@ -1,3 +1,152 @@ +# -*- coding: utf-8 -*- +import time import uvicorn import asyncio -from fastapi import FastAPI, Request \ No newline at end of file +import logging +import configparser +import json +from fastapi import FastAPI, Request, HTTPException +from pydantic import BaseModel +from concurrent.futures import ThreadPoolExecutor +from starlette.responses import PlainTextResponse + +from modelcache import cache +from modelcache.adapter import adapter +from modelcache.manager import CacheBase, VectorBase, get_data_manager +from modelcache.similarity_evaluation.distance import SearchDistanceEvaluation +from modelcache.processor.pre import query_multi_splicing +from modelcache.processor.pre import insert_multi_splicing +from modelcache.utils.model_filter import model_blacklist_filter +from modelcache.embedding import Data2VecAudio + +#创建一个FastAPI实例 +app = FastAPI() + +class RequestData(BaseModel): + type: str + scope: dict = None + query: str = None + chat_info: dict = None + remove_type: str = None + id_list: list = [] + +data2vec = Data2VecAudio() +mysql_config = configparser.ConfigParser() +mysql_config.read('modelcache/config/mysql_config.ini') + +milvus_config = configparser.ConfigParser() +milvus_config.read('modelcache/config/milvus_config.ini') + +# redis_config = configparser.ConfigParser() +# redis_config.read('modelcache/config/redis_config.ini') + +# 初始化datamanager +data_manager = get_data_manager( + CacheBase("mysql", config=mysql_config), + VectorBase("milvus", dimension=data2vec.dimension, milvus_config=milvus_config) +) + +# # 使用redis初始化datamanager +# data_manager = get_data_manager( +# CacheBase("mysql", config=mysql_config), +# VectorBase("redis", dimension=data2vec.dimension, redis_config=redis_config) +# ) + +cache.init( + embedding_func=data2vec.to_embeddings, + data_manager=data_manager, + similarity_evaluation=SearchDistanceEvaluation(), + query_pre_embedding_func=query_multi_splicing, + insert_pre_embedding_func=insert_multi_splicing, +) + +executor = ThreadPoolExecutor(max_workers=6) + +# 异步保存查询信息 +async def save_query_info(result, model, query, delta_time_log): + loop = asyncio.get_running_loop() + await loop.run_in_executor(executor, cache.data_manager.save_query_resp, result, model, json.dumps(query, ensure_ascii=False), delta_time_log) + + + +@app.get("/welcome", response_class=PlainTextResponse) +async def first_fastapi(): + return "hello, modelcache!" + +@app.post("/modelcache") +async def user_backend(request_data: RequestData): + # param parsing + try: + request_type = request_data.type + model = None + if request_data.scope: + model = request_data.scope.get('model', '').replace('-','_').replace('.', '_') + query = request_data.query + chat_info = request_data.chat_info + + except Exception as e: + result = {"errorCode": 103, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} + return result + + # model filter + filter_resp = model_blacklist_filter(model, request_type) + if isinstance(filter_resp, dict): + return filter_resp + + if request_type == 'query': + try: + start_time = time.time() + response = adapter.ChatCompletion.create_query(scope={"model": model}, query=query) + delta_time = f"{round(time.time() - start_time, 2)}s" + + if response is None: + result = {"errorCode": 0, "errorDesc": '', "cacheHit": False, "delta_time": delta_time, "hit_query": '', "answer": ''} + # elif response in ['adapt_query_exception']: + elif isinstance(response, str): + result = {"errorCode": 201, "errorDesc": response, "cacheHit": False, "delta_time": delta_time, + "hit_query": '', "answer": ''} + else: + answer = response['data'] + hit_query = response['hitQuery'] + result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, "hit_query": hit_query, "answer": answer} + + delta_time_log = round(time.time() - start_time, 2) + asyncio.create_task(save_query_info(result, model, query, delta_time_log)) + return result + except Exception as e: + result = {"errorCode": 202, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, + "hit_query": '', "answer": ''} + logging.info(f'result: {str(result)}') + return result + + if request_type == 'insert': + try: + response = adapter.ChatCompletion.create_insert(model=model, chat_info=chat_info) + if response == 'success': + return {"errorCode": 0, "errorDesc": "", "writeStatus": "success"} + else: + return {"errorCode": 301, "errorDesc": response, "writeStatus": "exception"} + except Exception as e: + return {"errorCode": 303, "errorDesc": str(e), "writeStatus": "exception"} + + if request_type == 'remove': + response = adapter.ChatCompletion.create_remove(model=model, remove_type=request_data.remove_type, id_list=request_data.id_list) + if not isinstance(response, dict): + return {"errorCode": 401, "errorDesc": "", "response": response, "removeStatus": "exception"} + + state = response.get('status') + if state == 'success': + return {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} + else: + return {"errorCode": 402, "errorDesc": "", "response": response, "writeStatus": "exception"} + + if request_type == 'register': + response = adapter.ChatCompletion.create_register(model=model) + if response in ['create_success', 'already_exists']: + return {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} + else: + return {"errorCode": 502, "errorDesc": "", "response": response, "writeStatus": "exception"} + +# TODO: 可以修改为在命令行中使用`uvicorn your_module_name:app --host 0.0.0.0 --port 5000 --reload`的命令启动 +if __name__ == '__main__': + uvicorn.run(app, host='0.0.0.0', port=5001) \ No newline at end of file From 72018ce28cfce262ee4dad4c1db0ee5d8f2f56c7 Mon Sep 17 00:00:00 2001 From: charleschile Date: Wed, 20 Nov 2024 13:17:55 +0800 Subject: [PATCH 03/36] fastapi for modelcache_demo --- fastapi4modelcache_demo.py | 162 ++++++++++++++++++ .../manager/scalar_data/sql_storage_sqlite.py | 3 +- 2 files changed, 163 insertions(+), 2 deletions(-) create mode 100644 fastapi4modelcache_demo.py diff --git a/fastapi4modelcache_demo.py b/fastapi4modelcache_demo.py new file mode 100644 index 0000000..624fc75 --- /dev/null +++ b/fastapi4modelcache_demo.py @@ -0,0 +1,162 @@ +# -*- coding: utf-8 -*- +import time +import uvicorn +import asyncio +import logging +# import configparser +import json +from fastapi import FastAPI, Request, HTTPException +from pydantic import BaseModel +from concurrent.futures import ThreadPoolExecutor +from starlette.responses import PlainTextResponse +import functools + +from modelcache import cache +from modelcache.adapter import adapter +from modelcache.manager import CacheBase, VectorBase, get_data_manager +from modelcache.similarity_evaluation.distance import SearchDistanceEvaluation +from modelcache.processor.pre import query_multi_splicing +from modelcache.processor.pre import insert_multi_splicing +from modelcache.utils.model_filter import model_blacklist_filter +from modelcache.embedding import Data2VecAudio + +# 创建一个FastAPI实例 +app = FastAPI() + +class RequestData(BaseModel): + type: str + scope: dict = None + query: str = None + chat_info: list = None + remove_type: str = None + id_list: list = [] + +data2vec = Data2VecAudio() + +data_manager = get_data_manager(CacheBase("sqlite"), VectorBase("faiss", dimension=data2vec.dimension)) + +cache.init( + embedding_func=data2vec.to_embeddings, + data_manager=data_manager, + similarity_evaluation=SearchDistanceEvaluation(), + query_pre_embedding_func=query_multi_splicing, + insert_pre_embedding_func=insert_multi_splicing, +) + +executor = ThreadPoolExecutor(max_workers=6) + +# 异步保存查询信息 +async def save_query_info_fastapi(result, model, query, delta_time_log): + loop = asyncio.get_running_loop() + func = functools.partial(cache.data_manager.save_query_resp, result, model=model, query=json.dumps(query, ensure_ascii=False), delta_time=delta_time_log) + await loop.run_in_executor(None, func) + + + +@app.get("/welcome", response_class=PlainTextResponse) +async def first_fastapi(): + return "hello, modelcache!" + +@app.post("/modelcache") +async def user_backend(request: Request): + try: + raw_body = await request.body() + # 解析字符串为JSON对象 + if isinstance(raw_body, bytes): + raw_body = raw_body.decode("utf-8") + if isinstance(raw_body, str): + try: + # 尝试将字符串解析为JSON对象 + request_data = json.loads(raw_body) + except json.JSONDecodeError: + # 如果无法解析，返回格式错误 + raise HTTPException(status_code=400, detail="Invalid JSON format") + else: + request_data = raw_body + + # 确保request_data是字典对象 + if isinstance(request_data, str): + try: + request_data = json.loads(request_data) + except json.JSONDecodeError: + raise HTTPException(status_code=400, detail="Invalid JSON format") + + request_type = request_data.get('type') + model = None + if 'scope' in request_data: + model = request_data['scope'].get('model', '').replace('-', '_').replace('.', '_') + query = request_data.get('query') + chat_info = request_data.get('chat_info') + + if not request_type or request_type not in ['query', 'insert', 'remove', 'detox']: + raise HTTPException(status_code=400, detail="Type exception, should be one of ['query', 'insert', 'remove', 'detox']") + + except Exception as e: + request_data = raw_body if 'raw_body' in locals() else None + result = { + "errorCode": 103, + "errorDesc": str(e), + "cacheHit": False, + "delta_time": 0, + "hit_query": '', + "answer": '', + "para_dict": request_data + } + return result + + + # model filter + filter_resp = model_blacklist_filter(model, request_type) + if isinstance(filter_resp, dict): + return filter_resp + + if request_type == 'query': + try: + start_time = time.time() + response = adapter.ChatCompletion.create_query(scope={"model": model}, query=query) + delta_time = f"{round(time.time() - start_time, 2)}s" + + if response is None: + result = {"errorCode": 0, "errorDesc": '', "cacheHit": False, "delta_time": delta_time, "hit_query": '', "answer": ''} + elif response in ['adapt_query_exception']: + # elif isinstance(response, str): + result = {"errorCode": 201, "errorDesc": response, "cacheHit": False, "delta_time": delta_time, + "hit_query": '', "answer": ''} + else: + answer = response['data'] + hit_query = response['hitQuery'] + result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, "hit_query": hit_query, "answer": answer} + + delta_time_log = round(time.time() - start_time, 2) + asyncio.create_task(save_query_info_fastapi(result, model, query, delta_time_log)) + return result + except Exception as e: + result = {"errorCode": 202, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, + "hit_query": '', "answer": ''} + logging.info(f'result: {str(result)}') + return result + + if request_type == 'insert': + try: + response = adapter.ChatCompletion.create_insert(model=model, chat_info=chat_info) + if response == 'success': + return {"errorCode": 0, "errorDesc": "", "writeStatus": "success"} + else: + return {"errorCode": 301, "errorDesc": response, "writeStatus": "exception"} + except Exception as e: + return {"errorCode": 303, "errorDesc": str(e), "writeStatus": "exception"} + + if request_type == 'remove': + response = adapter.ChatCompletion.create_remove(model=model, remove_type=request_data.get("remove_type"), id_list=request_data.get("id_list")) + if not isinstance(response, dict): + return {"errorCode": 401, "errorDesc": "", "response": response, "removeStatus": "exception"} + + state = response.get('status') + if state == 'success': + return {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} + else: + return {"errorCode": 402, "errorDesc": "", "response": response, "writeStatus": "exception"} + +# TODO: 可以修改为在命令行中使用`uvicorn your_module_name:app --host 0.0.0.0 --port 5000 --reload`的命令启动 +if __name__ == '__main__': + uvicorn.run(app, host='0.0.0.0', port=5000) \ No newline at end of file diff --git a/modelcache/manager/scalar_data/sql_storage_sqlite.py b/modelcache/manager/scalar_data/sql_storage_sqlite.py index 3c68f9e..b7463c5 100644 --- a/modelcache/manager/scalar_data/sql_storage_sqlite.py +++ b/modelcache/manager/scalar_data/sql_storage_sqlite.py @@ -100,8 +100,7 @@ def insert_query_resp(self, query_resp, **kwargs): hit_query = json.dumps(hit_query, ensure_ascii=False) table_name = "modelcache_query_log" - insert_sql = "INSERT INTO {} (error_code, error_desc, cache_hit, model, query, delta_time, hit_query, answer) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)".format(table_name) - + insert_sql = "INSERT INTO {} (error_code, error_desc, cache_hit, model, query, delta_time, hit_query, answer) VALUES (?, ?, ?, ?, ?, ?, ?, ?)".format(table_name) conn = sqlite3.connect(self._url) try: cursor = conn.cursor() From 3dbc951c9bd7390f66492e357a84914b101b0d96 Mon Sep 17 00:00:00 2001 From: charleschile Date: Wed, 20 Nov 2024 14:16:22 +0800 Subject: [PATCH 04/36] fastapi for modelcache --- examples/flask/llms_cache/data_insert.py | 1 + examples/flask/llms_cache/data_query.py | 1 + examples/flask/llms_cache/data_query_long.py | 1 + fastapi4modelcache.py | 62 +++++++++++++++----- 4 files changed, 51 insertions(+), 14 deletions(-) diff --git a/examples/flask/llms_cache/data_insert.py b/examples/flask/llms_cache/data_insert.py index 52d1405..e26bec7 100644 --- a/examples/flask/llms_cache/data_insert.py +++ b/examples/flask/llms_cache/data_insert.py @@ -14,6 +14,7 @@ def run(): res = requests.post(url, headers=headers, json=json.dumps(data)) res_text = res.text + print("data_insert:", res.status_code, res_text) if __name__ == '__main__': run() diff --git a/examples/flask/llms_cache/data_query.py b/examples/flask/llms_cache/data_query.py index aa59ae3..6fe8313 100644 --- a/examples/flask/llms_cache/data_query.py +++ b/examples/flask/llms_cache/data_query.py @@ -14,6 +14,7 @@ def run(): res = requests.post(url, headers=headers, json=json.dumps(data)) res_text = res.text + print("data_query:", res.status_code, res_text) if __name__ == '__main__': run() diff --git a/examples/flask/llms_cache/data_query_long.py b/examples/flask/llms_cache/data_query_long.py index e8c29f8..7aa4f14 100644 --- a/examples/flask/llms_cache/data_query_long.py +++ b/examples/flask/llms_cache/data_query_long.py @@ -19,6 +19,7 @@ def run(): res = requests.post(url, headers=headers, json=json.dumps(data)) res_text = res.text + print("data_query_long:", res.status_code, res_text) if __name__ == '__main__': run() diff --git a/fastapi4modelcache.py b/fastapi4modelcache.py index 58374c1..6e85612 100644 --- a/fastapi4modelcache.py +++ b/fastapi4modelcache.py @@ -9,6 +9,7 @@ from pydantic import BaseModel from concurrent.futures import ThreadPoolExecutor from starlette.responses import PlainTextResponse +import functools from modelcache import cache from modelcache.adapter import adapter @@ -63,9 +64,10 @@ class RequestData(BaseModel): executor = ThreadPoolExecutor(max_workers=6) # 异步保存查询信息 -async def save_query_info(result, model, query, delta_time_log): +async def save_query_info_fastapi(result, model, query, delta_time_log): loop = asyncio.get_running_loop() - await loop.run_in_executor(executor, cache.data_manager.save_query_resp, result, model, json.dumps(query, ensure_ascii=False), delta_time_log) + func = functools.partial(cache.data_manager.save_query_resp, result, model=model, query=json.dumps(query, ensure_ascii=False), delta_time=delta_time_log) + await loop.run_in_executor(None, func) @@ -74,20 +76,53 @@ async def first_fastapi(): return "hello, modelcache!" @app.post("/modelcache") -async def user_backend(request_data: RequestData): - # param parsing +async def user_backend(request: Request): try: - request_type = request_data.type + raw_body = await request.body() + # 解析字符串为JSON对象 + if isinstance(raw_body, bytes): + raw_body = raw_body.decode("utf-8") + if isinstance(raw_body, str): + try: + # 尝试将字符串解析为JSON对象 + request_data = json.loads(raw_body) + except json.JSONDecodeError: + # 如果无法解析，返回格式错误 + raise HTTPException(status_code=400, detail="Invalid JSON format") + else: + request_data = raw_body + + # 确保request_data是字典对象 + if isinstance(request_data, str): + try: + request_data = json.loads(request_data) + except json.JSONDecodeError: + raise HTTPException(status_code=400, detail="Invalid JSON format") + + request_type = request_data.get('type') model = None - if request_data.scope: - model = request_data.scope.get('model', '').replace('-','_').replace('.', '_') - query = request_data.query - chat_info = request_data.chat_info + if 'scope' in request_data: + model = request_data['scope'].get('model', '').replace('-', '_').replace('.', '_') + query = request_data.get('query') + chat_info = request_data.get('chat_info') + + if not request_type or request_type not in ['query', 'insert', 'remove', 'detox']: + raise HTTPException(status_code=400, detail="Type exception, should be one of ['query', 'insert', 'remove', 'detox']") except Exception as e: - result = {"errorCode": 103, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} + request_data = raw_body if 'raw_body' in locals() else None + result = { + "errorCode": 103, + "errorDesc": str(e), + "cacheHit": False, + "delta_time": 0, + "hit_query": '', + "answer": '', + "para_dict": request_data + } return result + # model filter filter_resp = model_blacklist_filter(model, request_type) if isinstance(filter_resp, dict): @@ -101,8 +136,7 @@ async def user_backend(request_data: RequestData): if response is None: result = {"errorCode": 0, "errorDesc": '', "cacheHit": False, "delta_time": delta_time, "hit_query": '', "answer": ''} - # elif response in ['adapt_query_exception']: - elif isinstance(response, str): + elif response in ['adapt_query_exception']: result = {"errorCode": 201, "errorDesc": response, "cacheHit": False, "delta_time": delta_time, "hit_query": '', "answer": ''} else: @@ -111,7 +145,7 @@ async def user_backend(request_data: RequestData): result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, "hit_query": hit_query, "answer": answer} delta_time_log = round(time.time() - start_time, 2) - asyncio.create_task(save_query_info(result, model, query, delta_time_log)) + asyncio.create_task(save_query_info_fastapi(result, model, query, delta_time_log)) return result except Exception as e: result = {"errorCode": 202, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, @@ -130,7 +164,7 @@ async def user_backend(request_data: RequestData): return {"errorCode": 303, "errorDesc": str(e), "writeStatus": "exception"} if request_type == 'remove': - response = adapter.ChatCompletion.create_remove(model=model, remove_type=request_data.remove_type, id_list=request_data.id_list) + response = adapter.ChatCompletion.create_remove(model=model, remove_type=request_data.get("remove_type"), id_list=request_data.get("id_list")) if not isinstance(response, dict): return {"errorCode": 401, "errorDesc": "", "response": response, "removeStatus": "exception"} From e490aeeb08251485275163b7ecdeb6424f03e327 Mon Sep 17 00:00:00 2001 From: charleschile Date: Wed, 20 Nov 2024 16:08:38 +0800 Subject: [PATCH 05/36] fastapi requirementsd --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 78d682e..3a8d124 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,3 +12,4 @@ transformers==4.38.2 faiss-cpu==1.7.4 redis==5.0.1 modelscope==1.14.0 +fastapi==0.115.5 \ No newline at end of file From 850bbca988ca760b824c3250227d0e9801540121 Mon Sep 17 00:00:00 2001 From: charleschile Date: Wed, 20 Nov 2024 16:18:59 +0800 Subject: [PATCH 06/36] fix bug --- fastapi4modelcache.py | 2 +- requirements.txt | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/fastapi4modelcache.py b/fastapi4modelcache.py index 6e85612..34a6bdc 100644 --- a/fastapi4modelcache.py +++ b/fastapi4modelcache.py @@ -183,4 +183,4 @@ async def user_backend(request: Request): # TODO: 可以修改为在命令行中使用`uvicorn your_module_name:app --host 0.0.0.0 --port 5000 --reload`的命令启动 if __name__ == '__main__': - uvicorn.run(app, host='0.0.0.0', port=5001) \ No newline at end of file + uvicorn.run(app, host='0.0.0.0', port=5000) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 3a8d124..95e1948 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,4 +12,5 @@ transformers==4.38.2 faiss-cpu==1.7.4 redis==5.0.1 modelscope==1.14.0 -fastapi==0.115.5 \ No newline at end of file +fastapi==0.115.5 +uvicorn==0.32.0 \ No newline at end of file From c000a42cd5d91e32608794a826c9a769ac0419ed Mon Sep 17 00:00:00 2001 From: charleschile Date: Wed, 20 Nov 2024 17:31:50 +0800 Subject: [PATCH 07/36] fix bug --- fastapi4modelcache.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/fastapi4modelcache.py b/fastapi4modelcache.py index 34a6bdc..f9d0267 100644 --- a/fastapi4modelcache.py +++ b/fastapi4modelcache.py @@ -64,7 +64,7 @@ class RequestData(BaseModel): executor = ThreadPoolExecutor(max_workers=6) # 异步保存查询信息 -async def save_query_info_fastapi(result, model, query, delta_time_log): +async def save_query_info(result, model, query, delta_time_log): loop = asyncio.get_running_loop() func = functools.partial(cache.data_manager.save_query_resp, result, model=model, query=json.dumps(query, ensure_ascii=False), delta_time=delta_time_log) await loop.run_in_executor(None, func) @@ -86,9 +86,12 @@ async def user_backend(request: Request): try: # 尝试将字符串解析为JSON对象 request_data = json.loads(raw_body) - except json.JSONDecodeError: + except json.JSONDecodeError as e: # 如果无法解析，返回格式错误 - raise HTTPException(status_code=400, detail="Invalid JSON format") + result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', + "answer": ''} + asyncio.create_task(save_query_info(result, model='', query='', delta_time_log=0)) + raise HTTPException(status_code=101, detail="Invalid JSON format") else: request_data = raw_body @@ -97,7 +100,7 @@ async def user_backend(request: Request): try: request_data = json.loads(request_data) except json.JSONDecodeError: - raise HTTPException(status_code=400, detail="Invalid JSON format") + raise HTTPException(status_code=101, detail="Invalid JSON format") request_type = request_data.get('type') model = None @@ -106,8 +109,12 @@ async def user_backend(request: Request): query = request_data.get('query') chat_info = request_data.get('chat_info') - if not request_type or request_type not in ['query', 'insert', 'remove', 'detox']: - raise HTTPException(status_code=400, detail="Type exception, should be one of ['query', 'insert', 'remove', 'detox']") + if not request_type or request_type not in ['query', 'insert', 'remove', 'register']: + result = {"errorCode": 102, + "errorDesc": "type exception, should one of ['query', 'insert', 'remove', 'register']", + "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} + asyncio.create_task(save_query_info(result, model=model, query='', delta_time_log=0)) + raise HTTPException(status_code=102, detail="Type exception, should be one of ['query', 'insert', 'remove', 'register']") except Exception as e: request_data = raw_body if 'raw_body' in locals() else None @@ -145,7 +152,7 @@ async def user_backend(request: Request): result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, "hit_query": hit_query, "answer": answer} delta_time_log = round(time.time() - start_time, 2) - asyncio.create_task(save_query_info_fastapi(result, model, query, delta_time_log)) + asyncio.create_task(save_query_info(result, model, query, delta_time_log)) return result except Exception as e: result = {"errorCode": 202, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, From 2d692ae09c238589f29ab8233216407eca5d2c5d Mon Sep 17 00:00:00 2001 From: powerli2002 <845160705@qq.com> Date: Mon, 9 Dec 2024 22:09:47 +0800 Subject: [PATCH 08/36] Add feature : Using elasticsearch as a scalar database --- flask4modelcache.py | 5 +- modelcache/config/elasticsearch_config.ini | 5 + modelcache/manager/scalar_data/manager.py | 4 + .../manager/scalar_data/sql_storage_es.py | 195 ++++++++++++++++++ requirements.txt | 4 +- 5 files changed, 211 insertions(+), 2 deletions(-) create mode 100644 modelcache/config/elasticsearch_config.ini create mode 100644 modelcache/manager/scalar_data/sql_storage_es.py diff --git a/flask4modelcache.py b/flask4modelcache.py index ee5955b..3d15a68 100644 --- a/flask4modelcache.py +++ b/flask4modelcache.py @@ -38,11 +38,14 @@ def response_hitquery(cache_resp): milvus_config = configparser.ConfigParser() milvus_config.read('modelcache/config/milvus_config.ini') +es_config = configparser.ConfigParser() +es_config.read('modelcache/config/elasticsearch_config.ini') + # redis_config = configparser.ConfigParser() # redis_config.read('modelcache/config/redis_config.ini') -data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), +data_manager = get_data_manager(CacheBase("elasticsearch", config=es_config), VectorBase("milvus", dimension=data2vec.dimension, milvus_config=milvus_config)) # data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), diff --git a/modelcache/config/elasticsearch_config.ini b/modelcache/config/elasticsearch_config.ini new file mode 100644 index 0000000..ba87a5a --- /dev/null +++ b/modelcache/config/elasticsearch_config.ini @@ -0,0 +1,5 @@ +[elasticsearch] +host = '' +port = '' +user = '' +password = '' \ No newline at end of file diff --git a/modelcache/manager/scalar_data/manager.py b/modelcache/manager/scalar_data/manager.py index 4c02c45..8ff3aee 100644 --- a/modelcache/manager/scalar_data/manager.py +++ b/modelcache/manager/scalar_data/manager.py @@ -27,6 +27,10 @@ def get(name, **kwargs): from modelcache.manager.scalar_data.sql_storage_sqlite import SQLStorage sql_url = kwargs.get("sql_url", SQL_URL[name]) cache_base = SQLStorage(db_type=name, url=sql_url) + elif name == 'elasticsearch': + from modelcache.manager.scalar_data.sql_storage_es import SQLStorage + config = kwargs.get("config") + cache_base = SQLStorage(db_type=name, config=config) else: raise NotFoundError("cache store", name) return cache_base diff --git a/modelcache/manager/scalar_data/sql_storage_es.py b/modelcache/manager/scalar_data/sql_storage_es.py new file mode 100644 index 0000000..68bcb31 --- /dev/null +++ b/modelcache/manager/scalar_data/sql_storage_es.py @@ -0,0 +1,195 @@ +# -*- coding: utf-8 -*- +import json +from typing import List +from elasticsearch import Elasticsearch, helpers +from modelcache.manager.scalar_data.base import CacheStorage, CacheData +import time +from snowflake import SnowflakeGenerator + + +class SQLStorage(CacheStorage): + def __init__( + self, + db_type: str = "elasticsearch", + config=None + ): + self.host = config.get('elasticsearch', 'host') + self.port = int(config.get('elasticsearch', 'port')) + self.client = Elasticsearch( + hosts=[{"host": self.host, "port": self.port}], + timeout=30, + http_auth=('esuser', 'password') + ) + + self.log_index = "modelcache_query_log" + self.ans_index = "modelcache_llm_answer" + self.create() + self.instance_id = 1 # 雪花算法使用的机器id 使用同一套数据库的分布式系统需要配置不同id + # 生成雪花id + self.snowflake_id = SnowflakeGenerator(self.instance_id) + + def create(self): + answer_index_body = { + "mappings": { + "properties": { + "gmt_create": {"type": "date", "format": "strict_date_optional_time||epoch_millis"}, + "gmt_modified": {"type": "date", "format": "strict_date_optional_time||epoch_millis"}, + "question": {"type": "text"}, + "answer": {"type": "text"}, + "answer_type": {"type": "integer"}, + "hit_count": {"type": "integer"}, + "model": {"type": "keyword"}, + "embedding_data": {"type": "binary"}, + "is_deleted": {"type": "integer"}, + } + } + } + + log_index_body = { + "mappings": { + "properties": { + "gmt_create": {"type": "date", "format": "strict_date_optional_time||epoch_millis"}, + "gmt_modified": {"type": "date", "format": "strict_date_optional_time||epoch_millis"}, + "error_code": {"type": "integer"}, + "error_desc": {"type": "text"}, + "cache_hit": {"type": "keyword"}, + "delta_time": {"type": "float"}, + "model": {"type": "keyword"}, + "query": {"type": "text"}, + "hit_query": {"type": "text"}, + "answer": {"type": "text"} + } + } + } + + if not self.client.indices.exists(index="modelcache_llm_answer"): + self.client.indices.create(index="modelcache_llm_answer", body=answer_index_body) + + if not self.client.indices.exists(index="modelcache_query_log"): + self.client.indices.create(index="modelcache_query_log", body=log_index_body) + + def _insert(self, data: List) -> str or None: + doc = { + "answer": data[0], + "question": data[1], + "embedding_data": data[2].tolist() if hasattr(data[2], "tolist") else data[2], + "model": data[3], + "answer_type": 0, + "hit_count": 0, + "is_deleted": 0 + } + + try: + + response = self.client.index( + index=self.ans_index, + id=next(self.snowflake_id), + body=doc, + ) + return int(response['_id']) + except Exception as e: + + print(f"Failed to insert document: {e}") + return None + + def batch_insert(self, all_data: List[List]) -> List[str]: + successful_ids = [] + for data in all_data: + _id = self._insert(data) + if _id is not None: + successful_ids.append(_id) + self.client.indices.refresh(index=self.ans_index) # 批量插入后手动刷新 + + return successful_ids + + def insert_query_resp(self, query_resp, **kwargs): + doc = { + "error_code": query_resp.get('errorCode'), + "error_desc": query_resp.get('errorDesc'), + "cache_hit": query_resp.get('cacheHit'), + "model": kwargs.get('model'), + "query": kwargs.get('query'), + "delta_time": kwargs.get('delta_time'), + "hit_query": json.dumps(query_resp.get('hit_query'), ensure_ascii=False) if isinstance( + query_resp.get('hit_query'), list) else query_resp.get('hit_query'), + "answer": query_resp.get('answer'), + "hit_count": 0, + "is_deleted": 0 + + } + self.client.index(index=self.log_index, body=doc) + + def get_data_by_id(self, key: int): + try: + response = self.client.get(index=self.ans_index, id=key, _source=['question', 'answer', 'embedding_data', 'model']) + source = response["_source"] + result = [ + source.get('question'), + source.get('answer'), + source.get('embedding_data'), + source.get('model') + ] + return result + except Exception as e: + print(e) + + def update_hit_count_by_id(self, primary_id: int): + self.client.update( + index=self.ans_index, + id=primary_id, + body={"script": {"source": "ctx._source.hit_count += 1"}} + ) + + def get_ids(self, deleted=True): + query = { + "query": { + "term": {"is_deleted": 1 if deleted else 0} + } + } + response = self.client.search(index=self.ans_index, body=query) + return [hit["_id"] for hit in response["hits"]["hits"]] + + def mark_deleted(self, keys): + actions = [ + { + "_op_type": "update", + "_index": self.ans_index, + "_id": key, + "doc": {"is_deleted": 1} + } + for key in keys + ] + responses = helpers.bulk(self.client, actions) + return responses[0] # 返回更新的文档数 + + def model_deleted(self, model_name): + query = { + "query": { + "term": {"model": model_name} + } + } + + response = self.client.delete_by_query(index=self.ans_index, body=query) + return response["deleted"] + + def clear_deleted_data(self): + query = { + "query": { + "term": {"is_deleted": 1} + } + } + response = self.client.delete_by_query(index=self.ans_index, body=query) + return response["deleted"] + + def count(self, state: int = 0, is_all: bool = False): + query = {"query": {"match_all": {}}} if is_all else {"query": {"term": {"is_deleted": state}}} + response = self.client.count(index=self.ans_index, body=query) + return response["count"] + + def close(self): + self.client.close() + + def count_answers(self): + query = {"query": {"match_all": {}}} + response = self.client.count(index=self.ans_index, body=query) + return response["count"] diff --git a/requirements.txt b/requirements.txt index 95e1948..63899e9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,4 +13,6 @@ faiss-cpu==1.7.4 redis==5.0.1 modelscope==1.14.0 fastapi==0.115.5 -uvicorn==0.32.0 \ No newline at end of file +uvicorn==0.32.0 +elasticsearch==7.10.0 +snowflake-id==1.0.2 \ No newline at end of file From 682179b50a7323753ad41d3f02f0a80362e03704 Mon Sep 17 00:00:00 2001 From: powerli2002 <845160705@qq.com> Date: Mon, 9 Dec 2024 22:43:07 +0800 Subject: [PATCH 09/36] Add feature : add timm support --- .../manager/scalar_data/sql_storage_es.py | 8 +- modelcache_mm/config/elasticsearch_config.ini | 5 + .../manager/scalar_data/sql_storage_es.py | 198 ++++++++++++++++++ 3 files changed, 207 insertions(+), 4 deletions(-) create mode 100644 modelcache_mm/config/elasticsearch_config.ini create mode 100644 modelcache_mm/manager/scalar_data/sql_storage_es.py diff --git a/modelcache/manager/scalar_data/sql_storage_es.py b/modelcache/manager/scalar_data/sql_storage_es.py index 68bcb31..7e0184a 100644 --- a/modelcache/manager/scalar_data/sql_storage_es.py +++ b/modelcache/manager/scalar_data/sql_storage_es.py @@ -62,11 +62,11 @@ def create(self): } } - if not self.client.indices.exists(index="modelcache_llm_answer"): - self.client.indices.create(index="modelcache_llm_answer", body=answer_index_body) + if not self.client.indices.exists(index=self.ans_index): + self.client.indices.create(index=self.ans_index, body=answer_index_body) - if not self.client.indices.exists(index="modelcache_query_log"): - self.client.indices.create(index="modelcache_query_log", body=log_index_body) + if not self.client.indices.exists(index=self.log_index): + self.client.indices.create(index=self.log_index, body=log_index_body) def _insert(self, data: List) -> str or None: doc = { diff --git a/modelcache_mm/config/elasticsearch_config.ini b/modelcache_mm/config/elasticsearch_config.ini new file mode 100644 index 0000000..ba87a5a --- /dev/null +++ b/modelcache_mm/config/elasticsearch_config.ini @@ -0,0 +1,5 @@ +[elasticsearch] +host = '' +port = '' +user = '' +password = '' \ No newline at end of file diff --git a/modelcache_mm/manager/scalar_data/sql_storage_es.py b/modelcache_mm/manager/scalar_data/sql_storage_es.py new file mode 100644 index 0000000..562b349 --- /dev/null +++ b/modelcache_mm/manager/scalar_data/sql_storage_es.py @@ -0,0 +1,198 @@ +# -*- coding: utf-8 -*- +import json +from typing import List +from elasticsearch import Elasticsearch, helpers +from modelcache.manager.scalar_data.base import CacheStorage, CacheData +import time +from snowflake import SnowflakeGenerator + + +class SQLStorage(CacheStorage): + def __init__( + self, + db_type: str = "elasticsearch", + config=None + ): + self.host = config.get('elasticsearch', 'host') + self.port = int(config.get('elasticsearch', 'port')) + self.client = Elasticsearch( + hosts=[{"host": self.host, "port": self.port}], + timeout=30, + http_auth=('esuser', 'password') + ) + + self.log_index = "open_cache_mm_query_log" + self.ans_index = "open_cache_mm_answer" + self.create() + self.instance_id = 1 # 雪花算法使用的机器id 使用同一套数据库的分布式系统需要配置不同id + # 生成雪花id + self.snowflake_id = SnowflakeGenerator(self.instance_id) + + def create(self): + answer_index_body = { + "mappings": { + "properties": { + "gmt_create": {"type": "date", "format": "strict_date_optional_time||epoch_millis"}, + "gmt_modified": {"type": "date", "format": "strict_date_optional_time||epoch_millis"}, + "question": {"type": "text"}, + "answer": {"type": "text"}, + "answer_type": {"type": "integer"}, + "hit_count": {"type": "integer"}, + "model": {"type": "keyword"}, + "image_url": {"type": "text"}, + "image_id": {"type": "text"}, + "is_deleted": {"type": "integer"}, + } + } + } + + log_index_body = { + "mappings": { + "properties": { + "gmt_create": {"type": "date", "format": "strict_date_optional_time||epoch_millis"}, + "gmt_modified": {"type": "date", "format": "strict_date_optional_time||epoch_millis"}, + "error_code": {"type": "integer"}, + "error_desc": {"type": "text"}, + "cache_hit": {"type": "keyword"}, + "delta_time": {"type": "float"}, + "model": {"type": "keyword"}, + "query": {"type": "text"}, + "hit_query": {"type": "text"}, + "answer": {"type": "text"} + } + } + } + + if not self.client.indices.exists(index=self.ans_index): + self.client.indices.create(index=self.ans_index, body=answer_index_body) + + if not self.client.indices.exists(index=self.log_index): + self.client.indices.create(index=self.log_index, body=log_index_body) + + def _insert(self, data: List) -> str or None: + doc = { + "answer": data[0], + "question": data[1], + "image_url": data[2], + "image_id": data[3], + "model": data[4], + "answer_type": 0, + "hit_count": 0, + "is_deleted": 0 + } + + try: + + response = self.client.index( + index=self.ans_index, + id=next(self.snowflake_id), + body=doc, + ) + return int(response['_id']) + except Exception as e: + + print(f"Failed to insert document: {e}") + return None + + def batch_insert(self, all_data: List[List]) -> List[str]: + successful_ids = [] + for data in all_data: + _id = self._insert(data) + if _id is not None: + successful_ids.append(_id) + self.client.indices.refresh(index=self.ans_index) # 批量插入后手动刷新 + + return successful_ids + + def insert_query_resp(self, query_resp, **kwargs): + doc = { + "error_code": query_resp.get('errorCode'), + "error_desc": query_resp.get('errorDesc'), + "cache_hit": query_resp.get('cacheHit'), + "model": kwargs.get('model'), + "query": kwargs.get('query'), + "delta_time": kwargs.get('delta_time'), + "hit_query": json.dumps(query_resp.get('hit_query'), ensure_ascii=False) if isinstance( + query_resp.get('hit_query'), list) else query_resp.get('hit_query'), + "answer": query_resp.get('answer'), + "hit_count": 0, + "is_deleted": 0 + + } + self.client.index(index=self.log_index, body=doc) + + def get_data_by_id(self, key: int): + try: + response = self.client.get(index=self.ans_index, id=key, _source=['question', 'image_url','image_id', 'answer', 'model']) + source = response["_source"] + result = [ + source.get('question'), + source.get('image_url'), + source.get('image_id'), + source.get('answer'), + source.get('model') + ] + return result + except Exception as e: + print(e) + + def update_hit_count_by_id(self, primary_id: int): + self.client.update( + index=self.ans_index, + id=primary_id, + body={"script": {"source": "ctx._source.hit_count += 1"}} + ) + + def get_ids(self, deleted=True): + query = { + "query": { + "term": {"is_deleted": 1 if deleted else 0} + } + } + response = self.client.search(index=self.ans_index, body=query) + return [hit["_id"] for hit in response["hits"]["hits"]] + + def mark_deleted(self, keys): + actions = [ + { + "_op_type": "update", + "_index": self.ans_index, + "_id": key, + "doc": {"is_deleted": 1} + } + for key in keys + ] + responses = helpers.bulk(self.client, actions) + return responses[0] # 返回更新的文档数 + + def model_deleted(self, model_name): + query = { + "query": { + "term": {"model": model_name} + } + } + + response = self.client.delete_by_query(index=self.ans_index, body=query) + return response["deleted"] + + def clear_deleted_data(self): + query = { + "query": { + "term": {"is_deleted": 1} + } + } + response = self.client.delete_by_query(index=self.ans_index, body=query) + return response["deleted"] + + def count(self, state: int = 0, is_all: bool = False): + query = {"query": {"match_all": {}}} if is_all else {"query": {"term": {"is_deleted": state}}} + response = self.client.count(index=self.ans_index, body=query) + return response["count"] + + def close(self): + self.client.close() + + def count_answers(self): + query = {"query": {"match_all": {}}} + response = self.client.count(index=self.ans_index, body=query) + return response["count"] From aead2c1d67f62a2ec09d8c16446556abc6431563 Mon Sep 17 00:00:00 2001 From: powerli2002 <845160705@qq.com> Date: Sat, 14 Dec 2024 16:35:29 +0800 Subject: [PATCH 10/36] Add feature : add chromadb support as a vector database --- flask4modelcache.py | 6 ++ modelcache/config/chromadb_config.ini | 2 + modelcache/manager/vector_data/chroma.py | 93 +++++++++++++++++ modelcache/manager/vector_data/manager.py | 8 +- modelcache/utils/__init__.py | 4 + modelcache_mm/config/chromadb_config.ini | 2 + modelcache_mm/manager/vector_data/chroma.py | 100 +++++++++++++++++++ modelcache_mm/manager/vector_data/manager.py | 9 ++ modelcache_mm/utils/__init__.py | 4 + requirements.txt | 3 +- 10 files changed, 225 insertions(+), 6 deletions(-) create mode 100644 modelcache/config/chromadb_config.ini create mode 100644 modelcache/manager/vector_data/chroma.py create mode 100644 modelcache_mm/config/chromadb_config.ini create mode 100644 modelcache_mm/manager/vector_data/chroma.py diff --git a/flask4modelcache.py b/flask4modelcache.py index ee5955b..4dc85a9 100644 --- a/flask4modelcache.py +++ b/flask4modelcache.py @@ -41,10 +41,16 @@ def response_hitquery(cache_resp): # redis_config = configparser.ConfigParser() # redis_config.read('modelcache/config/redis_config.ini') +# chromadb_config = configparser.ConfigParser() +# chromadb_config.read('modelcache/config/chromadb_config.ini') data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), VectorBase("milvus", dimension=data2vec.dimension, milvus_config=milvus_config)) + +# data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), +# VectorBase("chromadb", dimension=data2vec.dimension, chromadb_config=chromadb_config)) + # data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), # VectorBase("redis", dimension=data2vec.dimension, redis_config=redis_config)) diff --git a/modelcache/config/chromadb_config.ini b/modelcache/config/chromadb_config.ini new file mode 100644 index 0000000..7400f80 --- /dev/null +++ b/modelcache/config/chromadb_config.ini @@ -0,0 +1,2 @@ +[chromadb] +persist_directory='' diff --git a/modelcache/manager/vector_data/chroma.py b/modelcache/manager/vector_data/chroma.py new file mode 100644 index 0000000..5bcb419 --- /dev/null +++ b/modelcache/manager/vector_data/chroma.py @@ -0,0 +1,93 @@ +from typing import List + +import numpy as np +import logging +from modelcache.manager.vector_data.base import VectorBase, VectorData +from modelcache.utils import import_chromadb, import_torch + +import_torch() +import_chromadb() + +import chromadb + + +class Chromadb(VectorBase): + + def __init__( + self, + persist_directory="./chromadb", + top_k: int = 1, + ): + self.collection_name = "modelcache" + self.top_k = top_k + + self._client = chromadb.PersistentClient(path=persist_directory) + self._collection = None + + def mul_add(self, datas: List[VectorData], model=None): + collection_name_model = self.collection_name + '_' + model + self._collection = self._client.get_or_create_collection(name=collection_name_model) + + data_array, id_array = map(list, zip(*((data.data.tolist(), str(data.id)) for data in datas))) + self._collection.add(embeddings=data_array, ids=id_array) + + def search(self, data: np.ndarray, top_k: int = -1, model=None): + collection_name_model = self.collection_name + '_' + model + self._collection = self._client.get_or_create_collection(name=collection_name_model) + + if self._collection.count() == 0: + return [] + if top_k == -1: + top_k = self.top_k + results = self._collection.query( + query_embeddings=[data.tolist()], + n_results=top_k, + include=["distances"], + ) + return list(zip(results["distances"][0], [int(x) for x in results["ids"][0]])) + + def rebuild(self, ids=None): + pass + + def delete(self, ids, model=None): + try: + collection_name_model = self.collection_name + '_' + model + self._collection = self._client.get_or_create_collection(name=collection_name_model) + # 查询集合中实际存在的 ID + ids_str = [str(x) for x in ids] + existing_ids = set(self._collection.get(ids=ids_str).ids) + + # 删除存在的 ID + if existing_ids: + self._collection.delete(list(existing_ids)) + + # 返回实际删除的条目数量 + return len(existing_ids) + + except Exception as e: + logging.error('Error during deletion: {}'.format(e)) + raise ValueError(str(e)) + + def rebuild_col(self, model): + collection_name_model = self.collection_name + '_' + model + + # 检查集合是否存在，如果存在则删除 + collections = self._client.list_collections() + if any(col.name == collection_name_model for col in collections): + self._client.delete_collection(collection_name_model) + else: + return 'model collection not found, please check!' + + try: + self._client.create_collection(collection_name_model) + except Exception as e: + logging.info(f'rebuild_collection: {e}') + raise ValueError(str(e)) + + def flush(self): + # chroma无flush方法 + pass + + def close(self): + # chroma无flush方法 + pass diff --git a/modelcache/manager/vector_data/manager.py b/modelcache/manager/vector_data/manager.py index 70448b2..0fc5334 100644 --- a/modelcache/manager/vector_data/manager.py +++ b/modelcache/manager/vector_data/manager.py @@ -102,13 +102,11 @@ def get(name, **kwargs): elif name == "chromadb": from modelcache.manager.vector_data.chroma import Chromadb - client_settings = kwargs.get("client_settings", None) - persist_directory = kwargs.get("persist_directory", None) - collection_name = kwargs.get("collection_name", COLLECTION_NAME) + chromadb_config = kwargs.get("chromadb_config", None) + persist_directory = chromadb_config.get('chromadb','persist_directory') + vector_base = Chromadb( - client_settings=client_settings, persist_directory=persist_directory, - collection_name=collection_name, top_k=top_k, ) elif name == "hnswlib": diff --git a/modelcache/utils/__init__.py b/modelcache/utils/__init__.py index 147a56e..15d90ec 100644 --- a/modelcache/utils/__init__.py +++ b/modelcache/utils/__init__.py @@ -73,3 +73,7 @@ def import_pillow(): def import_redis(): _check_library("redis") + + +def import_chromadb(): + _check_library("chromadb", package="chromadb") \ No newline at end of file diff --git a/modelcache_mm/config/chromadb_config.ini b/modelcache_mm/config/chromadb_config.ini new file mode 100644 index 0000000..2a87f3b --- /dev/null +++ b/modelcache_mm/config/chromadb_config.ini @@ -0,0 +1,2 @@ +[chromadb] +persist_directory=./chromadb diff --git a/modelcache_mm/manager/vector_data/chroma.py b/modelcache_mm/manager/vector_data/chroma.py new file mode 100644 index 0000000..fb97e32 --- /dev/null +++ b/modelcache_mm/manager/vector_data/chroma.py @@ -0,0 +1,100 @@ +from typing import List + +import numpy as np +import logging +from modelcache_mm.manager.vector_data.base import VectorBase, VectorData +from modelcache_mm.utils import import_chromadb, import_torch +from modelcache_mm.utils.index_util import get_mm_index_name + +import_torch() +import_chromadb() + +import chromadb + + +class Chromadb(VectorBase): + + def __init__( + self, + persist_directory="./chromadb", + top_k: int = 1, + ): + # self.collection_name = "modelcache" + self.top_k = top_k + + self._client = chromadb.PersistentClient(path=persist_directory) + self._collection = None + + def create(self, model=None, mm_type=None): + try: + collection_name_model = get_mm_index_name(model, mm_type) + # collection_name_model = self.collection_name + '_' + model + self._client.get_or_create_collection(name=collection_name_model) + except Exception as e: + raise ValueError(str(e)) + + def add(self, datas: List[VectorData], model=None, mm_type=None): + collection_name_model = get_mm_index_name(model, mm_type) + self._collection = self._client.get_or_create_collection(name=collection_name_model) + + data_array, id_array = map(list, zip(*((data.data.tolist(), str(data.id)) for data in datas))) + self._collection.add(embeddings=data_array, ids=id_array) + + def search(self, data: np.ndarray, top_k: int = -1, model=None, mm_type='mm'): + collection_name_model = get_mm_index_name(model, mm_type) + self._collection = self._client.get_or_create_collection(name=collection_name_model) + + if self._collection.count() == 0: + return [] + if top_k == -1: + top_k = self.top_k + results = self._collection.query( + query_embeddings=[data.tolist()], + n_results=top_k, + include=["distances"], + ) + return list(zip(results["distances"][0], [int(x) for x in results["ids"][0]])) + + def delete(self, ids, model=None, mm_type=None): + try: + collection_name_model = get_mm_index_name(model, mm_type) + self._collection = self._client.get_or_create_collection(name=collection_name_model) + # 查询集合中实际存在的 ID + ids_str = [str(x) for x in ids] + existing_ids = set(self._collection.get(ids=ids_str).ids) + + # 删除存在的 ID + if existing_ids: + self._collection.delete(list(existing_ids)) + + # 返回实际删除的条目数量 + return len(existing_ids) + + except Exception as e: + logging.error('Error during deletion: {}'.format(e)) + raise ValueError(str(e)) + + def rebuild_idx(self, model, mm_type=None): + collection_name_model = get_mm_index_name(model, mm_type) + + # 检查集合是否存在，如果存在则删除 + collections = self._client.list_collections() + if any(col.name == collection_name_model for col in collections): + self._client.delete_collection(collection_name_model) + else: + return 'model collection not found, please check!' + + try: + self._client.create_collection(collection_name_model) + except Exception as e: + logging.info(f'rebuild_collection: {e}') + raise ValueError(str(e)) + + def rebuild(self, ids=None): + pass + + def flush(self): + pass + + def close(self): + pass diff --git a/modelcache_mm/manager/vector_data/manager.py b/modelcache_mm/manager/vector_data/manager.py index c10decc..64cca45 100644 --- a/modelcache_mm/manager/vector_data/manager.py +++ b/modelcache_mm/manager/vector_data/manager.py @@ -108,6 +108,15 @@ def get(name, **kwargs): dimension=dimension, top_k=top_k ) + elif name == "chromadb": + from modelcache_mm.manager.vector_data.chroma import Chromadb + + chromadb_config = kwargs.get("chromadb_config", None) + persist_directory = chromadb_config.get('chromadb', 'persist_directory') + vector_base = Chromadb( + persist_directory=persist_directory, + top_k=top_k, + ) else: raise NotFoundError("vector store", name) return vector_base diff --git a/modelcache_mm/utils/__init__.py b/modelcache_mm/utils/__init__.py index 147a56e..15d90ec 100644 --- a/modelcache_mm/utils/__init__.py +++ b/modelcache_mm/utils/__init__.py @@ -73,3 +73,7 @@ def import_pillow(): def import_redis(): _check_library("redis") + + +def import_chromadb(): + _check_library("chromadb", package="chromadb") \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 95e1948..77b3868 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,4 +13,5 @@ faiss-cpu==1.7.4 redis==5.0.1 modelscope==1.14.0 fastapi==0.115.5 -uvicorn==0.32.0 \ No newline at end of file +uvicorn==0.32.0 +chromadb==0.5.23 From f3c7657858f18ec2e199a41208b2d6648fd311a2 Mon Sep 17 00:00:00 2001 From: powerli2002 <845160705@qq.com> Date: Sat, 14 Dec 2024 16:37:34 +0800 Subject: [PATCH 11/36] Add feature : add chromadb support as a vector database --- modelcache/manager/vector_data/chroma.py | 1 - modelcache_mm/manager/vector_data/chroma.py | 1 - 2 files changed, 2 deletions(-) diff --git a/modelcache/manager/vector_data/chroma.py b/modelcache/manager/vector_data/chroma.py index 5bcb419..446b354 100644 --- a/modelcache/manager/vector_data/chroma.py +++ b/modelcache/manager/vector_data/chroma.py @@ -89,5 +89,4 @@ def flush(self): pass def close(self): - # chroma无flush方法 pass diff --git a/modelcache_mm/manager/vector_data/chroma.py b/modelcache_mm/manager/vector_data/chroma.py index fb97e32..7f5b78c 100644 --- a/modelcache_mm/manager/vector_data/chroma.py +++ b/modelcache_mm/manager/vector_data/chroma.py @@ -19,7 +19,6 @@ def __init__( persist_directory="./chromadb", top_k: int = 1, ): - # self.collection_name = "modelcache" self.top_k = top_k self._client = chromadb.PersistentClient(path=persist_directory) From 2556eb6dda5d235603b0f6a3b16f47e68c6eb104 Mon Sep 17 00:00:00 2001 From: "Amber.Z" Date: Wed, 18 Dec 2024 17:38:46 +0800 Subject: [PATCH 12/36] add docs --- README.md | 186 +++++++++++++++++++----------- README_CN.md | 51 +++++++- docs/1.what-is-model-cache.md | 0 docs/2.model-cache-features.md | 0 docs/3.model-cache-quick-start.md | 0 docs/4.create-cache.md | 0 docs/5.configure-cache.md | 0 7 files changed, 169 insertions(+), 68 deletions(-) create mode 100644 docs/1.what-is-model-cache.md create mode 100644 docs/2.model-cache-features.md create mode 100644 docs/3.model-cache-quick-start.md create mode 100644 docs/4.create-cache.md create mode 100644 docs/5.configure-cache.md diff --git a/README.md b/README.md index 6ebc5f7..8e9489c 100644 --- a/README.md +++ b/README.md @@ -9,62 +9,102 @@ ModelCache

中文 | - English + English

## Contents -- [news](#news) -- [Introduction](#Introduction) -- [Quick-Deployment](#Quick-Deployment) -- [Service-Access](#Service-Access) -- [Articles](#Articles) -- [Modules](#Modules) -- [Core-Features](#Core-Features) -- [Acknowledgements](#Acknowledgements) -- [Contributing](#Contributing) -## news -- 🔥🔥[2024.04.09] Add Redis Search to store and retrieve embeddings in multi-tenant scene, this can reduce the interaction time between Cache and vector databases to 10ms. -- 🔥🔥[2023.12.10] we integrate LLM embedding frameworks such as 'llmEmb', 'ONNX', 'PaddleNLP', 'FastText', alone with the image embedding framework 'timm', to bolster embedding functionality. -- 🔥🔥[2023.11.20] codefuse-ModelCache has integrated local storage, such as sqlite and faiss, providing users with the convenience of quickly initiating tests. + +- [Contents](#contents) +- [News](#news) + - [Introduction](#introduction) +- [Architecture](#architecture) +- [Quick start](#quick-start) + - [Dependencies](#dependencies) + - [Start service](#start-service) + - [Start Demo](#start-demo) + - [Start normal service](#start-normal-service) +- [Access the service](#access-the-service) + - [Write cache](#write-cache) + - [Query cache](#query-cache) + - [Clear cache](#clear-cache) +- [Function comparison](#function-comparison) +- [Core-Features](#core-features) +- [Todo List](#todo-list) + - [Adapter](#adapter) + - [Embedding model\&inference](#embedding-modelinference) + - [Scalar Storage](#scalar-storage) + - [Vector Storage](#vector-storage) + - [Ranking](#ranking) + - [Service](#service) +- [Acknowledgements](#acknowledgements) +- [Contributing](#contributing) + +## News + +- 🔥🔥[2024.04.09] Added Redis Search to store and retrieve embeddings in multi-tenant. This can reduce the interaction time between Cache and vector databases to 10ms. +- 🔥🔥[2023.12.10] Integrated LLM embedding frameworks such as 'llmEmb', 'ONNX', 'PaddleNLP', 'FastText', and the image embedding framework 'timm' to bolster embedding functionality. +- 🔥🔥[2023.11.20] Integrated local storage, such as sqlite and faiss. This enables you to initiate quick and convenient tests. - [2023.08.26] codefuse-ModelCache... + ### Introduction + Codefuse-ModelCache is a semantic cache for large language models (LLMs). By caching pre-generated model results, it reduces response time for similar requests and improves user experience.
This project aims to optimize services by introducing a caching mechanism. It helps businesses and research institutions reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. Through open-source, we aim to share and exchange technologies related to large model semantic cache. -## Quick Deployment -The project's startup scripts are divided into flask4modelcache.py and flask4modelcache_demo.py. -- flask4modelcache_demo.py is a quick test service that embeds sqlite and faiss, and users do not need to be concerned about database-related matters. -- flask4modelcache.py is the normal service that requires configuration of mysql and milvus database services. + +## Architecture + +![modelcache modules](docs/modelcache_modules_20240409.png) + +## Quick start + +You can find the start script in `flask4modelcache.py` and `flask4modelcache_demo.py`. + +- `flask4modelcache_demo.py` is a quick test service that embeds sqlite and faiss. You do not need to be concerned about database-related matters. +- `flask4modelcache.py` is the normal service that requires configuration of MySQL and Milvus. + ### Dependencies -- Python version: 3.8 and above -- Package Installation -```shell -pip install -r requirements.txt -``` -### Service Startup -#### Demo Service Startup -1. Download the embedding model bin file from the following address: [https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Place the downloaded bin file in the model/text2vec-base-chinese folder. -2. Start the backend service using the flask4modelcache_dome.py script. -```shell -cd CodeFuse-ModelCache -``` -```shell -python flask4modelcache_demo.py -``` +- Python: V3.8 and above +- Package installation + + ```shell + pip install -r requirements.txt + ``` + +### Start service + +#### Start Demo + +1. Download the embedding model bin file on [Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Place the downloaded bin file in the model/text2vec-base-chinese folder. +2. Start the backend service by using `flask4modelcache_dome.py`. + + ```shell + cd CodeFuse-ModelCache + ``` + + ```shell + python flask4modelcache_demo.py + ``` + +#### Start normal service + +Before you start normal service, make sure that you have completed these steps: + +1. Install the relational database MySQL and import the SQL file to create the data tables. You can find the SQL file in `reference_doc/create_table.sql`. +2. Install vector database Milvus. +3. Add the database access information to the configuration files: + 1. `modelcache/config/milvus_config.ini` + 2. `modelcache/config/mysql_config.ini` +4. Download the embedding model bin file from [Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Put the bin file in the `model/text2vec-base-chinese` directory. +5. Start the backend service by using the `flask4modelcache.py` script. + +## Access the service -#### Normal Service Startup -Before starting the service, the following environment configurations should be performed: -1. Install the relational database MySQL and import the SQL file to create the data tables. The SQL file can be found at: ```reference_doc/create_table.sql``` -2. Install the vector database Milvus. -3. Add the database access information to the configuration files: - 1. ```modelcache/config/milvus_config.ini ``` - 2. ```modelcache/config/mysql_config.ini``` -4. Download the embedding model bin file from the following address: [https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Place the downloaded bin file in the model/text2vec-base-chinese folder. -5. Start the backend service using the flask4modelcache.py script. -## Service-Access The current service provides three core functionalities through RESTful API.: Cache-Writing, Cache-Querying, and Cache-Clearing. Demos: -### Cache-Writing + +### Write cache + ```python import json import requests @@ -77,7 +117,9 @@ data = {'type': type, 'scope': scope, 'chat_info': chat_info} headers = {"Content-Type": "application/json"} res = requests.post(url, headers=headers, json=json.dumps(data)) ``` -### Cache-Querying + +### Query cache + ```python import json import requests @@ -90,7 +132,9 @@ data = {'type': type, 'scope': scope, 'query': query} headers = {"Content-Type": "application/json"} res = requests.post(url, headers=headers, json=json.dumps(data)) ``` -### Cache-Clearing + +### Clear cache + ```python import json import requests @@ -103,12 +147,10 @@ data = {'type': type, 'scope': scope, 'remove_type': remove_type} headers = {"Content-Type": "application/json"} res = requests.post(url, headers=headers, json=json.dumps(data)) ``` -## Articles -https://mp.weixin.qq.com/s/ExIRu2o7yvXa6nNLZcCfhQ -## modules -![modelcache modules](docs/modelcache_modules_20240409.png) -## Function-Comparison -In terms of functionality, we have made several changes to the git repository. Firstly, we have addressed the network issues with huggingface and enhanced the inference speed by introducing local inference capabilities for embeddings. Additionally, considering the limitations of the SqlAlchemy framework, we have completely revamped the module responsible for interacting with relational databases, enabling more flexible database operations. In practical scenarios, LLM products often require integration with multiple users and multiple models. Hence, we have added support for multi-tenancy in the ModelCache, while also making preliminary compatibility adjustments for system commands and multi-turn dialogue. + +## Function comparison + +We've implemented several key updates to our repository. We've resolved network issues with Hugging Face and improved inference speed by introducing local embedding capabilities. Due to limitations in SqlAlchemy, we've redesigned our relational database interaction module for more flexible operations. We've added multi-tenancy support to ModelCache, recognizing the need for multiple users and models in LLM products. Lastly, we've made initial adjustments for better compatibility with system commands and multi-turn dialogues. @@ -231,45 +273,59 @@ In terms of functionality, we have made several changes to the git repository. F
- ## Core-Features + In ModelCache, we adopted the main idea of GPTCache, includes core modules: adapter, embedding, similarity, and data_manager. The adapter module is responsible for handling the business logic of various tasks and can connect the embedding, similarity, and data_manager modules. The embedding module is mainly responsible for converting text into semantic vector representations, it transforms user queries into vector form.The rank module is used for sorting and evaluating the similarity of the recalled vectors. The data_manager module is primarily used for managing the database. In order to better facilitate industrial applications, we have made architectural and functional upgrades as follows: - [x] We have modified it similar to Redis and embedded it into the LLMs product, providing semantic caching capabilities. This ensures that it does not interfere with LLM calls, security audits, and other functionalities, achieving compatibility with all large-scale model services. -- [x] Multiple Model Loading Schemes: - - Support loading local embedding models to address Hugging Face network connectivity issues. - - Support loading various pretrained model embedding layers. -- [x] Data Isolation Capability - - Environment Isolation: Can pull different database configurations based on the environment to achieve environment isolation (dev, prepub, prod). - - Multi-tenant Data Isolation: Dynamically create collections based on the model for data isolation, addressing data isolation issues in multi-model/services scenarios in LLMs products. +- [x] Multiple Model Loading Schemes: + - Support loading local embedding models to address Hugging Face network connectivity issues. + - Support loading various pretrained model embedding layers. +- [x] Data Isolation Capability + - Environment Isolation: Can pull different database configurations based on the environment to achieve environment isolation (dev, prepub, prod). + - Multi-tenant Data Isolation: Dynamically create collections based on the model for data isolation, addressing data isolation issues in multi-model/services scenarios in LLMs products. - [x] Support for System Commands: Adopting a concatenation approach to address the issue of system commands in the prompt format. - [x] Differentiation of Long and Short Texts: Long texts pose more challenges for similarity evaluation. To address this, we have added differentiation between long and short texts, allowing for separate configuration of threshold values for determining similarity. - [x] Milvus Performance Optimization: The consistency_level of Milvus has been adjusted to "Session" level, which can result in better performance. -- [x] Data Management Capability: - - Ability to clear the cache, used for data management after model upgrades. - - Hitquery recall for subsequent data analysis and model iteration reference. - - Asynchronous log write-back capability for data analysis and statistics. - - Added model field and data statistics field for feature expansion. +- [x] Data Management Capability: + - Ability to clear the cache, used for data management after model upgrades. + - Hitquery recall for subsequent data analysis and model iteration reference. + - Asynchronous log write-back capability for data analysis and statistics. + - Added model field and data statistics field for feature expansion. ## Todo List + ### Adapter + - [ ] Register adapter for Milvus：Based on the "model" parameter in the scope, initialize the corresponding Collection and perform the load operation. + ### Embedding model&inference + - [ ] Inference Optimization: Optimizing the speed of embedding inference, compatible with inference engines such as FasterTransformer, TurboTransformers, and ByteTransformer. - [ ] Compatibility with Hugging Face models and ModelScope models, offering more methods for model loading. + ### Scalar Storage + - [ ] Support MongoDB - [ ] Support ElasticSearch + ### Vector Storage + - [ ] Adapts Faiss storage in multimodal scenarios. + ### Ranking + - [ ] Add ranking model to refine the order of data after embedding recall. + ### Service + - [ ] Supports FastAPI. - [ ] Add visual interface to offer a more direct user experience. ## Acknowledgements + This project has referenced the following open-source projects. We would like to express our gratitude to the projects and their developers for their contributions and research.
[GPTCache](https://github.com/zilliztech/GPTCache) ## Contributing -ModelCache is a captivating and invaluable project, whether you are an experienced developer or a novice just starting out, your contributions to this project are warmly welcomed. Your involvement in this project, be it through raising issues, providing suggestions, writing code, or documenting and creating examples, will enhance the project's quality and make a significant contribution to the open-source community. \ No newline at end of file + +ModelCache is a captivating and invaluable project, whether you are an experienced developer or a novice just starting out, your contributions to this project are warmly welcomed. Your involvement in this project, be it through raising issues, providing suggestions, writing code, or documenting and creating examples, will enhance the project's quality and make a significant contribution to the open-source community. diff --git a/README_CN.md b/README_CN.md index 5959380..89c3fad 100644 --- a/README_CN.md +++ b/README_CN.md @@ -15,6 +15,7 @@ ModelCache ## Contents + - [新闻](#新闻) - [项目简介](#项目简介) - [快速部署](#快速部署) @@ -24,29 +25,42 @@ ModelCache - [核心功能](#核心功能) - [致谢](#致谢) - [Contributing](#Contributing) + ## 新闻 + - 🔥🔥[2024.10.22] 增加1024程序员节任务。 - 🔥🔥[2024.04.09] 增加了多租户场景中Redis Search存储和检索embedding的能力，可以将Cache和向量数据库的交互耗时降低至10ms内。 - 🔥🔥[2023.12.10] 增加llmEmb、onnx、paddlenlp、fasttext等LLM embedding框架，并增加timm 图片embedding框架，用于提供更丰富的embedding能力。 - 🔥🔥[2023.11.20] codefuse-ModelCache增加本地存储能力, 适配了嵌入式数据库sqlite、faiss，方便用户快速启动测试。 - [2023.10.31] codefuse-ModelCache... + ## 项目简介 + Codefuse-ModelCache 是一个开源的大模型语义缓存系统，通过缓存已生成的模型结果，降低类似请求的响应时间，提升用户体验。该项目从服务优化角度出发，引入缓存机制，在资源有限和对实时性要求较高的场景下，帮助企业和研究机构降低推理部署成本、提升模型性能和效率、提供规模化大模型服务。我们希望通过开源，分享交流大模型语义Cache的相关技术。 + ## 快速部署 + 项目中启动服务脚本分为flask4modelcache.py 和 flask4modelcache_demo.py，其中： - flask4modelcache_demo.py 为快速测试服务，内嵌了sqlite和faiss，用户无需关心数据库相关事宜。 - flask4modelcache.py 为正常服务，需用户具备mysql和milvus等数据库服务。 + ### 环境依赖 + - python版本: 3.8及以上 - 依赖包安装： -```shell -pip install -r requirements.txt -``` + + ```shell + pip install -r requirements.txt + ``` + ### 服务启动 + #### Demo服务启动 + - 离线模型bin文件下载， 参考地址：[https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main)，并将下载的bin文件，放到 model/text2vec-base-chinese 文件夹中。 - 执行flask4modelcache_demo.py启动服务。 + ```shell cd CodeFuse-ModelCache ``` @@ -55,7 +69,9 @@ python flask4modelcache_demo.py ``` #### 正常服务启动 + 在启动服务前，应该进行如下环境配置： + 1. 安装关系数据库 mysql， 导入sql创建数据表，sql文件:```reference_doc/create_table.sql``` 2. 安装向量数据库milvus 3. 在配置文件中添加数据库访问信息，配置文件为： @@ -63,9 +79,13 @@ python flask4modelcache_demo.py 2. ```modelcache/config/mysql_config.ini``` 4. 离线模型bin文件下载， 参考地址：[https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main)，并将下载的bin文件，放到 model/text2vec-base-chinese 文件夹中 5. 通过flask4modelcache.py脚本启动后端服务。 + ## 服务访问 + 当前服务以restful API方式提供3个核心功能：数据写入，cache查询和cache数据清空。请求demo 如下： + ### cache写入 + ```python import json import requests @@ -78,7 +98,9 @@ data = {'type': type, 'scope': scope, 'chat_info': chat_info} headers = {"Content-Type": "application/json"} res = requests.post(url, headers=headers, json=json.dumps(data)) ``` + ### cache查询 + ```python import json import requests @@ -91,7 +113,9 @@ data = {'type': type, 'scope': scope, 'query': query} headers = {"Content-Type": "application/json"} res = requests.post(url, headers=headers, json=json.dumps(data)) ``` + ### cache清空 + ```python import json import requests @@ -104,11 +128,17 @@ data = {'type': type, 'scope': scope, 'remove_type': remove_type} headers = {"Content-Type": "application/json"} res = requests.post(url, headers=headers, json=json.dumps(data)) ``` + ## 文章 + https://mp.weixin.qq.com/s/ExIRu2o7yvXa6nNLZcCfhQ + ## 架构大图 + ![modelcache modules](docs/modelcache_modules_20240409.png) + ## 功能对比 + 功能方面，为了解决huggingface网络问题并提升推理速度，增加了embedding本地推理能力。鉴于SqlAlchemy框架存在一些限制，我们对关系数据库交互模块进行了重写，以更灵活地实现数据库操作。在实践中，大型模型产品需要与多个用户和多个模型对接，因此在ModelCache中增加了对多租户的支持，同时也初步兼容了系统指令和多轮会话。 @@ -233,6 +263,7 @@ https://mp.weixin.qq.com/s/ExIRu2o7yvXa6nNLZcCfhQ
## 核心功能 + 在ModelCache中，沿用了GPTCache的主要思想，包含了一系列核心模块：adapter、embedding、similarity和data_manager。adapter模块主要功能是处理各种任务的业务逻辑，并且能够将embedding、similarity、data_manager等模块串联起来；embedding模块主要负责将文本转换为语义向量表示，它将用户的查询转换为向量形式，并用于后续的召回或存储操作；rank模块用于对召回的向量进行相似度排序和评估；data_manager模块主要用于管理数据库。同时，为了更好的在工业界落地，我们做了架构和功能上的升级，如下： - [x] 架构调整（轻量化集成）：以类redis的缓存模式嵌入到大模型产品中，提供语义缓存能力，不会干扰LLM调用和安全审核等功能，适配所有大模型服务。 @@ -252,24 +283,38 @@ https://mp.weixin.qq.com/s/ExIRu2o7yvXa6nNLZcCfhQ - 增加model字段和数据统计字段，用于功能拓展。 ## Todo List + ### Adapter + - [ ] register adapter for Milvus：根据scope中的model参数，初始化对应Collection 并且执行load操作。 + ### Embedding model&inference + - [ ] inference优化：优化embedding推理速度，适配fastertransformer, TurboTransformers, ByteTransformer等推理引擎。 - [ ] 兼容huggingface模型和modelscope模型，提供更多模型加载方式。 + ### Scalar Storage + - [ ] Support MongoDB。 - [ ] Support ElasticSearch。 + ### Vector Storage + - [ ] 在多模态场景中适配faiss存储。 + ### Ranking + - [ ] 增加Rank模型，对embedding召回后的数据，进行精排。 + ### Service + - [ ] 支持fastapi。 - [ ] 增加前端界面，用于测试。 ## 致谢 + 本项目参考了以下开源项目，在此对相关项目和研究开发人员表示感谢。
[GPTCache](https://github.com/zilliztech/GPTCache) ## Contributing + ModelCache是一个非常有趣且有用的项目，我们相信这个项目有很大的潜力，无论你是经验丰富的开发者，还是刚刚入门的新手，都欢迎你为这个项目做出一些贡献，包括但不限于：提交问题和建议，参与代码编写，完善文档和示例。你的参与将会使这个项目变得更好，同时也会为开源社区做出贡献。 \ No newline at end of file diff --git a/docs/1.what-is-model-cache.md b/docs/1.what-is-model-cache.md new file mode 100644 index 0000000..e69de29 diff --git a/docs/2.model-cache-features.md b/docs/2.model-cache-features.md new file mode 100644 index 0000000..e69de29 diff --git a/docs/3.model-cache-quick-start.md b/docs/3.model-cache-quick-start.md new file mode 100644 index 0000000..e69de29 diff --git a/docs/4.create-cache.md b/docs/4.create-cache.md new file mode 100644 index 0000000..e69de29 diff --git a/docs/5.configure-cache.md b/docs/5.configure-cache.md new file mode 100644 index 0000000..e69de29 From 2a0a00fa5063309e67f8ca3987d2674ac838ab45 Mon Sep 17 00:00:00 2001 From: "Amber.Z" Date: Wed, 18 Dec 2024 19:53:58 +0800 Subject: [PATCH 13/36] add more content --- README.md | 88 +++++++++++--------- docs/1.what-is-model-cache.md | 132 ++++++++++++++++++++++++++++++ docs/2.model-cache-features.md | 29 +++++++ docs/3.model-cache-quick-start.md | 97 ++++++++++++++++++++++ docs/4.create-cache.md | 42 ++++++++++ docs/5.configure-cache.md | 0 6 files changed, 351 insertions(+), 37 deletions(-) delete mode 100644 docs/5.configure-cache.md diff --git a/README.md b/README.md index 8e9489c..6520171 100644 --- a/README.md +++ b/README.md @@ -23,14 +23,14 @@ ModelCache - [Quick start](#quick-start) - [Dependencies](#dependencies) - [Start service](#start-service) - - [Start Demo](#start-demo) + - [Start demo](#start-demo) - [Start normal service](#start-normal-service) -- [Access the service](#access-the-service) +- [Visit the service](#visit-the-service) - [Write cache](#write-cache) - [Query cache](#query-cache) - [Clear cache](#clear-cache) - [Function comparison](#function-comparison) -- [Core-Features](#core-features) +- [Features](#features) - [Todo List](#todo-list) - [Adapter](#adapter) - [Embedding model\&inference](#embedding-modelinference) @@ -60,12 +60,12 @@ Codefuse-ModelCache is a semantic cache for large language models (LLMs). By cac You can find the start script in `flask4modelcache.py` and `flask4modelcache_demo.py`. -- `flask4modelcache_demo.py` is a quick test service that embeds sqlite and faiss. You do not need to be concerned about database-related matters. -- `flask4modelcache.py` is the normal service that requires configuration of MySQL and Milvus. +- `flask4modelcache_demo.py`: A quick test service that embeds SQLite and FAISS. No database configuration required. +- `flask4modelcache.py`: The standard service that requires MySQL and Milvus configuration. ### Dependencies -- Python: V3.8 and above +- Python: V3.8 or above - Package installation ```shell @@ -74,10 +74,10 @@ You can find the start script in `flask4modelcache.py` and `flask4modelcache_dem ### Start service -#### Start Demo +#### Start demo -1. Download the embedding model bin file on [Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Place the downloaded bin file in the model/text2vec-base-chinese folder. -2. Start the backend service by using `flask4modelcache_dome.py`. +1. Download the embedding model bin file from [Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Place it in the `model/text2vec-base-chinese` folder. +2. Start the backend service: ```shell cd CodeFuse-ModelCache @@ -89,19 +89,23 @@ You can find the start script in `flask4modelcache.py` and `flask4modelcache_dem #### Start normal service -Before you start normal service, make sure that you have completed these steps: +Before you start standard service, do these steps: -1. Install the relational database MySQL and import the SQL file to create the data tables. You can find the SQL file in `reference_doc/create_table.sql`. +1. Install MySQL and import the SQL file from `reference_doc/create_table.sql`. 2. Install vector database Milvus. -3. Add the database access information to the configuration files: - 1. `modelcache/config/milvus_config.ini` - 2. `modelcache/config/mysql_config.ini` -4. Download the embedding model bin file from [Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Put the bin file in the `model/text2vec-base-chinese` directory. -5. Start the backend service by using the `flask4modelcache.py` script. +3. Configure database access in: + - `modelcache/config/milvus_config.ini` + - `modelcache/config/mysql_config.ini` +4. Download the embedding model bin file from [Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Put it in `model/text2vec-base-chinese`. +5. Start the backend service: -## Access the service + ```bash + python flask4modelcache.py + ``` -The current service provides three core functionalities through RESTful API.: Cache-Writing, Cache-Querying, and Cache-Clearing. Demos: +## Visit the service + +The service provides three core RESTful API functionalities: Cache-Writing, Cache-Querying, and Cache-Clearing. ### Write cache @@ -273,25 +277,35 @@ We've implemented several key updates to our repository. We've resolved network -## Core-Features - -In ModelCache, we adopted the main idea of GPTCache, includes core modules: adapter, embedding, similarity, and data_manager. The adapter module is responsible for handling the business logic of various tasks and can connect the embedding, similarity, and data_manager modules. The embedding module is mainly responsible for converting text into semantic vector representations, it transforms user queries into vector form.The rank module is used for sorting and evaluating the similarity of the recalled vectors. The data_manager module is primarily used for managing the database. In order to better facilitate industrial applications, we have made architectural and functional upgrades as follows: - -- [x] We have modified it similar to Redis and embedded it into the LLMs product, providing semantic caching capabilities. This ensures that it does not interfere with LLM calls, security audits, and other functionalities, achieving compatibility with all large-scale model services. -- [x] Multiple Model Loading Schemes: - - Support loading local embedding models to address Hugging Face network connectivity issues. - - Support loading various pretrained model embedding layers. -- [x] Data Isolation Capability - - Environment Isolation: Can pull different database configurations based on the environment to achieve environment isolation (dev, prepub, prod). - - Multi-tenant Data Isolation: Dynamically create collections based on the model for data isolation, addressing data isolation issues in multi-model/services scenarios in LLMs products. -- [x] Support for System Commands: Adopting a concatenation approach to address the issue of system commands in the prompt format. -- [x] Differentiation of Long and Short Texts: Long texts pose more challenges for similarity evaluation. To address this, we have added differentiation between long and short texts, allowing for separate configuration of threshold values for determining similarity. -- [x] Milvus Performance Optimization: The consistency_level of Milvus has been adjusted to "Session" level, which can result in better performance. -- [x] Data Management Capability: - - Ability to clear the cache, used for data management after model upgrades. - - Hitquery recall for subsequent data analysis and model iteration reference. - - Asynchronous log write-back capability for data analysis and statistics. - - Added model field and data statistics field for feature expansion. +## Features + +In ModelCache, we incorporated the core principles of GPTCache. ModelCache has four modules: adapter, embedding, similarity, and data_manager. + +- The adapter module orchestrates the business logic for various tasks, integrate the embedding, similarity, and data_manager modules. +- The embedding module converts text into semantic vector representations, and transforms user queries into vectors. +- The rank module ranks and evaluate the similarity of recalled vectors. +- The data_manager module manages the databases. + +To make ModelCache more suitable for industrial use, we made several improvements to its architecture and functionality: + +- [x] Architectural adjustment (lightweight integration): + - Embedded into LLM products using a Redis-like caching mode + - Provided semantic caching without interfering with LLM calls, security audits, and other functions + - Compatible with all LLM services +- [x] Multiple model loading: + - Supported local embedding model loading, and resolved Hugging Face network connectivity issues + - Supported loading embedding layers from various pre-trained models +- [x] Data isolation + - Environment isolation: Read different database configurations based on the environment. Isolate development, staging, and production environments. + - Multi-tenant data isolation: Dynamically create collections based on models for data isolation, addressing data separation issues in multi-model/service scenarios within large language model products +- [x] Supported system instruction: Adopted a concatenation approach to resolve issues with system instructions in the prompt paradigm. +- [x] Long and short text differentiation: Long texts bring more challenges for similarity assessment. Added differentiation between long and short texts, allowing for separate threshold configurations. +- [x] Milvus performance optimization: Adjusted Milvus consistency level to "Session" level for better performance. +- [x] Data management: + - One-click cache clearing to enable easy data management after model upgrades. + - Recall of hit queries for subsequent data analysis and model iteration reference. + - Asynchronous log write-back for data analysis and statistics + - Added model field and data statistics field to enhance features ## Todo List diff --git a/docs/1.what-is-model-cache.md b/docs/1.what-is-model-cache.md index e69de29..8fdbffa 100644 --- a/docs/1.what-is-model-cache.md +++ b/docs/1.what-is-model-cache.md @@ -0,0 +1,132 @@ +# What is ModelCache + +In ModelCache, we adopted the main idea of GPTCache, includes core modules: adapter, embedding, similarity, and data_manager. The adapter module is responsible for handling the business logic of various tasks and can connect the embedding, similarity, and data_manager modules. The embedding module is mainly responsible for converting text into semantic vector representations, it transforms user queries into vector form.The rank module is used for sorting and evaluating the similarity of the recalled vectors. The data_manager module is primarily used for managing the database. In order to better facilitate industrial applications, we have made architectural and functional upgrades as follows: + +## Architecture + +![modelcache modules](modelcache_modules_20240409.png) + +## Function comparison + +We've implemented several key updates to our repository. We've resolved network issues with Hugging Face and improved inference speed by introducing local embedding capabilities. Due to limitations in SqlAlchemy, we've redesigned our relational database interaction module for more flexible operations. We've added multi-tenancy support to ModelCache, recognizing the need for multiple users and models in LLM products. Lastly, we've made initial adjustments for better compatibility with system commands and multi-turn dialogues. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModuleFunction
ModelCacheGPTCache
Basic InterfaceData query interface☑ ☑
Data writing interface☑ ☑
EmbeddingEmbedding model configuration☑ ☑
Large model embedding layer☑
BERT model long text processing☑
Large model invocationDecoupling from large models☑
Local loading of embedding model☑
Data isolationModel data isolation☑ ☑
Hyperparameter isolation
DatabasesMySQL☑ ☑
Milvus☑ ☑
OceanBase☑
Session managementSingle-turn dialogue☑ ☑
System commands☑
Multi-turn dialogue☑
Data managementData persistence☑ ☑
One-click cache clearance☑
Tenant managementSupport for multi-tenancy☑
Milvus multi-collection capability☑
OtherLong-short dialogue distinction☑
diff --git a/docs/2.model-cache-features.md b/docs/2.model-cache-features.md index e69de29..2294d03 100644 --- a/docs/2.model-cache-features.md +++ b/docs/2.model-cache-features.md @@ -0,0 +1,29 @@ +# ModelCache features + +This topic describes ModelCache features. In ModelCache, we incorporated the core principles of GPTCache. ModelCache has four modules: adapter, embedding, similarity, and data_manager. + +- The adapter module orchestrates the business logic for various tasks, integrate the embedding, similarity, and data_manager modules. +- The embedding module converts text into semantic vector representations, and transforms user queries into vectors. +- The rank module ranks and evaluate the similarity of recalled vectors. +- The data_manager module manages the databases. + +To make ModelCache more suitable for industrial use, we made several improvements to its architecture and functionality: + +- [x] Architectural adjustment (lightweight integration): + - Embedded into LLM products using a Redis-like caching mode. + - Provided semantic caching without interfering with LLM calls, security audits, and other functions. + - Compatible with all LLM services. +- [x] Multiple model loading: + - Supported local embedding model loading, and resolved Hugging Face network connectivity issues. + - Supported loading embedding layers from various pre-trained models. +- [x] Data isolation + - Environment isolation: Read different database configurations based on the environment. Isolate development, staging, and production environments. + - Multi-tenant data isolation: Dynamically create collections based on models for data isolation, addressing data separation issues in multi-model/service scenarios within large language model products. +- [x] Supported system instruction: Adopted a concatenation approach to resolve issues with system instructions in the prompt paradigm. +- [x] Long and short text differentiation: Long texts bring more challenges for similarity assessment. Added differentiation between long and short texts, allowing for separate threshold configurations. +- [x] Milvus performance optimization: Adjusted Milvus consistency level to "Session" level for better performance. +- [x] Data management: + - One-click cache clearing to enable easy data management after model upgrades. + - Recall of hit queries for subsequent data analysis and model iteration reference. + - Asynchronous log write-back for data analysis and statistics. + - Added model field and data statistics field to enhance features. diff --git a/docs/3.model-cache-quick-start.md b/docs/3.model-cache-quick-start.md index e69de29..7cd3903 100644 --- a/docs/3.model-cache-quick-start.md +++ b/docs/3.model-cache-quick-start.md @@ -0,0 +1,97 @@ +# Quick start + +This topic describes how to set up and use ModelCache. + +You can find the start script in `flask4modelcache.py` and `flask4modelcache_demo.py`. + +- `flask4modelcache_demo.py`: A quick test service that embeds SQLite and FAISS. No database configuration required. +- `flask4modelcache.py`: The standard service that requires MySQL and Milvus configuration. + +## Dependencies + +- Python: V3.8 or above +- Package installation + + ```shell + pip install -r requirements.txt + ``` + +## Start service + +### Start demo + +1. Download the embedding model bin file from [Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Place it in the `model/text2vec-base-chinese` folder. +2. Start the backend service: + + ```shell + cd CodeFuse-ModelCache + ``` + + ```shell + python flask4modelcache_demo.py + ``` + +### Start standard service + +Before you start standard service, do these steps: + +1. Install MySQL and import the SQL file from `reference_doc/create_table.sql`. +2. Install vector database Milvus. +3. Configure database access in: + - `modelcache/config/milvus_config.ini` + - `modelcache/config/mysql_config.ini` +4. Download the embedding model bin file from [Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Put it in `model/text2vec-base-chinese`. +5. Start the backend service: + + ```bash + python flask4modelcache.py + ``` + +## Visit the service + +The service provides three core RESTful API functionalities: Cache-Writing, Cache-Querying, and Cache-Clearing. + +### Write cache + +```python +import json +import requests +url = 'http://127.0.0.1:5000/modelcache' +type = 'insert' +scope = {"model": "CODEGPT-1008"} +chat_info = [{"query": [{"role": "system", "content": "You are an AI code assistant and you must provide neutral and harmless answers to help users solve code-related problems."}, {"role": "user", "content": "你是谁?"}], + "answer": "Hello, I am an intelligent assistant. How can I assist you?"}] +data = {'type': type, 'scope': scope, 'chat_info': chat_info} +headers = {"Content-Type": "application/json"} +res = requests.post(url, headers=headers, json=json.dumps(data)) +``` + +### Query cache + +```python +import json +import requests +url = 'http://127.0.0.1:5000/modelcache' +type = 'query' +scope = {"model": "CODEGPT-1008"} +query = [{"role": "system", "content": "You are an AI code assistant and you must provide neutral and harmless answers to help users solve code-related problems."}, {"role": "user", "content": "Who are you?"}] +data = {'type': type, 'scope': scope, 'query': query} + +headers = {"Content-Type": "application/json"} +res = requests.post(url, headers=headers, json=json.dumps(data)) +``` + +### Clear cache + +```python +import json +import requests +url = 'http://127.0.0.1:5000/modelcache' +type = 'remove' +scope = {"model": "CODEGPT-1008"} +remove_type = 'truncate_by_model' +data = {'type': type, 'scope': scope, 'remove_type': remove_type} + +headers = {"Content-Type": "application/json"} +res = requests.post(url, headers=headers, json=json.dumps(data)) +``` diff --git a/docs/4.create-cache.md b/docs/4.create-cache.md index e69de29..e0506ba 100644 --- a/docs/4.create-cache.md +++ b/docs/4.create-cache.md @@ -0,0 +1,42 @@ +# Create cache + +This topic describes how to create cache. + +## Default cache interface + +```py +class Cache: + # ModelCache calls it whe you start the cache system + def __init__(self): + self.has_init = False + self.cache_enable_func = None + self.embedding_func = None + self.post_process_messages_func = None + self.config = Config() +``` + +This function embeds text into dense vectors for context similarity search. ModelCache supports these embedding context methods: Huggingface, ONNX, and SentenceTransformers. The default model is text2vec Hugging Face because it performs better for Chinese. Simply initialize your embedding function as `text2vec.to_embeddings`. + +```py +data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), + VectorBase("milvus", dimension=data2vec.dimension, milvus_config=milvus_config)) + +cache.init( + embedding_func=data2vec.to_embeddings, + data_manager=data_manager, + similarity_evaluation=SearchDistanceEvaluation(), + query_pre_embedding_func=query_multi_splicing, + insert_pre_embedding_func=insert_multi_splicing, +) +``` + +data_manager CacheVase stores all scalar data, such as original questions, prompts, answers, and access times. ModelCache supports multiple cache storages like SQLite, MySQL, and OceanBase. NoSQL databases will be supported in the future. + +data_manager VectorBase stores and searches all embedding vectors to find semantically similar results. ModelCache supports using vector search libraries like FAISS or vector databases like Milvus. More vector database and cloud service will be supported in the future. + +## Examples + +```py +data_manager = get_data_manager(CacheBase("sqlite"), VectorBase("faiss", dimension=data2vec.dimension)) +data_manager = get_data_manager(CacheBase("oceanbase"), VectorBase("milvus", dimension=data2vec.dimension)) +``` diff --git a/docs/5.configure-cache.md b/docs/5.configure-cache.md deleted file mode 100644 index e69de29..0000000 From ac759ab2450e0add41fdcd7113708809e2792f32 Mon Sep 17 00:00:00 2001 From: charleschile Date: Wed, 18 Dec 2024 22:39:45 +0800 Subject: [PATCH 14/36] bge-m3 embedding generator --- modelcache/embedding/__init__.py | 6 +++++- modelcache/embedding/bge_m3.py | 28 ++++++++++++++++++++++++++++ 2 files changed, 33 insertions(+), 1 deletion(-) create mode 100644 modelcache/embedding/bge_m3.py diff --git a/modelcache/embedding/__init__.py b/modelcache/embedding/__init__.py index 5684a2d..a7ab511 100644 --- a/modelcache/embedding/__init__.py +++ b/modelcache/embedding/__init__.py @@ -7,6 +7,7 @@ paddlenlp = LazyImport("paddlenlp", globals(), "modelcache.embedding.paddlenlp") timm = LazyImport("timm", globals(), "modelcache.embedding.timm") huggingface_tei = LazyImport("huggingface_tei", globals(), "modelcache.embedding.huggingface_tei") +bge_m3 = LazyImport("bge_m3", globals(), "modelcache.embedding.bge_m3") def Huggingface(model="sentence-transformers/all-mpnet-base-v2"): @@ -33,4 +34,7 @@ def Timm(model="resnet50", device="default"): return timm.Timm(model, device) def HuggingfaceTEI(base_url, model): - return huggingface_tei.HuggingfaceTEI(base_url, model) \ No newline at end of file + return huggingface_tei.HuggingfaceTEI(base_url, model) + +def BgeM3Embedding(model_path="model/bge-m3"): + return bge_m3.BgeM3Embedding(model_path) \ No newline at end of file diff --git a/modelcache/embedding/bge_m3.py b/modelcache/embedding/bge_m3.py new file mode 100644 index 0000000..10cc15c --- /dev/null +++ b/modelcache/embedding/bge_m3.py @@ -0,0 +1,28 @@ +# -*- coding: utf-8 -*- +import numpy as np +from modelcache.embedding.base import BaseEmbedding +from transformers import AutoTokenizer, AutoModel +from FlagEmbedding import BGEM3FlagModel + +class BgeM3Embedding(BaseEmbedding): + def __init__(self, model_path: str = "model/bge-m3"): + self.tokenizer = AutoTokenizer.from_pretrained(model_path) + self.model = AutoModel.from_pretrained(model_path) + + self.bge_model = BGEM3FlagModel(model_name_or_path=model_path, + model=self.model, + tokenizer=self.tokenizer, + use_fp16=False) + + self.__dimension = 768 + + def to_embeddings(self, data, **_): + if not isinstance(data, list): + data = [data] + + embeddings = self.bge_model.encode(data, batch_size=12, max_length=8192)['dense_vecs'] + return np.array(embeddings).astype("float32") + + @property + def dimension(self): + return self.__dimension \ No newline at end of file From 992f3b9aa67aa9000992f26b4390ab7c5b58bca2 Mon Sep 17 00:00:00 2001 From: charleschile Date: Wed, 18 Dec 2024 23:11:23 +0800 Subject: [PATCH 15/36] reranker --- modelcache/adapter/adapter_query.py | 141 +++++++++++++++++++--------- 1 file changed, 96 insertions(+), 45 deletions(-) diff --git a/modelcache/adapter/adapter_query.py b/modelcache/adapter/adapter_query.py index a8c90f1..e3be30a 100644 --- a/modelcache/adapter/adapter_query.py +++ b/modelcache/adapter/adapter_query.py @@ -5,7 +5,9 @@ from modelcache.utils.error import NotInitError from modelcache.utils.time import time_cal from modelcache.processor.pre import multi_analysis +from FlagEmbedding import FlagReranker +USE_RERANKER = True # 如果为 True 则启用 reranker，否则使用原有逻辑 def adapt_query(cache_data_convert, *args, **kwargs): chat_cache = kwargs.pop("cache_obj", cache) @@ -74,53 +76,102 @@ def adapt_query(cache_data_convert, *args, **kwargs): if rank_pre < rank_threshold: return - for cache_data in cache_data_list: - primary_id = cache_data[1] - ret = chat_cache.data_manager.get_scalar_data( - cache_data, extra_param=context.get("get_scalar_data", None) - ) - if ret is None: - continue + if USE_RERANKER: + reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=False) + for cache_data in cache_data_list: + primary_id = cache_data[1] + ret = chat_cache.data_manager.get_scalar_data( + cache_data, extra_param=context.get("get_scalar_data", None) + ) + if ret is None: + continue - if "deps" in context and hasattr(ret.question, "deps"): - eval_query_data = { - "question": context["deps"][0]["data"], - "embedding": None - } - eval_cache_data = { - "question": ret.question.deps[0].data, - "answer": ret.answers[0].answer, - "search_result": cache_data, - "embedding": None, - } - else: - eval_query_data = { - "question": pre_embedding_data, - "embedding": embedding_data, - } + rank = reranker.compute_score([pre_embedding_data, ret[0]], normalize=True) - eval_cache_data = { - "question": ret[0], - "answer": ret[1], - "search_result": cache_data, - "embedding": None - } - rank = chat_cache.similarity_evaluation.evaluation( - eval_query_data, - eval_cache_data, - extra_param=context.get("evaluation_func", None), - ) + if "deps" in context and hasattr(ret.question, "deps"): + eval_query_data = { + "question": context["deps"][0]["data"], + "embedding": None + } + eval_cache_data = { + "question": ret.question.deps[0].data, + "answer": ret.answers[0].answer, + "search_result": cache_data, + "embedding": None, + } + else: + eval_query_data = { + "question": pre_embedding_data, + "embedding": embedding_data, + } + + eval_cache_data = { + "question": ret[0], + "answer": ret[1], + "search_result": cache_data, + "embedding": None + } + + if len(pre_embedding_data) <= 256: + if rank_threshold <= rank: + cache_answers.append((rank, ret[1])) + cache_questions.append((rank, ret[0])) + cache_ids.append((rank, primary_id)) + else: + if rank_threshold_long <= rank: + cache_answers.append((rank, ret[1])) + cache_questions.append((rank, ret[0])) + cache_ids.append((rank, primary_id)) + else: + # 不使用 reranker 时，走原来的逻辑 + for cache_data in cache_data_list: + primary_id = cache_data[1] + ret = chat_cache.data_manager.get_scalar_data( + cache_data, extra_param=context.get("get_scalar_data", None) + ) + if ret is None: + continue + + if "deps" in context and hasattr(ret.question, "deps"): + eval_query_data = { + "question": context["deps"][0]["data"], + "embedding": None + } + eval_cache_data = { + "question": ret.question.deps[0].data, + "answer": ret.answers[0].answer, + "search_result": cache_data, + "embedding": None, + } + else: + eval_query_data = { + "question": pre_embedding_data, + "embedding": embedding_data, + } + + eval_cache_data = { + "question": ret[0], + "answer": ret[1], + "search_result": cache_data, + "embedding": None + } + rank = chat_cache.similarity_evaluation.evaluation( + eval_query_data, + eval_cache_data, + extra_param=context.get("evaluation_func", None), + ) + + if len(pre_embedding_data) <= 256: + if rank_threshold <= rank: + cache_answers.append((rank, ret[1])) + cache_questions.append((rank, ret[0])) + cache_ids.append((rank, primary_id)) + else: + if rank_threshold_long <= rank: + cache_answers.append((rank, ret[1])) + cache_questions.append((rank, ret[0])) + cache_ids.append((rank, primary_id)) - if len(pre_embedding_data) <= 256: - if rank_threshold <= rank: - cache_answers.append((rank, ret[1])) - cache_questions.append((rank, ret[0])) - cache_ids.append((rank, primary_id)) - else: - if rank_threshold_long <= rank: - cache_answers.append((rank, ret[1])) - cache_questions.append((rank, ret[0])) - cache_ids.append((rank, primary_id)) cache_answers = sorted(cache_answers, key=lambda x: x[0], reverse=True) cache_questions = sorted(cache_questions, key=lambda x: x[0], reverse=True) cache_ids = sorted(cache_ids, key=lambda x: x[0], reverse=True) @@ -141,4 +192,4 @@ def adapt_query(cache_data_convert, *args, **kwargs): logging.info('update_hit_count except, please check!') chat_cache.report.hint_cache() - return cache_data_convert(return_message, return_query) + return cache_data_convert(return_message, return_query) \ No newline at end of file From e6ef83c757dd0dff13c8074bdc1919a8c0451d9d Mon Sep 17 00:00:00 2001 From: "Amber.Z" Date: Fri, 20 Dec 2024 14:46:34 +0800 Subject: [PATCH 16/36] update --- README.md | 3 +- README_CN.md | 85 +++++++++++++++++++++++++++++++--------------------- 2 files changed, 53 insertions(+), 35 deletions(-) diff --git a/README.md b/README.md index 6520171..c500d7d 100644 --- a/README.md +++ b/README.md @@ -43,6 +43,7 @@ ModelCache ## News +- 🔥🔥[2024.10.22] Added tasks for 1024 developer day. - 🔥🔥[2024.04.09] Added Redis Search to store and retrieve embeddings in multi-tenant. This can reduce the interaction time between Cache and vector databases to 10ms. - 🔥🔥[2023.12.10] Integrated LLM embedding frameworks such as 'llmEmb', 'ONNX', 'PaddleNLP', 'FastText', and the image embedding framework 'timm' to bolster embedding functionality. - 🔥🔥[2023.11.20] Integrated local storage, such as sqlite and faiss. This enables you to initiate quick and convenient tests. @@ -60,7 +61,7 @@ Codefuse-ModelCache is a semantic cache for large language models (LLMs). By cac You can find the start script in `flask4modelcache.py` and `flask4modelcache_demo.py`. -- `flask4modelcache_demo.py`: A quick test service that embeds SQLite and FAISS. No database configuration required. +- `flask4modelcache_demo.py`: A quick test service that embeds SQLite and FAISS. No database configuration required. - `flask4modelcache.py`: The standard service that requires MySQL and Milvus configuration. ### Dependencies diff --git a/README_CN.md b/README_CN.md index 89c3fad..b2e70a9 100644 --- a/README_CN.md +++ b/README_CN.md @@ -16,19 +16,35 @@ ModelCache ## Contents +- [Contents](#contents) - [新闻](#新闻) - [项目简介](#项目简介) +- [架构大图](#架构大图) - [快速部署](#快速部署) + - [环境依赖](#环境依赖) + - [启动服务](#启动服务) + - [启动 Demo](#启动-demo) + - [启动标准服务](#启动标准服务) - [服务访问](#服务访问) + - [写入 cache](#写入-cache) + - [查询 cache](#查询-cache) + - [清空 cache](#清空-cache) - [文章](#文章) -- [架构大图](#架构大图) +- [功能对比](#功能对比) - [核心功能](#核心功能) +- [Todo List](#todo-list) + - [Adapter](#adapter) + - [Embedding model\&inference](#embedding-modelinference) + - [Scalar Storage](#scalar-storage) + - [Vector Storage](#vector-storage) + - [Ranking](#ranking) + - [Service](#service) - [致谢](#致谢) -- [Contributing](#Contributing) +- [Contributing](#contributing) ## 新闻 -- 🔥🔥[2024.10.22] 增加1024程序员节任务。 +- 🔥🔥[2024.10.22] 增加1024程序员节任务。 - 🔥🔥[2024.04.09] 增加了多租户场景中Redis Search存储和检索embedding的能力，可以将Cache和向量数据库的交互耗时降低至10ms内。 - 🔥🔥[2023.12.10] 增加llmEmb、onnx、paddlenlp、fasttext等LLM embedding框架，并增加timm 图片embedding框架，用于提供更丰富的embedding能力。 - 🔥🔥[2023.11.20] codefuse-ModelCache增加本地存储能力, 适配了嵌入式数据库sqlite、faiss，方便用户快速启动测试。 @@ -38,53 +54,58 @@ ModelCache Codefuse-ModelCache 是一个开源的大模型语义缓存系统，通过缓存已生成的模型结果，降低类似请求的响应时间，提升用户体验。该项目从服务优化角度出发，引入缓存机制，在资源有限和对实时性要求较高的场景下，帮助企业和研究机构降低推理部署成本、提升模型性能和效率、提供规模化大模型服务。我们希望通过开源，分享交流大模型语义Cache的相关技术。 +## 架构大图 + +![modelcache modules](docs/modelcache_modules_20240409.png) + ## 快速部署 -项目中启动服务脚本分为flask4modelcache.py 和 flask4modelcache_demo.py，其中： +项目中启动服务脚本分为 `flask4modelcache.py` 和 `flask4modelcache_demo.py`，其中： -- flask4modelcache_demo.py 为快速测试服务，内嵌了sqlite和faiss，用户无需关心数据库相关事宜。 -- flask4modelcache.py 为正常服务，需用户具备mysql和milvus等数据库服务。 +- `flask4modelcache_demo.py` 为快速测试服务，内嵌了 SQLite 和 FAISS，用户无需关心数据库相关事宜。 +- `flask4modelcache.py` 为正常服务，需用户具备 MySQL 和 Milvus 等数据库服务。 ### 环境依赖 -- python版本: 3.8及以上 +- python版本: 3.8 及以上 - 依赖包安装： ```shell pip install -r requirements.txt ``` -### 服务启动 +### 启动服务 -#### Demo服务启动 +#### 启动 Demo -- 离线模型bin文件下载， 参考地址：[https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main)，并将下载的bin文件，放到 model/text2vec-base-chinese 文件夹中。 -- 执行flask4modelcache_demo.py启动服务。 +- 离线模型 bin 文件下载， 参考地址：[Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main)，并将下载的 bin 文件，放到 `model/text2vec-base-chinese` 文件夹中。 +- 执行 `flask4modelcache_demo.py` 启动服务。 -```shell -cd CodeFuse-ModelCache -``` -```shell -python flask4modelcache_demo.py -``` + ```shell + cd CodeFuse-ModelCache + ``` + + ```shell + python flask4modelcache_demo.py + ``` -#### 正常服务启动 +#### 启动标准服务 -在启动服务前，应该进行如下环境配置： +在启动标准服务前，应该进行如下环境配置： -1. 安装关系数据库 mysql， 导入sql创建数据表，sql文件:```reference_doc/create_table.sql``` -2. 安装向量数据库milvus +1. 安装关系数据库 MySQL， 导入 SQL 创建数据表，MySQL 文件:```reference_doc/create_table.sql```。 +2. 安装向量数据库 Milvus。 3. 在配置文件中添加数据库访问信息，配置文件为： 1. ```modelcache/config/milvus_config.ini``` 2. ```modelcache/config/mysql_config.ini``` -4. 离线模型bin文件下载， 参考地址：[https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main)，并将下载的bin文件，放到 model/text2vec-base-chinese 文件夹中 +4. 离线模型 bin 文件下载， 参考地址：[Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main)，并将下载的 bin 文件，放到 `model/text2vec-base-chinese` 文件夹中。 5. 通过flask4modelcache.py脚本启动后端服务。 ## 服务访问 -当前服务以restful API方式提供3个核心功能：数据写入，cache查询和cache数据清空。请求demo 如下： +当前服务以 restful API 方式提供 3 个核心功能：数据写入，cache 查询和 cache 数据清空。请求 demo 如下： -### cache写入 +### 写入 cache ```python import json @@ -99,7 +120,7 @@ headers = {"Content-Type": "application/json"} res = requests.post(url, headers=headers, json=json.dumps(data)) ``` -### cache查询 +### 查询 cache ```python import json @@ -114,7 +135,7 @@ headers = {"Content-Type": "application/json"} res = requests.post(url, headers=headers, json=json.dumps(data)) ``` -### cache清空 +### 清空 cache ```python import json @@ -133,13 +154,9 @@ res = requests.post(url, headers=headers, json=json.dumps(data)) https://mp.weixin.qq.com/s/ExIRu2o7yvXa6nNLZcCfhQ -## 架构大图 - -![modelcache modules](docs/modelcache_modules_20240409.png) - ## 功能对比 -功能方面，为了解决huggingface网络问题并提升推理速度，增加了embedding本地推理能力。鉴于SqlAlchemy框架存在一些限制，我们对关系数据库交互模块进行了重写，以更灵活地实现数据库操作。在实践中，大型模型产品需要与多个用户和多个模型对接，因此在ModelCache中增加了对多租户的支持，同时也初步兼容了系统指令和多轮会话。 +功能方面，为了解决 Hugging Face 网络问题并提升推理速度，增加了 embedding 本地推理能力。鉴于 SqlAlchemy 框架存在一些限制，我们对关系数据库交互模块进行了重写，以更灵活地实现数据库操作。在实践中，大型模型产品需要与多个用户和多个模型对接，因此在 ModelCache 中增加了对多租户的支持，同时也初步兼容了系统指令和多轮会话。 @@ -264,7 +281,7 @@ https://mp.weixin.qq.com/s/ExIRu2o7yvXa6nNLZcCfhQ ## 核心功能 -在ModelCache中，沿用了GPTCache的主要思想，包含了一系列核心模块：adapter、embedding、similarity和data_manager。adapter模块主要功能是处理各种任务的业务逻辑，并且能够将embedding、similarity、data_manager等模块串联起来；embedding模块主要负责将文本转换为语义向量表示，它将用户的查询转换为向量形式，并用于后续的召回或存储操作；rank模块用于对召回的向量进行相似度排序和评估；data_manager模块主要用于管理数据库。同时，为了更好的在工业界落地，我们做了架构和功能上的升级，如下： +在ModelCache 中，沿用了 GPTCache 的主要思想，包含了一系列核心模块：adapter、embedding、similarity 和 data_manager。adapter模块主要功能是处理各种任务的业务逻辑，并且能够将 embedding、similarity、data_manager等模块串联起来；embedding 模块主要负责将文本转换为语义向量表示，它将用户的查询转换为向量形式，并用于后续的召回或存储操作；rank 模块用于对召回的向量进行相似度排序和评估；data_manager 模块主要用于管理数据库。同时，为了更好的在工业界落地，我们做了架构和功能上的升级，如下： - [x] 架构调整（轻量化集成）：以类redis的缓存模式嵌入到大模型产品中，提供语义缓存能力，不会干扰LLM调用和安全审核等功能，适配所有大模型服务。 - [x] 多种模型加载方案： @@ -286,11 +303,11 @@ https://mp.weixin.qq.com/s/ExIRu2o7yvXa6nNLZcCfhQ ### Adapter -- [ ] register adapter for Milvus：根据scope中的model参数，初始化对应Collection 并且执行load操作。 +- [ ] register adapter for Milvus：根据 scope 中的 model 参数，初始化对应 Collection 并且执行 load 操作。 ### Embedding model&inference -- [ ] inference优化：优化embedding推理速度，适配fastertransformer, TurboTransformers, ByteTransformer等推理引擎。 +- [ ] inference 优化：优化 embedding 推理速度，适配fastertransformer、TurboTransformers 和 ByteTransformer 等推理引擎。 - [ ] 兼容huggingface模型和modelscope模型，提供更多模型加载方式。 ### Scalar Storage From 19873489f564a0c229c2f5734f8e2b82afcfca04 Mon Sep 17 00:00:00 2001 From: "Amber.Z" Date: Fri, 20 Dec 2024 15:27:59 +0800 Subject: [PATCH 17/36] add MultiModal Cache --- .../cache-service-cost-time-distribution.webp | Bin 0 -> 11504 bytes docs/time-cost-comparison.webp | Bin 0 -> 64626 bytes mulicache-readme-cn.md | 127 ++++++++++++++++++ 3 files changed, 127 insertions(+) create mode 100644 docs/cache-service-cost-time-distribution.webp create mode 100644 docs/time-cost-comparison.webp create mode 100644 mulicache-readme-cn.md diff --git a/docs/cache-service-cost-time-distribution.webp b/docs/cache-service-cost-time-distribution.webp new file mode 100644 index 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| 文本+图像 | image_url/image_base64 | 支持 | + +- **兼容性**：支持文本和图片链接（image_url）和图片 Base64 编码三种数据格式及其组合。 +- **数据隔离**：支持多模型数据隔离，允许不同数据模型在同一系统中独立运行。 +- **模态隔离**：支持同一模型下不同模态数据（如文本和图像）的隔离处理。 + +## 性能 + +我们在生产环境中使用企业级数据库对 MultiModal Cache 进行了全面的性能评估。以下是详细的性能数据： + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
请求类型Cache Hit总耗时范围组件组件耗时
TextHit420ms-520msMulti-Encoder (Text):~300ms
向量存储检索40-50ms
关系存储检索60-70ms
Not Hit300ms+N(s)Multi-Encoder (Text):~300ms
向量存储检索40-50ms
大模型调用N (s)
IMG_TEXTHit600ms-800msMulti-Encoder (image+text)~600ms
向量存储检索40-50ms
关系存储检索60-70ms
Not Hit600ms+N(s)Multi-Encoder (image+text)~600ms
向量存储检索40-50ms
大模型调用N (s)
+ +根据目前的评估结果，Embedding 的推理时间存在较大的优化空间。 +**说明**：使用嵌入式数据库可能会进一步提升性能。 + +## 效果评估 + +为全面评估 Cache 对模型服务的影响，我们进行了端到端的性能测试，ua 比较了有 Cache 和无 Cache 两种服务配置。我们使用了 5000 个测试用例的数据集进行自动化测试。 + +- 有 Cache 的预发模型服务：观察其响应时间，预期 Cache 的引入能够显著提升服务的性能，降低延迟。 +- 无 Cache 的线上模型服务，以获取其原始性能指标和输出结果。这些数据将作为对比基准。 + +为了确保 Cache 引入后的数据准确性和一致性，我们比较了两个服务返回的结果，验证了 Cache 机制是否会影响最终用户收到的回复内容。 + +与原始的直接模型调用方式相比，Cache Service 的调用耗时数据呈现出稳定的分布特征，性能上并不会随着模型参数规模的增加而受到影响。在传统情况下，随着模型参数规模的扩大，模型调用的耗时往往会上升，这是因为更大规模的模型需要更多的计算资源。Cache 服务通过存储经常访问的数据来避免重复的计算，从而一定程度上解耦了耗时与模型复杂性之间的关联。 + +![cache-service-cost-time-distribution](docs/cache-service-cost-time-distribution.webp) + +我们对缓存命中的耗时与实际调用模型的耗时进行了对比分析。实验数据表明，在集成 Cache Service之后，基于 llama7B 模型，缓存命中所带来的性能提升超过了 40%。预计随着模型的持续迭代与优化，性能提升的幅度将会有更进一步的增长。 + +![time-cost-comparison](docs/time-cost-comparison.webp) + +## 参与贡献 + +MultiModal Cache 是一个充满潜力的开源项目，我们欢迎各种形式的贡献： + +- 提交问题和建议 +- 参与代码编写 +- 完善文档和示例 + +无论您是经验丰富的开发者还是新手，您的参与都将使这个项目更加出色，同时为开源社区做出贡献。 From 8753cb3ebbd73b92b6d4a421e4d26752fb23a1fe Mon Sep 17 00:00:00 2001 From: powerli2002 <845160705@qq.com> Date: Sun, 22 Dec 2024 10:00:28 +0800 Subject: [PATCH 18/36] update requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 63899e9..531fd82 100644 --- a/requirements.txt +++ b/requirements.txt @@ -14,5 +14,6 @@ redis==5.0.1 modelscope==1.14.0 fastapi==0.115.5 uvicorn==0.32.0 +chromadb==0.5.23 elasticsearch==7.10.0 snowflake-id==1.0.2 \ No newline at end of file From e035f8236b15081dbb0cc34d6e027152a1a72a7c Mon Sep 17 00:00:00 2001 From: cjn395973 <1046783533@qq.com> Date: Sun, 22 Dec 2024 12:29:24 +0800 Subject: [PATCH 19/36] support startup with docker-compose --- .gitignore | 5 ++- Dockerfile | 8 ++++ data/milvus/embedEtcd.yaml | 5 +++ data/milvus/user.yaml | 1 + data/mysql/init/init.sql | 32 +++++++++++++ data/mysql/my.conf | 9 ++++ docker-compose.yaml | 70 +++++++++++++++++++++++++++++ modelcache/config/milvus_config.ini | 4 +- modelcache/config/mysql_config.ini | 10 ++--- requirements.txt | 2 +- 10 files changed, 137 insertions(+), 9 deletions(-) create mode 100644 Dockerfile create mode 100644 data/milvus/embedEtcd.yaml create mode 100644 data/milvus/user.yaml create mode 100644 data/mysql/init/init.sql create mode 100644 data/mysql/my.conf create mode 100644 docker-compose.yaml diff --git a/.gitignore b/.gitignore index cd9b201..5d4a0f7 100644 --- a/.gitignore +++ b/.gitignore @@ -142,4 +142,7 @@ dmypy.json **/multicache_serving.py **/modelcache_serving.py -**/model/ \ No newline at end of file +**/model/ + +/data/milvus/db +/data/mysql/db \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..c8ed488 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,8 @@ +FROM python:3.9-slim-bookworm + +WORKDIR /home/user + +COPY ./requirements.txt /home/user/docker_requirements.txt + +RUN pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple +RUN pip install -r /home/user/docker_requirements.txt --retries 5 --timeout 120 diff --git a/data/milvus/embedEtcd.yaml b/data/milvus/embedEtcd.yaml new file mode 100644 index 0000000..32954fa --- /dev/null +++ b/data/milvus/embedEtcd.yaml @@ -0,0 +1,5 @@ +listen-client-urls: http://0.0.0.0:2379 +advertise-client-urls: http://0.0.0.0:2379 +quota-backend-bytes: 4294967296 +auto-compaction-mode: revision +auto-compaction-retention: '1000' diff --git a/data/milvus/user.yaml b/data/milvus/user.yaml new file mode 100644 index 0000000..3638d96 --- /dev/null +++ b/data/milvus/user.yaml @@ -0,0 +1 @@ +# Extra config to override default milvus.yaml \ No newline at end of file diff --git a/data/mysql/init/init.sql b/data/mysql/init/init.sql new file mode 100644 index 0000000..8e34c0a --- /dev/null +++ b/data/mysql/init/init.sql @@ -0,0 +1,32 @@ +CREATE DATABASE IF NOT EXISTS `modelcache`; + +USE `modelcache`; + +CREATE TABLE IF NOT EXISTS `modelcache_llm_answer` ( + `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT comment '主键', + `gmt_create` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP comment '创建时间', + `gmt_modified` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP comment '修改时间', + `question` text NOT NULL comment 'question', + `answer` text NOT NULL comment 'answer', + `answer_type` int(11) NOT NULL comment 'answer_type', + `hit_count` int(11) NOT NULL DEFAULT '0' comment 'hit_count', + `model` varchar(1000) NOT NULL comment 'model', + `embedding_data` blob NOT NULL comment 'embedding_data', + `is_deleted` tinyint(1) NOT NULL DEFAULT '0' COMMENT 'delete state(0 Not deleted,-1 deleted)', + PRIMARY KEY(`id`) +) AUTO_INCREMENT = 1 DEFAULT CHARSET = utf8mb4 COMMENT = 'cache_codegpt_answer'; + +CREATE TABLE IF NOT EXISTS `modelcache_query_log` ( + `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT comment '主键', + `gmt_create` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP comment '创建时间', + `gmt_modified` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP comment '修改时间', + `error_code` int(11) NOT NULL comment 'errorCode', + `error_desc` varchar(1000) NOT NULL comment 'errorDesc', + `cache_hit` varchar(100) NOT NULL comment 'cacheHit', + `delta_time` float NOT NULL comment 'delta_time', + `model` varchar(1000) NOT NULL comment 'model', + `query` text NOT NULL comment 'query', + `hit_query` text NOT NULL comment 'hitQuery', + `answer` text NOT NULL comment 'answer', + PRIMARY KEY(`id`) +) AUTO_INCREMENT = 1 DEFAULT CHARSET = utf8mb4 COMMENT = 'modelcache_query_log'; diff --git a/data/mysql/my.conf b/data/mysql/my.conf new file mode 100644 index 0000000..8594fef --- /dev/null +++ b/data/mysql/my.conf @@ -0,0 +1,9 @@ +[mysqld] +character-set-server=utf8mb4 +collation-server=utf8mb4_unicode_ci + +[client] +default-character-set=utf8mb4 + +[mysql] +default-character-set=utf8mb4 \ No newline at end of file diff --git a/docker-compose.yaml b/docker-compose.yaml new file mode 100644 index 0000000..e079df8 --- /dev/null +++ b/docker-compose.yaml @@ -0,0 +1,70 @@ +version: 'Beta' +services: + mysql: + image: mysql:8.0.23 + container_name: mysql + environment: + MYSQL_ROOT_PASSWORD: 'root' + MYSQL_DATABASE: 'modelcache' + MYSQL_USER: 'modelcache' + MYSQL_PASSWORD: 'modelcache' + ports: + - 3306:3306 + volumes: + - ./data/mysql/db:/var/lib/mysql + - ./data/mysql/my.cnf:/etc/mysql/conf.d/my.cnf + - ./data/mysql/init:/docker-entrypoint-initdb.d + restart: on-failure + networks: + - modelcache + + milvus: + image: milvusdb/milvus:v2.5.0-beta + container_name: milvus + security_opt: + - seccomp:unconfined + environment: + ETCD_USE_EMBED: true + ETCD_DATA_DIR: /var/lib/milvus/etcd + ETCD_CONFIG_PATH: /milvus/configs/embedEtcd.yaml + COMMON_STORAGETYPE: local + volumes: + - ./data/milvus/db:/var/lib/milvus + - ./data/milvus/embedEtcd.yaml:/milvus/configs/embedEtcd.yaml + - ./data/milvus/user.yaml:/milvus/configs/user.yaml + ports: + - 19530:19530 + - 9091:9091 + - 2379:2379 + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:9091/healthz"] + interval: 30s + start_period: 90s + timeout: 20s + retries: 3 + networks: + - modelcache + restart: on-failure + command: milvus run standalone + + modelcache: + build: + context: . + dockerfile: Dockerfile + container_name: modelcache + image: modelcache:0.1.0 + ports: + - 5000:5000 + volumes: + - ./model:/home/user/model + - ./modelcache:/home/user/modelcache + - ./modelcache_mm:/home/user/modelcache_mm + - ./fastapi4modelcache.py:/home/user/fastapi4modelcache.py + networks: + - modelcache + restart: on-failure + command: sh -c "uvicorn fastapi4modelcache:app --reload --reload-dir /home/user --port=5000 --host=0.0.0.0" + +networks: + modelcache: + external: true \ No newline at end of file diff --git a/modelcache/config/milvus_config.ini b/modelcache/config/milvus_config.ini index f5bd532..dee6fb6 100644 --- a/modelcache/config/milvus_config.ini +++ b/modelcache/config/milvus_config.ini @@ -1,5 +1,5 @@ [milvus] -host = '' -port = '' +host = milvus +port = 19530 user = '' password = '' \ No newline at end of file diff --git a/modelcache/config/mysql_config.ini b/modelcache/config/mysql_config.ini index 2c63f0e..2c17b81 100644 --- a/modelcache/config/mysql_config.ini +++ b/modelcache/config/mysql_config.ini @@ -1,6 +1,6 @@ [mysql] -host = '' -port = '' -username = '' -password = '' -database = '' +host = mysql +port = 3306 +username = modelcache +password = modelcache +database = modelcache diff --git a/requirements.txt b/requirements.txt index 95e1948..c09197c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,7 +7,7 @@ openai==0.28.1 pymilvus==2.3.1 PyMySQL==1.1.0 Requests==2.31.0 -torch==2.1.0 +torch==2.1.1 transformers==4.38.2 faiss-cpu==1.7.4 redis==5.0.1 From c91934ce28e42d5f71dab02ad15f042a260a71d5 Mon Sep 17 00:00:00 2001 From: cjn395973 <1046783533@qq.com> Date: Sun, 22 Dec 2024 12:29:56 +0800 Subject: [PATCH 20/36] update readme --- README.md | 18 ++++++++++++++++-- README_CN.md | 19 +++++++++++++++++-- 2 files changed, 33 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 6ebc5f7..aeba9cd 100644 --- a/README.md +++ b/README.md @@ -43,7 +43,7 @@ The project's startup scripts are divided into flask4modelcache.py and flask4mod pip install -r requirements.txt ``` ### Service Startup -#### Demo Service Startup +#### Method 1: Demo Service Startup 1. Download the embedding model bin file from the following address: [https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Place the downloaded bin file in the model/text2vec-base-chinese folder. 2. Start the backend service using the flask4modelcache_dome.py script. ```shell @@ -53,7 +53,21 @@ cd CodeFuse-ModelCache python flask4modelcache_demo.py ``` -#### Normal Service Startup +#### Method 2: Service Startup With Docker-compose +1. Download the embedding model bin file from the following address: [https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Place the downloaded bin file in the model/text2vec-base-chinese folder. +2. Configure docker network, only need to execute once +```shell +docker network create modelcache +``` +3. Execute the docker-compose command +```shell +# When the modelcache image does not exist locally for the first time, or when the Dockerfile is changed +docker-compose up --build + +# This is not the first run and the Dockerfile has not changed +docker-compose up +``` +#### Method 3: Service Startup Without Docker-compose Before starting the service, the following environment configurations should be performed: 1. Install the relational database MySQL and import the SQL file to create the data tables. The SQL file can be found at: ```reference_doc/create_table.sql``` 2. Install the vector database Milvus. diff --git a/README_CN.md b/README_CN.md index 5959380..2ea7da6 100644 --- a/README_CN.md +++ b/README_CN.md @@ -44,7 +44,7 @@ Codefuse-ModelCache 是一个开源的大模型语义缓存系统，通过缓存 pip install -r requirements.txt ``` ### 服务启动 -#### Demo服务启动 +#### 方式一：Demo服务启动 - 离线模型bin文件下载， 参考地址：[https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main)，并将下载的bin文件，放到 model/text2vec-base-chinese 文件夹中。 - 执行flask4modelcache_demo.py启动服务。 ```shell @@ -54,7 +54,22 @@ cd CodeFuse-ModelCache python flask4modelcache_demo.py ``` -#### 正常服务启动 +#### 方式二：通过 docker-compose 启动服务 +- 离线模型bin文件下载， 参考地址：[https://huggingface.co/shibing624/text2vec-base-chinese/tree/main](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main)，并将下载的bin文件，放到 model/text2vec-base-chinese 文件夹中。 + +- 配置 docker network，只需执行一次 +```shell +docker network create modelcache +``` +- 执行 docker-compose 命令 +```shell +# 首次运行本地不存在 modelcache 镜像、或 Dockerfile 变更时 +docker-compose up --build + +# 非首次运行，且 Dockerfile 无变更 +docker-compose up +``` +#### 方式三：不通过 docker-compose 启动服务 在启动服务前，应该进行如下环境配置： 1. 安装关系数据库 mysql， 导入sql创建数据表，sql文件:```reference_doc/create_table.sql``` 2. 安装向量数据库milvus From 10c9b06dd62be0360ee0ef5ce7c436a41b8bd44c Mon Sep 17 00:00:00 2001 From: cjn395973 <1046783533@qq.com> Date: Sun, 22 Dec 2024 12:44:07 +0800 Subject: [PATCH 21/36] update readme --- README.md | 17 +++++++++++++++++ README_CN.md | 20 ++++++++++++++++++++ 2 files changed, 37 insertions(+) diff --git a/README.md b/README.md index c500d7d..4326b2f 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,7 @@ ModelCache - [Dependencies](#dependencies) - [Start service](#start-service) - [Start demo](#start-demo) + - [Service Startup With Docker-compose](#service-startup-with-docker-compose) - [Start normal service](#start-normal-service) - [Visit the service](#visit-the-service) - [Write cache](#write-cache) @@ -87,7 +88,23 @@ You can find the start script in `flask4modelcache.py` and `flask4modelcache_dem ```shell python flask4modelcache_demo.py ``` +#### Service Startup With Docker-compose +1. Download the embedding model bin file from [Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main). Place it in the `model/text2vec-base-chinese` folder. +2. Configure docker network, only need to execute once +```shell +cd CodeFuse-ModelCache +``` +```shell +docker network create modelcache +``` +3. Execute the docker-compose command +```shell +# When the modelcache image does not exist locally for the first time, or when the Dockerfile is changed +docker-compose up --build +# This is not the first run and the Dockerfile has not changed +docker-compose up +``` #### Start normal service Before you start standard service, do these steps: diff --git a/README_CN.md b/README_CN.md index b2e70a9..8528603 100644 --- a/README_CN.md +++ b/README_CN.md @@ -24,6 +24,7 @@ ModelCache - [环境依赖](#环境依赖) - [启动服务](#启动服务) - [启动 Demo](#启动-demo) + - [通过 docker-compose 启动服务](#通过-docker-compose-启动服务) - [启动标准服务](#启动标准服务) - [服务访问](#服务访问) - [写入 cache](#写入-cache) @@ -89,6 +90,25 @@ Codefuse-ModelCache 是一个开源的大模型语义缓存系统，通过缓存 python flask4modelcache_demo.py ``` +#### 通过 docker-compose 启动服务 +- 离线模型 bin 文件下载， 参考地址：[Hugging Face](https://huggingface.co/shibing624/text2vec-base-chinese/tree/main)，并将下载的 bin 文件，放到 `model/text2vec-base-chinese` 文件夹中。 + +- 配置 docker network，只需执行一次 +```shell +cd CodeFuse-ModelCache +``` +```shell +docker network create modelcache +``` +- 执行 docker-compose 命令 +```shell +# 首次运行本地不存在 modelcache 镜像、或 Dockerfile 变更时 +docker-compose up --build + +# 非首次运行，且 Dockerfile 无变更 +docker-compose up +``` + #### 启动标准服务 在启动标准服务前，应该进行如下环境配置： From 82ba867408949bf896d1317a4d17d6c4408b84da Mon Sep 17 00:00:00 2001 From: Yuval Roth Date: Tue, 20 May 2025 16:57:59 +0300 Subject: [PATCH 22/36] made it run --- .gitignore | 2 +- docker-compose.yaml | 42 ++++++++++++++--------------- flask4modelcache.py | 15 ++++++----- model/clone_model_repository.bat | 2 ++ modelcache/adapter/adapter_query.py | 10 ++++--- modelcache/config/milvus_config.ini | 2 +- modelcache/config/mysql_config.ini | 2 +- requirements.txt | 14 +++++----- 8 files changed, 48 insertions(+), 41 deletions(-) create mode 100644 model/clone_model_repository.bat diff --git a/.gitignore b/.gitignore index 5d4a0f7..0e57011 100644 --- a/.gitignore +++ b/.gitignore @@ -142,7 +142,7 @@ dmypy.json **/multicache_serving.py **/modelcache_serving.py -**/model/ +**/model/text2vec-base-chinese /data/milvus/db /data/mysql/db \ No newline at end of file diff --git a/docker-compose.yaml b/docker-compose.yaml index e079df8..26c8456 100644 --- a/docker-compose.yaml +++ b/docker-compose.yaml @@ -1,7 +1,7 @@ -version: 'Beta' +name: "modelcache" services: mysql: - image: mysql:8.0.23 + image: mysql:9.3.0 container_name: mysql environment: MYSQL_ROOT_PASSWORD: 'root' @@ -19,7 +19,7 @@ services: - modelcache milvus: - image: milvusdb/milvus:v2.5.0-beta + image: milvusdb/milvus:v2.5.10 container_name: milvus security_opt: - seccomp:unconfined @@ -47,24 +47,24 @@ services: restart: on-failure command: milvus run standalone - modelcache: - build: - context: . - dockerfile: Dockerfile - container_name: modelcache - image: modelcache:0.1.0 - ports: - - 5000:5000 - volumes: - - ./model:/home/user/model - - ./modelcache:/home/user/modelcache - - ./modelcache_mm:/home/user/modelcache_mm - - ./fastapi4modelcache.py:/home/user/fastapi4modelcache.py - networks: - - modelcache - restart: on-failure - command: sh -c "uvicorn fastapi4modelcache:app --reload --reload-dir /home/user --port=5000 --host=0.0.0.0" +# modelcache: +# build: +# context: . +# dockerfile: Dockerfile +# container_name: modelcache +# image: modelcache:0.1.0 +# ports: +# - 5000:5000 +# volumes: +# - ./model:/home/user/model +# - ./modelcache:/home/user/modelcache +# - ./modelcache_mm:/home/user/modelcache_mm +# - ./fastapi4modelcache.py:/home/user/fastapi4modelcache.py +# networks: +# - modelcache +# restart: on-failure +# command: sh -c "uvicorn fastapi4modelcache:app --reload --reload-dir /home/user --port=5000 --host=0.0.0.0" networks: modelcache: - external: true \ No newline at end of file + driver: bridge \ No newline at end of file diff --git a/flask4modelcache.py b/flask4modelcache.py index ca70014..16f92cb 100644 --- a/flask4modelcache.py +++ b/flask4modelcache.py @@ -38,8 +38,8 @@ def response_hitquery(cache_resp): milvus_config = configparser.ConfigParser() milvus_config.read('modelcache/config/milvus_config.ini') -es_config = configparser.ConfigParser() -es_config.read('modelcache/config/elasticsearch_config.ini') +# es_config = configparser.ConfigParser() +# es_config.read('modelcache/config/elasticsearch_config.ini') # redis_config = configparser.ConfigParser() # redis_config.read('modelcache/config/redis_config.ini') @@ -47,7 +47,7 @@ def response_hitquery(cache_resp): # chromadb_config = configparser.ConfigParser() # chromadb_config.read('modelcache/config/chromadb_config.ini') -data_manager = get_data_manager(CacheBase("elasticsearch", config=es_config), +data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), VectorBase("milvus", dimension=data2vec.dimension, milvus_config=milvus_config)) @@ -77,18 +77,19 @@ def first_flask(): # 视图函数 @app.route('/modelcache', methods=['GET', 'POST']) def user_backend(): + param_dict = [] try: if request.method == 'POST': - request_data = request.json + param_dict = request.json elif request.method == 'GET': - request_data = request.args - param_dict = json.loads(request_data) + param_dict = request.args except Exception as e: result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} cache.data_manager.save_query_resp(result, model='', query='', delta_time=0) return json.dumps(result) + # param parsing try: request_type = param_dict.get("type") @@ -197,4 +198,4 @@ def user_backend(): if __name__ == '__main__': - app.run(host='0.0.0.0', port=5000, debug=True) + app.run(host='0.0.0.0', port=5000) diff --git a/model/clone_model_repository.bat b/model/clone_model_repository.bat new file mode 100644 index 0000000..10f2421 --- /dev/null +++ b/model/clone_model_repository.bat @@ -0,0 +1,2 @@ +git lfs install +git clone https://huggingface.co/shibing624/text2vec-base-chinese \ No newline at end of file diff --git a/modelcache/adapter/adapter_query.py b/modelcache/adapter/adapter_query.py index e3be30a..5a91807 100644 --- a/modelcache/adapter/adapter_query.py +++ b/modelcache/adapter/adapter_query.py @@ -7,7 +7,7 @@ from modelcache.processor.pre import multi_analysis from FlagEmbedding import FlagReranker -USE_RERANKER = True # 如果为 True 则启用 reranker，否则使用原有逻辑 +USE_RERANKER = False # 如果为 True 则启用 reranker，否则使用原有逻辑 def adapt_query(cache_data_convert, *args, **kwargs): chat_cache = kwargs.pop("cache_obj", cache) @@ -74,7 +74,7 @@ def adapt_query(cache_data_convert, *args, **kwargs): extra_param=context.get("evaluation_func", None), ) if rank_pre < rank_threshold: - return + return None if USE_RERANKER: reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=False) @@ -86,7 +86,7 @@ def adapt_query(cache_data_convert, *args, **kwargs): if ret is None: continue - rank = reranker.compute_score([pre_embedding_data, ret[0]], normalize=True) + rank = reranker.compute_score([pre_embedding_data, ret[0]], normalize=True)[0] if "deps" in context and hasattr(ret.question, "deps"): eval_query_data = { @@ -192,4 +192,6 @@ def adapt_query(cache_data_convert, *args, **kwargs): logging.info('update_hit_count except, please check!') chat_cache.report.hint_cache() - return cache_data_convert(return_message, return_query) \ No newline at end of file + return cache_data_convert(return_message, return_query) + return None + return None \ No newline at end of file diff --git a/modelcache/config/milvus_config.ini b/modelcache/config/milvus_config.ini index dee6fb6..1e9aa66 100644 --- a/modelcache/config/milvus_config.ini +++ b/modelcache/config/milvus_config.ini @@ -1,5 +1,5 @@ [milvus] -host = milvus +host = localhost port = 19530 user = '' password = '' \ No newline at end of file diff --git a/modelcache/config/mysql_config.ini b/modelcache/config/mysql_config.ini index 2c17b81..3d92385 100644 --- a/modelcache/config/mysql_config.ini +++ b/modelcache/config/mysql_config.ini @@ -1,5 +1,5 @@ [mysql] -host = mysql +host = localhost port = 3306 username = modelcache password = modelcache diff --git a/requirements.txt b/requirements.txt index 9a132bd..6e74137 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,16 +4,18 @@ Flask==3.0.0 numpy==1.24.4 onnxruntime==1.16.1 openai==0.28.1 -pymilvus==2.3.1 -PyMySQL==1.1.0 -Requests==2.31.0 -torch==2.1.1 -transformers==4.38.2 +pymilvus==2.5.9 +PyMySQL==1.1.1 +Requests==2.32.3 +torch==2.7.0 +transformers==4.44.2 faiss-cpu==1.7.4 redis==5.0.1 -modelscope==1.14.0 +modelscope==1.26.0 fastapi==0.115.5 uvicorn==0.32.0 chromadb==0.5.23 elasticsearch==7.10.0 snowflake-id==1.0.2 +flagembedding==1.3.4 +cryptography==45.0.2 \ No newline at end of file From c4f925317f3cdbe747d53b053d7af8af57d38ac1 Mon Sep 17 00:00:00 2001 From: olgaoznovich Date: Wed, 21 May 2025 17:06:57 +0300 Subject: [PATCH 23/36] Added handling for bulk insert Added a flag for normalize because the model we use already normalizes Co-authored-by: olgaoznovich Co-authored-by: Yuval-Roth --- docker-compose.yaml | 16 ++++++++-------- flask4modelcache.py | 11 ++++++----- model/.gitignore | 2 ++ model/clone_model_repository.bat | 2 -- model/download_bert_embedder.bat | 1 + modelcache/adapter/adapter_insert.py | 27 ++++++++++++++++----------- modelcache/manager/data_manager.py | 24 ++++++++++++++---------- modelcache/processor/pre.py | 2 +- requirements.txt | 4 +++- 9 files changed, 51 insertions(+), 38 deletions(-) create mode 100644 model/.gitignore delete mode 100644 model/clone_model_repository.bat create mode 100644 model/download_bert_embedder.bat diff --git a/docker-compose.yaml b/docker-compose.yaml index 26c8456..31ed1ab 100644 --- a/docker-compose.yaml +++ b/docker-compose.yaml @@ -14,7 +14,7 @@ services: - ./data/mysql/db:/var/lib/mysql - ./data/mysql/my.cnf:/etc/mysql/conf.d/my.cnf - ./data/mysql/init:/docker-entrypoint-initdb.d - restart: on-failure +# restart: on-failure networks: - modelcache @@ -36,15 +36,15 @@ services: - 19530:19530 - 9091:9091 - 2379:2379 - healthcheck: - test: ["CMD", "curl", "-f", "http://localhost:9091/healthz"] - interval: 30s - start_period: 90s - timeout: 20s - retries: 3 +# healthcheck: +# test: ["CMD", "curl", "-f", "http://localhost:9091/healthz"] +# interval: 30s +# start_period: 90s +# timeout: 20s +# retries: 3 networks: - modelcache - restart: on-failure +# restart: on-failure command: milvus run standalone # modelcache: diff --git a/flask4modelcache.py b/flask4modelcache.py index 16f92cb..823c66b 100644 --- a/flask4modelcache.py +++ b/flask4modelcache.py @@ -6,7 +6,7 @@ import json from modelcache import cache from modelcache.adapter import adapter -from modelcache.manager import CacheBase, VectorBase, get_data_manager +from modelcache.manager import CacheBase, VectorBase, get_data_manager, data_manager from modelcache.similarity_evaluation.distance import SearchDistanceEvaluation from modelcache.processor.pre import query_multi_splicing from modelcache.processor.pre import insert_multi_splicing @@ -30,8 +30,10 @@ def save_query_info(result, model, query, delta_time_log): def response_hitquery(cache_resp): return cache_resp['hitQuery'] - data2vec = Data2VecAudio() +embedding_func = data2vec.to_embeddings +dimension = data2vec.dimension + mysql_config = configparser.ConfigParser() mysql_config.read('modelcache/config/mysql_config.ini') @@ -48,7 +50,7 @@ def response_hitquery(cache_resp): # chromadb_config.read('modelcache/config/chromadb_config.ini') data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), - VectorBase("milvus", dimension=data2vec.dimension, milvus_config=milvus_config)) + VectorBase("milvus", dimension=dimension, milvus_config=milvus_config)) # data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), @@ -57,9 +59,8 @@ def response_hitquery(cache_resp): # data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), # VectorBase("redis", dimension=data2vec.dimension, redis_config=redis_config)) - cache.init( - embedding_func=data2vec.to_embeddings, + embedding_func=embedding_func, data_manager=data_manager, similarity_evaluation=SearchDistanceEvaluation(), query_pre_embedding_func=query_multi_splicing, diff --git a/model/.gitignore b/model/.gitignore new file mode 100644 index 0000000..08ad955 --- /dev/null +++ b/model/.gitignore @@ -0,0 +1,2 @@ +*.tflite +text2vec-base-chinese/* \ No newline at end of file diff --git a/model/clone_model_repository.bat b/model/clone_model_repository.bat deleted file mode 100644 index 10f2421..0000000 --- a/model/clone_model_repository.bat +++ /dev/null @@ -1,2 +0,0 @@ -git lfs install -git clone https://huggingface.co/shibing624/text2vec-base-chinese \ No newline at end of file diff --git a/model/download_bert_embedder.bat b/model/download_bert_embedder.bat new file mode 100644 index 0000000..e4c26af --- /dev/null +++ b/model/download_bert_embedder.bat @@ -0,0 +1 @@ +curl -o embedder.tflite https://storage.googleapis.com/mediapipe-models/text_embedder/bert_embedder/float32/1/bert_embedder.tflite \ No newline at end of file diff --git a/modelcache/adapter/adapter_insert.py b/modelcache/adapter/adapter_insert.py index 74aa619..c2e2a29 100644 --- a/modelcache/adapter/adapter_insert.py +++ b/modelcache/adapter/adapter_insert.py @@ -14,26 +14,31 @@ def adapt_insert(*args, **kwargs): raise NotInitError() cache_enable = chat_cache.cache_enable_func(*args, **kwargs) context = kwargs.pop("cache_context", {}) - embedding_data = None - pre_embedding_data = chat_cache.insert_pre_embedding_func( - kwargs, - extra_param=context.get("pre_embedding_func", None), - prompts=chat_cache.config.prompts, - ) chat_info = kwargs.pop("chat_info", []) - llm_data = chat_info[-1]['answer'] - if cache_enable: + pre_embedding_data_list = [] + embedding_data_list = [] + llm_data_list = [] + + for row in chat_info: + pre_embedding_data = chat_cache.insert_pre_embedding_func( + row, + extra_param=context.get("pre_embedding_func", None), + prompts=chat_cache.config.prompts, + ) + pre_embedding_data_list.append(pre_embedding_data) + llm_data_list.append(row['answer']) embedding_data = time_cal( chat_cache.embedding_func, func_name="embedding", report_func=chat_cache.report.embedding, )(pre_embedding_data) + embedding_data_list.append(embedding_data) chat_cache.data_manager.save( - pre_embedding_data, - llm_data, - embedding_data, + pre_embedding_data_list, + llm_data_list, + embedding_data_list, model=model, extra_param=context.get("save_func", None) ) diff --git a/modelcache/manager/data_manager.py b/modelcache/manager/data_manager.py index 6734ef2..ce9ab0c 100644 --- a/modelcache/manager/data_manager.py +++ b/modelcache/manager/data_manager.py @@ -7,6 +7,9 @@ import cachetools from abc import abstractmethod, ABCMeta from typing import List, Any, Optional, Union + +from numpy import ndarray + from modelcache.manager.scalar_data.base import ( CacheStorage, CacheData, @@ -21,6 +24,7 @@ from modelcache.manager.eviction_manager import EvictionManager from modelcache.utils.log import modelcache_log +NORMALIZE = True class DataManager(metaclass=ABCMeta): """DataManager manage the cache data, including save and search""" @@ -158,9 +162,9 @@ def __init__( self.v = v self.o = o - def save(self, question, answer, embedding_data, **kwargs): + def save(self, questions: List[any], answers: List[any], embedding_datas: List[any], **kwargs): model = kwargs.pop("model", None) - self.import_data([question], [answer], [embedding_data], model) + self.import_data(questions, answers, embedding_datas, model) def save_query_resp(self, query_resp_dict, **kwargs): save_query_start_time = time.time() @@ -197,9 +201,10 @@ def import_data( raise ParamError("Make sure that all parameters have the same length") cache_datas = [] - embedding_datas = [ - normalize(embedding_data) for embedding_data in embedding_datas - ] + if NORMALIZE: + embedding_datas = [ + normalize(embedding_data) for embedding_data in embedding_datas + ] for i, embedding_data in enumerate(embedding_datas): if self.o is not None: @@ -212,11 +217,9 @@ def import_data( cache_datas.append([ans, question, embedding_data, model]) ids = self.s.batch_insert(cache_datas) + datas_ = [VectorData(id=ids[i], data=embedding_data.astype("float32")) for i, embedding_data in enumerate(embedding_datas)] self.v.mul_add( - [ - VectorData(id=ids[i], data=embedding_data) - for i, embedding_data in enumerate(embedding_datas) - ], + datas_, model ) @@ -235,7 +238,8 @@ def hit_cache_callback(self, res_data, **kwargs): def search(self, embedding_data, **kwargs): model = kwargs.pop("model", None) - embedding_data = normalize(embedding_data) + if NORMALIZE: + embedding_data = normalize(embedding_data) top_k = kwargs.get("top_k", -1) return self.v.search(data=embedding_data, top_k=top_k, model=model) diff --git a/modelcache/processor/pre.py b/modelcache/processor/pre.py index 5875294..0c72d40 100644 --- a/modelcache/processor/pre.py +++ b/modelcache/processor/pre.py @@ -64,7 +64,7 @@ def query_multi_splicing(data: Dict[str, Any], **_: Dict[str, Any]) -> Any: def insert_multi_splicing(data: Dict[str, Any], **_: Dict[str, Any]) -> Any: - insert_query_list = data.get("chat_info")[-1]['query'] + insert_query_list = data['query'] return multi_splicing(insert_query_list) diff --git a/requirements.txt b/requirements.txt index 6e74137..44d332d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -18,4 +18,6 @@ chromadb==0.5.23 elasticsearch==7.10.0 snowflake-id==1.0.2 flagembedding==1.3.4 -cryptography==45.0.2 \ No newline at end of file +cryptography==45.0.2 +mediapipe==0.10.21 +protobuf==4.25.8 From 5127d7700773439c86d8035c08bfd20a4e600ded Mon Sep 17 00:00:00 2001 From: Yuval-Roth Date: Sat, 7 Jun 2025 12:06:33 +0300 Subject: [PATCH 24/36] Added efficiency fixes to Milvus and MySQL Milvus: started loading all the collections into the memory instead of loading the collection into memory anew every time MySQL: Added support for bulk insert to MySQL. Had to change the id generation from AUTO INCREMENT to uuids generated before insertion. This is because MySQL doesn't support bulk insert with returning all ids generated by auto increment. Co-authored-by: olgaoznovich Co-authored-by: Yuval-Roth --- data/mysql/init/init.sql | 2 +- docker-compose.yaml | 2 +- modelcache/adapter/adapter_query.py | 2 +- modelcache/manager/scalar_data/sql_storage.py | 119 +++++++++++++----- modelcache/manager/vector_data/milvus.py | 52 ++++---- reference_doc/create_table.sql | 11 +- 6 files changed, 132 insertions(+), 56 deletions(-) diff --git a/data/mysql/init/init.sql b/data/mysql/init/init.sql index 8e34c0a..a358de2 100644 --- a/data/mysql/init/init.sql +++ b/data/mysql/init/init.sql @@ -3,7 +3,7 @@ CREATE DATABASE IF NOT EXISTS `modelcache`; USE `modelcache`; CREATE TABLE IF NOT EXISTS `modelcache_llm_answer` ( - `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT comment '主键', + `id` CHAR(36) comment '主键', `gmt_create` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP comment '创建时间', `gmt_modified` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP comment '修改时间', `question` text NOT NULL comment 'question', diff --git a/docker-compose.yaml b/docker-compose.yaml index 31ed1ab..06efd4c 100644 --- a/docker-compose.yaml +++ b/docker-compose.yaml @@ -1,7 +1,7 @@ name: "modelcache" services: mysql: - image: mysql:9.3.0 + image: mysql:8.0.23 container_name: mysql environment: MYSQL_ROOT_PASSWORD: 'root' diff --git a/modelcache/adapter/adapter_query.py b/modelcache/adapter/adapter_query.py index 5a91807..e9d595c 100644 --- a/modelcache/adapter/adapter_query.py +++ b/modelcache/adapter/adapter_query.py @@ -11,7 +11,7 @@ def adapt_query(cache_data_convert, *args, **kwargs): chat_cache = kwargs.pop("cache_obj", cache) - scope = kwargs.pop("scope", None) + scope = kwargs.pop("scope") model = scope['model'] if not chat_cache.has_init: raise NotInitError() diff --git a/modelcache/manager/scalar_data/sql_storage.py b/modelcache/manager/scalar_data/sql_storage.py index 52fd559..dee2a73 100644 --- a/modelcache/manager/scalar_data/sql_storage.py +++ b/modelcache/manager/scalar_data/sql_storage.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- import os import time +import uuid import pymysql import json @@ -42,26 +43,59 @@ def _insert(self, data: List): answer_type = 0 embedding_data = embedding_data.tobytes() is_deleted = 0 + _id = str(uuid.uuid4()) table_name = "modelcache_llm_answer" - insert_sql = "INSERT INTO {} (question, answer, answer_type, model, embedding_data, is_deleted) VALUES (%s, %s, %s, %s, _binary%s, %s)".format(table_name) + insert_sql = f""" + INSERT INTO {table_name} + (id, question, answer, answer_type, model, embedding_data, is_deleted) + VALUES (%s, %s, %s, %s, %s, _binary%s, %s) + """ conn = self.pool.connection() try: with conn.cursor() as cursor: # 执行插入数据操作 - values = (question, answer, answer_type, model, embedding_data, is_deleted) + values = (_id, question, answer, answer_type, model, embedding_data, is_deleted) cursor.execute(insert_sql, values) conn.commit() - id = cursor.lastrowid finally: # 关闭连接，将连接返回给连接池 conn.close() - return id + return _id - def batch_insert(self, all_data: List[CacheData]): + def batch_insert(self, all_data: List[List]): + table_name = "modelcache_llm_answer" + insert_sql = f""" + INSERT INTO {table_name} + (id, question, answer, answer_type, model, embedding_data, is_deleted) + VALUES (%s, %s, %s, %s, %s, %s, %s) + """ + + values_list = [] ids = [] + for data in all_data: - ids.append(self._insert(data)) + answer = data[0] + question = data[1] + embedding_data = data[2].tobytes() + model = data[3] + answer_type = 0 + is_deleted = 0 + _id = str(uuid.uuid4()) + ids.append(_id) + + values_list.append(( + _id, question, answer, answer_type, model, embedding_data, is_deleted + )) + + conn = self.pool.connection() + try: + with conn.cursor() as cursor: + cursor.executemany(insert_sql, values_list) + conn.commit() + finally: + conn.close() + return ids def insert_query_resp(self, query_resp, **kwargs): @@ -78,7 +112,11 @@ def insert_query_resp(self, query_resp, **kwargs): hit_query = json.dumps(hit_query, ensure_ascii=False) table_name = "modelcache_query_log" - insert_sql = "INSERT INTO {} (error_code, error_desc, cache_hit, model, query, delta_time, hit_query, answer) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)".format(table_name) + insert_sql = f""" + INSERT INTO {table_name} + (error_code, error_desc, cache_hit, model, query, delta_time, hit_query, answer) + VALUES (%s, %s, %s, %s, %s, %s, %s, %s) + """ conn = self.pool.connection() try: with conn.cursor() as cursor: @@ -92,15 +130,16 @@ def insert_query_resp(self, query_resp, **kwargs): def get_data_by_id(self, key: int): table_name = "modelcache_llm_answer" - query_sql = "select question, answer, embedding_data, model from {} where id={}".format(table_name, key) - conn_start = time.time() + query_sql = f""" + SELECT question, answer, embedding_data, model + FROM {table_name} + WHERE id = %s + """ conn = self.pool.connection() - - search_start = time.time() try: with conn.cursor() as cursor: # 执行数据库操作 - cursor.execute(query_sql) + cursor.execute(query_sql, (key,)) resp = cursor.fetchone() finally: # 关闭连接，将连接返回给连接池 @@ -113,14 +152,18 @@ def get_data_by_id(self, key: int): def update_hit_count_by_id(self, primary_id: int): table_name = "modelcache_llm_answer" - update_sql = "UPDATE {} SET hit_count = hit_count+1 WHERE id={}".format(table_name, primary_id) + update_sql = f""" + UPDATE {table_name} + SET hit_count = hit_count+1 + WHERE id = %s + """ conn = self.pool.connection() # 使用连接执行更新数据操作 try: with conn.cursor() as cursor: # 执行更新数据操作 - cursor.execute(update_sql) + cursor.execute(update_sql,(primary_id,)) conn.commit() finally: # 关闭连接，将连接返回给连接池 @@ -129,12 +172,16 @@ def update_hit_count_by_id(self, primary_id: int): def get_ids(self, deleted=True): table_name = "modelcache_llm_answer" state = 1 if deleted else 0 - query_sql = "Select id FROM {} WHERE is_deleted = {}".format(table_name, state) + query_sql = f""" + SELECT id + FROM {table_name} + WHERE is_deleted = %s + """ conn = self.pool.connection() try: with conn.cursor() as cursor: - cursor.execute(query_sql) + cursor.execute(query_sql, (state,)) ids = [row[0] for row in cursor.fetchall()] finally: conn.close() @@ -143,37 +190,45 @@ def get_ids(self, deleted=True): def mark_deleted(self, keys): table_name = "modelcache_llm_answer" - mark_sql = " update {} set is_deleted=1 WHERE id in ({})".format(table_name, ",".join([str(i) for i in keys])) + placeholders = ",".join(["%s"] * len(keys)) + mark_sql = f""" + UPDATE {table_name} + SET is_deleted=1 + WHERE id in ({placeholders}) + """ - # 从连接池中获取连接 conn = self.pool.connection() try: with conn.cursor() as cursor: - # 执行删除数据操作 - cursor.execute(mark_sql) + cursor.execute(mark_sql, keys) delete_count = cursor.rowcount conn.commit() finally: - # 关闭连接，将连接返回给连接池 conn.close() return delete_count def model_deleted(self, model_name): table_name = "modelcache_llm_answer" - delete_sql = "Delete from {} WHERE model='{}'".format(table_name, model_name) + delete_sql = f""" + Delete from {table_name} + WHERE model = %s + """ table_log_name = "modelcache_query_log" - delete_log_sql = "Delete from {} WHERE model='{}'".format(table_log_name, model_name) + delete_log_sql = f""" + Delete from {table_log_name} + WHERE model = %s + """ conn = self.pool.connection() # 使用连接执行删除数据操作 try: with conn.cursor() as cursor: # 执行删除数据操作 - resp = cursor.execute(delete_sql) + resp = cursor.execute(delete_sql, (model_name,)) conn.commit() # 执行删除该模型对应日志操作 resp_log行数不返回 - resp_log = cursor.execute(delete_log_sql) + resp_log = cursor.execute(delete_log_sql, (model_name,)) conn.commit() # 分别提交事务 finally: # 关闭连接，将连接返回给连接池 @@ -182,7 +237,10 @@ def model_deleted(self, model_name): def clear_deleted_data(self): table_name = "modelcache_llm_answer" - delete_sql = "DELETE FROM {} WHERE is_deleted = 1".format(table_name) + delete_sql = f""" + DELETE FROM {table_name} + WHERE is_deleted = 1 + """ conn = self.pool.connection() try: @@ -197,10 +255,15 @@ def clear_deleted_data(self): def count(self, state: int = 0, is_all: bool = False): table_name = "modelcache_llm_answer" + + # we're not using prepared statements here, so we need to ensure state is an integer + if not isinstance(state, int): + raise ValueError("'state' must be an integer.") + if is_all: - count_sql = "SELECT COUNT(*) FROM {}".format(table_name) + count_sql = f"SELECT COUNT(*) FROM {table_name}" else: - count_sql = "SELECT COUNT(*) FROM {} WHERE is_deleted = {}".format(table_name,state) + count_sql = f"SELECT COUNT(*) FROM {table_name} WHERE is_deleted = {state}" conn = self.pool.connection() try: diff --git a/modelcache/manager/vector_data/milvus.py b/modelcache/manager/vector_data/milvus.py index 50d6ab1..b16d884 100644 --- a/modelcache/manager/vector_data/milvus.py +++ b/modelcache/manager/vector_data/milvus.py @@ -66,6 +66,8 @@ def __init__( self.search_params = ( search_params or self.SEARCH_PARAM[self.index_params["index_type"]] ) + self.collections = dict() + def _connect(self, host, port, user, password, secure): try: @@ -87,12 +89,14 @@ def _connect(self, host, port, user, password, secure): timeout=10 ) + def _create_collection(self, collection_name): if not utility.has_collection(collection_name, using=self.alias): schema = [ FieldSchema( name="id", - dtype=DataType.INT64, + dtype=DataType.VARCHAR, + max_length=36, is_primary=True, auto_id=False, ), @@ -101,7 +105,8 @@ def _create_collection(self, collection_name): ), ] schema = CollectionSchema(schema) - self.col = Collection( + + new_collection = Collection( collection_name, schema=schema, consistency_level="Session", @@ -109,46 +114,48 @@ def _create_collection(self, collection_name): ) else: modelcache_log.warning("The %s collection already exists, and it will be used directly.", collection_name) - self.col = Collection( + new_collection = Collection( collection_name, consistency_level="Session", using=self.alias ) - if len(self.col.indexes) == 0: + self.collections[collection_name] = new_collection + + if len(new_collection.indexes) == 0: try: modelcache_log.info("Attempting creation of Milvus index.") - self.col.create_index("embedding", index_params=self.index_params) + new_collection.create_index("embedding", index_params=self.index_params) modelcache_log.info("Creation of Milvus index successful.") except MilvusException as e: modelcache_log.warning("Error with building index: %s, and attempting creation of default index.", e) i_p = {"metric_type": "L2", "index_type": "AUTOINDEX", "params": {}} - self.col.create_index("embedding", index_params=i_p) + new_collection.create_index("embedding", index_params=i_p) self.index_params = i_p else: - self.index_params = self.col.indexes[0].to_dict()["index_param"] + self.index_params = new_collection.indexes[0].to_dict()["index_param"] + + new_collection.load() - self.col.load() def _get_collection(self, collection_name): - self.col = Collection( - collection_name, consistency_level="Session", using=self.alias - ) - self.col.load() + if collection_name not in self.collections: + self._create_collection(collection_name) + return self.collections[collection_name] def mul_add(self, datas: List[VectorData], model=None): collection_name_model = self.collection_name + '_' + model - self._create_collection(collection_name_model) - + col = self._get_collection(collection_name_model) data_array, id_array = map(list, zip(*((data.data, data.id) for data in datas))) np_data = np.array(data_array).astype("float32") entities = [id_array, np_data] - self.col.insert(entities) + col.insert(entities) + def search(self, data: np.ndarray, top_k: int = -1, model=None): if top_k == -1: top_k = self.top_k collection_name_model = self.collection_name + '_' + model - self._create_collection(collection_name_model) - search_result = self.col.search( + col = self._get_collection(collection_name_model) + search_result = col.search( data=data.reshape(1, -1).tolist(), anns_field="embedding", param=self.search_params, @@ -156,12 +163,13 @@ def search(self, data: np.ndarray, top_k: int = -1, model=None): ) return list(zip(search_result[0].distances, search_result[0].ids)) + def delete(self, ids, model=None): collection_name_model = self.collection_name + '_' + model - self._get_collection(collection_name_model) + col = self._get_collection(collection_name_model) del_ids = ",".join([str(x) for x in ids]) - resp = self.col.delete(f"id in [{del_ids}]") + resp = col.delete(f"id in [{del_ids}]") delete_count = resp.delete_count return delete_count @@ -178,10 +186,12 @@ def rebuild_col(self, model): logging.info('create_collection: {}'.format(e)) def rebuild(self, ids=None): # pylint: disable=unused-argument - self.col.compact() + for col in self.collections.values(): + col.compact() def flush(self): - self.col.flush(_async=True) + for col in self.collections.values(): + col.flush(_async=True) def close(self): self.flush() diff --git a/reference_doc/create_table.sql b/reference_doc/create_table.sql index 1d0f30a..a358de2 100644 --- a/reference_doc/create_table.sql +++ b/reference_doc/create_table.sql @@ -1,5 +1,9 @@ -CREATE TABLE `modelcache_llm_answer` ( - `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT comment '主键', +CREATE DATABASE IF NOT EXISTS `modelcache`; + +USE `modelcache`; + +CREATE TABLE IF NOT EXISTS `modelcache_llm_answer` ( + `id` CHAR(36) comment '主键', `gmt_create` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP comment '创建时间', `gmt_modified` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP comment '修改时间', `question` text NOT NULL comment 'question', @@ -12,8 +16,7 @@ CREATE TABLE `modelcache_llm_answer` ( PRIMARY KEY(`id`) ) AUTO_INCREMENT = 1 DEFAULT CHARSET = utf8mb4 COMMENT = 'cache_codegpt_answer'; - -CREATE TABLE `modelcache_query_log` ( +CREATE TABLE IF NOT EXISTS `modelcache_query_log` ( `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT comment '主键', `gmt_create` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP comment '创建时间', `gmt_modified` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP comment '修改时间', From b474d156df1214ed8b090c7169f2210853044f89 Mon Sep 17 00:00:00 2001 From: Yuval Roth Date: Sun, 8 Jun 2025 00:21:55 +0300 Subject: [PATCH 25/36] Changed the embedding model to be mpnet-base-v2 added flags for its usage and updated milvus to use cosine instead of L2 for searching Co-authored-by: olgaoznovich Co-authored-by: Yuval-Roth --- flask4modelcache.py | 42 ++++++- model/download_bert_embedder.bat | 1 - modelcache/adapter/adapter_query.py | 145 ++++++++++++---------- modelcache/embedding/mpnet_base.py | 17 +++ modelcache/manager/vector_data/manager.py | 2 + requirements.txt | 3 +- 6 files changed, 137 insertions(+), 73 deletions(-) delete mode 100644 model/download_bert_embedder.bat create mode 100644 modelcache/embedding/mpnet_base.py diff --git a/flask4modelcache.py b/flask4modelcache.py index 823c66b..e30d3b3 100644 --- a/flask4modelcache.py +++ b/flask4modelcache.py @@ -6,6 +6,8 @@ import json from modelcache import cache from modelcache.adapter import adapter +from modelcache.embedding.mpnet_base import MPNet_Base +from modelcache.manager.vector_data import manager from modelcache.manager import CacheBase, VectorBase, get_data_manager, data_manager from modelcache.similarity_evaluation.distance import SearchDistanceEvaluation from modelcache.processor.pre import query_multi_splicing @@ -30,9 +32,17 @@ def save_query_info(result, model, query, delta_time_log): def response_hitquery(cache_resp): return cache_resp['hitQuery'] -data2vec = Data2VecAudio() -embedding_func = data2vec.to_embeddings -dimension = data2vec.dimension +manager.MPNet_base = True + +if manager.MPNet_base: + mpnet_base = MPNet_Base() + embedding_func = lambda x: mpnet_base.embedding_func(x) + dimension = mpnet_base.dimension + data_manager.NORMALIZE = False +else: + data2vec = Data2VecAudio() + embedding_func = data2vec.to_embeddings + dimension = data2vec.dimension mysql_config = configparser.ConfigParser() mysql_config.read('modelcache/config/mysql_config.ini') @@ -49,8 +59,30 @@ def response_hitquery(cache_resp): # chromadb_config = configparser.ConfigParser() # chromadb_config.read('modelcache/config/chromadb_config.ini') -data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), - VectorBase("milvus", dimension=dimension, milvus_config=milvus_config)) +data_manager = get_data_manager( + CacheBase("mysql", config=mysql_config), + VectorBase("milvus", + dimension=dimension, + milvus_config=milvus_config, + index_params={ + "metric_type": "COSINE", + "index_type": "HNSW", + "params": {"M": 16, "efConstruction": 64}, + } if manager.MPNet_base else None, + search_params={ + "IVF_FLAT": {"metric_type": "COSINE", "params": {"nprobe": 10}}, + "IVF_SQ8": {"metric_type": "COSINE", "params": {"nprobe": 10}}, + "IVF_PQ": {"metric_type": "COSINE", "params": {"nprobe": 10}}, + "HNSW": {"metric_type": "COSINE", "params": {"ef": 10}}, + "RHNSW_FLAT": {"metric_type": "COSINE", "params": {"ef": 10}}, + "RHNSW_SQ": {"metric_type": "COSINE", "params": {"ef": 10}}, + "RHNSW_PQ": {"metric_type": "COSINE", "params": {"ef": 10}}, + "IVF_HNSW": {"metric_type": "COSINE", "params": {"nprobe": 10, "ef": 10}}, + "ANNOY": {"metric_type": "COSINE", "params": {"search_k": 10}}, + "AUTOINDEX": {"metric_type": "COSINE", "params": {}}, + } if manager.MPNet_base else None + ) +) # data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), diff --git a/model/download_bert_embedder.bat b/model/download_bert_embedder.bat deleted file mode 100644 index e4c26af..0000000 --- a/model/download_bert_embedder.bat +++ /dev/null @@ -1 +0,0 @@ -curl -o embedder.tflite https://storage.googleapis.com/mediapipe-models/text_embedder/bert_embedder/float32/1/bert_embedder.tflite \ No newline at end of file diff --git a/modelcache/adapter/adapter_query.py b/modelcache/adapter/adapter_query.py index e9d595c..ae8e894 100644 --- a/modelcache/adapter/adapter_query.py +++ b/modelcache/adapter/adapter_query.py @@ -1,11 +1,13 @@ # -*- coding: utf-8 -*- import logging import time + from modelcache import cache from modelcache.utils.error import NotInitError from modelcache.utils.time import time_cal from modelcache.processor.pre import multi_analysis from FlagEmbedding import FlagReranker +from modelcache.manager.vector_data import manager USE_RERANKER = False # 如果为 True 则启用 reranker，否则使用原有逻辑 @@ -44,39 +46,47 @@ def adapt_query(cache_data_convert, *args, **kwargs): cache_answers = [] cache_questions = [] cache_ids = [] - similarity_threshold = chat_cache.config.similarity_threshold - similarity_threshold_long = chat_cache.config.similarity_threshold_long + cosine_similarity = cache_data_list[0][0] - min_rank, max_rank = chat_cache.similarity_evaluation.range() - rank_threshold = (max_rank - min_rank) * similarity_threshold * cache_factor - rank_threshold_long = (max_rank - min_rank) * similarity_threshold_long * cache_factor - rank_threshold = ( - max_rank - if rank_threshold > max_rank - else min_rank - if rank_threshold < min_rank - else rank_threshold - ) - rank_threshold_long = ( - max_rank - if rank_threshold_long > max_rank - else min_rank - if rank_threshold_long < min_rank - else rank_threshold_long - ) - if cache_data_list is None or len(cache_data_list) == 0: - rank_pre = -1.0 + if manager.MPNet_base: + # This code uses the built-in cosine similarity evaluation in milvus + if cosine_similarity < 0.9: + return None else: - cache_data_dict = {'search_result': cache_data_list[0]} - rank_pre = chat_cache.similarity_evaluation.evaluation( - None, - cache_data_dict, - extra_param=context.get("evaluation_func", None), + ## this is the code that uses L2 for similarity evaluation + similarity_threshold = chat_cache.config.similarity_threshold + similarity_threshold_long = chat_cache.config.similarity_threshold_long + + min_rank, max_rank = chat_cache.similarity_evaluation.range() + rank_threshold = (max_rank - min_rank) * similarity_threshold * cache_factor + rank_threshold_long = (max_rank - min_rank) * similarity_threshold_long * cache_factor + rank_threshold = ( + max_rank + if rank_threshold > max_rank + else min_rank + if rank_threshold < min_rank + else rank_threshold + ) + rank_threshold_long = ( + max_rank + if rank_threshold_long > max_rank + else min_rank + if rank_threshold_long < min_rank + else rank_threshold_long ) - if rank_pre < rank_threshold: - return None + if cache_data_list is None or len(cache_data_list) == 0: + rank_pre = -1.0 + else: + cache_data_dict = {'search_result': cache_data_list[0]} + rank_pre = chat_cache.similarity_evaluation.evaluation( + None, + cache_data_dict, + extra_param=context.get("evaluation_func", None), + ) + if rank_pre < rank_threshold: + return None - if USE_RERANKER: + if USE_RERANKER and not manager.MPNet_base: reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=False) for cache_data in cache_data_list: primary_id = cache_data[1] @@ -132,45 +142,50 @@ def adapt_query(cache_data_convert, *args, **kwargs): if ret is None: continue - if "deps" in context and hasattr(ret.question, "deps"): - eval_query_data = { - "question": context["deps"][0]["data"], - "embedding": None - } - eval_cache_data = { - "question": ret.question.deps[0].data, - "answer": ret.answers[0].answer, - "search_result": cache_data, - "embedding": None, - } + if manager.MPNet_base: + cache_answers.append((cosine_similarity, ret[1])) + cache_questions.append((cosine_similarity, ret[0])) + cache_ids.append((cosine_similarity, primary_id)) else: - eval_query_data = { - "question": pre_embedding_data, - "embedding": embedding_data, - } + if "deps" in context and hasattr(ret.question, "deps"): + eval_query_data = { + "question": context["deps"][0]["data"], + "embedding": None + } + eval_cache_data = { + "question": ret.question.deps[0].data, + "answer": ret.answers[0].answer, + "search_result": cache_data, + "embedding": None, + } + else: + eval_query_data = { + "question": pre_embedding_data, + "embedding": embedding_data, + } - eval_cache_data = { - "question": ret[0], - "answer": ret[1], - "search_result": cache_data, - "embedding": None - } - rank = chat_cache.similarity_evaluation.evaluation( - eval_query_data, - eval_cache_data, - extra_param=context.get("evaluation_func", None), - ) + eval_cache_data = { + "question": ret[0], + "answer": ret[1], + "search_result": cache_data, + "embedding": None + } + rank = chat_cache.similarity_evaluation.evaluation( + eval_query_data, + eval_cache_data, + extra_param=context.get("evaluation_func", None), + ) - if len(pre_embedding_data) <= 256: - if rank_threshold <= rank: - cache_answers.append((rank, ret[1])) - cache_questions.append((rank, ret[0])) - cache_ids.append((rank, primary_id)) - else: - if rank_threshold_long <= rank: - cache_answers.append((rank, ret[1])) - cache_questions.append((rank, ret[0])) - cache_ids.append((rank, primary_id)) + if len(pre_embedding_data) <= 256: + if rank_threshold <= rank: + cache_answers.append((rank, ret[1])) + cache_questions.append((rank, ret[0])) + cache_ids.append((rank, primary_id)) + else: + if rank_threshold_long <= rank: + cache_answers.append((rank, ret[1])) + cache_questions.append((rank, ret[0])) + cache_ids.append((rank, primary_id)) cache_answers = sorted(cache_answers, key=lambda x: x[0], reverse=True) cache_questions = sorted(cache_questions, key=lambda x: x[0], reverse=True) diff --git a/modelcache/embedding/mpnet_base.py b/modelcache/embedding/mpnet_base.py new file mode 100644 index 0000000..5c1b0c4 --- /dev/null +++ b/modelcache/embedding/mpnet_base.py @@ -0,0 +1,17 @@ +from sentence_transformers import SentenceTransformer + +class MPNet_Base: + def __init__(self): + self.dimension = 768 + self.model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2') + + def embedding_func(self, *args, **kwargs): + if not args: + raise ValueError("No word provided for embedding.") + embeddings = self.model.encode(args) + return embeddings[0] if len(args) == 1 else embeddings + + def similarity(self, a, b): + if not a or not b: + raise ValueError("Both inputs must be non-empty for similarity calculation.") + return self.model.similarity(a, b) diff --git a/modelcache/manager/vector_data/manager.py b/modelcache/manager/vector_data/manager.py index 0fc5334..2167fbc 100644 --- a/modelcache/manager/vector_data/manager.py +++ b/modelcache/manager/vector_data/manager.py @@ -17,6 +17,8 @@ COLLECTION_NAME = "modelcache" +MPNet_base = False # whether to use MPNet base model for embedding, if True, will use cosine similarity evaluation in milvus + class VectorBase: """ diff --git a/requirements.txt b/requirements.txt index 44d332d..a84a988 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,5 +19,4 @@ elasticsearch==7.10.0 snowflake-id==1.0.2 flagembedding==1.3.4 cryptography==45.0.2 -mediapipe==0.10.21 -protobuf==4.25.8 +sentence-transformers==4.1.0 From 81b471573cc69249e7183c67bf246618a0c7415d Mon Sep 17 00:00:00 2001 From: Yuval Roth Date: Sun, 8 Jun 2025 14:53:05 +0300 Subject: [PATCH 26/36] Added custom pre embedding func and some other improvements Co-authored-by: olgaoznovich Co-authored-by: Yuval-Roth --- flask4modelcache.py | 13 ++++++++----- modelcache/embedding/mpnet_base.py | 2 +- modelcache/processor/pre.py | 7 ++++++- 3 files changed, 15 insertions(+), 7 deletions(-) diff --git a/flask4modelcache.py b/flask4modelcache.py index e30d3b3..ff56157 100644 --- a/flask4modelcache.py +++ b/flask4modelcache.py @@ -10,8 +10,7 @@ from modelcache.manager.vector_data import manager from modelcache.manager import CacheBase, VectorBase, get_data_manager, data_manager from modelcache.similarity_evaluation.distance import SearchDistanceEvaluation -from modelcache.processor.pre import query_multi_splicing -from modelcache.processor.pre import insert_multi_splicing +from modelcache.processor.pre import query_multi_splicing,insert_multi_splicing, query_with_role from concurrent.futures import ThreadPoolExecutor from modelcache.utils.model_filter import model_blacklist_filter from modelcache.embedding import Data2VecAudio @@ -36,13 +35,17 @@ def response_hitquery(cache_resp): if manager.MPNet_base: mpnet_base = MPNet_Base() - embedding_func = lambda x: mpnet_base.embedding_func(x) + embedding_func = mpnet_base.to_embeddings dimension = mpnet_base.dimension data_manager.NORMALIZE = False + query_pre_embedding_func=query_with_role + insert_pre_embedding_func=query_with_role else: data2vec = Data2VecAudio() embedding_func = data2vec.to_embeddings dimension = data2vec.dimension + query_pre_embedding_func=query_multi_splicing + insert_pre_embedding_func=insert_multi_splicing mysql_config = configparser.ConfigParser() mysql_config.read('modelcache/config/mysql_config.ini') @@ -95,8 +98,8 @@ def response_hitquery(cache_resp): embedding_func=embedding_func, data_manager=data_manager, similarity_evaluation=SearchDistanceEvaluation(), - query_pre_embedding_func=query_multi_splicing, - insert_pre_embedding_func=insert_multi_splicing, + query_pre_embedding_func=query_pre_embedding_func, + insert_pre_embedding_func=insert_pre_embedding_func, ) global executor diff --git a/modelcache/embedding/mpnet_base.py b/modelcache/embedding/mpnet_base.py index 5c1b0c4..a106948 100644 --- a/modelcache/embedding/mpnet_base.py +++ b/modelcache/embedding/mpnet_base.py @@ -5,7 +5,7 @@ def __init__(self): self.dimension = 768 self.model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2') - def embedding_func(self, *args, **kwargs): + def to_embeddings(self, *args, **kwargs): if not args: raise ValueError("No word provided for embedding.") embeddings = self.model.encode(args) diff --git a/modelcache/processor/pre.py b/modelcache/processor/pre.py index 0c72d40..6a9d0f6 100644 --- a/modelcache/processor/pre.py +++ b/modelcache/processor/pre.py @@ -4,7 +4,7 @@ def insert_last_content(data: Dict[str, Any], **_: Dict[str, Any]) -> Any: - return data.get("chat_info")[-1]["query"] + return data.get("query")[-1]["content"] def query_last_content(data: Dict[str, Any], **_: Dict[str, Any]) -> Any: @@ -67,6 +67,11 @@ def insert_multi_splicing(data: Dict[str, Any], **_: Dict[str, Any]) -> Any: insert_query_list = data['query'] return multi_splicing(insert_query_list) +def query_with_role(data: Dict[str, Any], **_: Dict[str, Any]) -> Any: + query = data["query"][-1] + content = query["content"] + role = query["role"] + return role+": "+content def multi_splicing(data_list) -> Any: result_str = "" From bd2caeb389fd5321f0b44bece2304591d9fa3fc9 Mon Sep 17 00:00:00 2001 From: adiaybgu Date: Tue, 10 Jun 2025 07:11:17 +0300 Subject: [PATCH 27/36] Added feature: Eviction logic and new memory cache types. connected the memory caching logic to the main logic because it was not connected up until now Co-authored-by: omerdor001 Co-authored-by: adiaybgu --- flask4modelcache.py | 4 +- modelcache/adapter/adapter.py | 1 + modelcache/adapter/adapter_query.py | 24 ++-- modelcache/embedding/mpnet_base.py | 2 +- modelcache/manager/data_manager.py | 69 +++++++-- modelcache/manager/eviction/arc_cache.py | 131 ++++++++++++++++++ modelcache/manager/eviction/base.py | 4 +- modelcache/manager/eviction/memory_cache.py | 52 +++++-- modelcache/manager/eviction/wtinylfu_cache.py | 129 +++++++++++++++++ modelcache/manager/factory.py | 6 +- modelcache/manager/scalar_data/base.py | 5 + modelcache/manager/scalar_data/sql_storage.py | 2 +- modelcache/manager/vector_data/milvus.py | 2 +- 13 files changed, 385 insertions(+), 46 deletions(-) create mode 100644 modelcache/manager/eviction/arc_cache.py create mode 100644 modelcache/manager/eviction/wtinylfu_cache.py diff --git a/flask4modelcache.py b/flask4modelcache.py index ff56157..d793953 100644 --- a/flask4modelcache.py +++ b/flask4modelcache.py @@ -84,7 +84,9 @@ def response_hitquery(cache_resp): "ANNOY": {"metric_type": "COSINE", "params": {"search_k": 10}}, "AUTOINDEX": {"metric_type": "COSINE", "params": {}}, } if manager.MPNet_base else None - ) + ), + eviction='WTINYLFU', + max_size=100000 ) diff --git a/modelcache/adapter/adapter.py b/modelcache/adapter/adapter.py index 452be7b..ddad9af 100644 --- a/modelcache/adapter/adapter.py +++ b/modelcache/adapter/adapter.py @@ -30,6 +30,7 @@ def create_insert(cls, *args, **kwargs): **kwargs ) except Exception as e: + print(e) return str(e) @classmethod diff --git a/modelcache/adapter/adapter_query.py b/modelcache/adapter/adapter_query.py index ae8e894..aa42eae 100644 --- a/modelcache/adapter/adapter_query.py +++ b/modelcache/adapter/adapter_query.py @@ -91,7 +91,7 @@ def adapt_query(cache_data_convert, *args, **kwargs): for cache_data in cache_data_list: primary_id = cache_data[1] ret = chat_cache.data_manager.get_scalar_data( - cache_data, extra_param=context.get("get_scalar_data", None) + cache_data, extra_param=context.get("get_scalar_data", None),model=model ) if ret is None: continue @@ -124,27 +124,27 @@ def adapt_query(cache_data_convert, *args, **kwargs): if len(pre_embedding_data) <= 256: if rank_threshold <= rank: - cache_answers.append((rank, ret[1])) - cache_questions.append((rank, ret[0])) + cache_answers.append((rank, ret[0])) + cache_questions.append((rank, ret[1])) cache_ids.append((rank, primary_id)) else: if rank_threshold_long <= rank: - cache_answers.append((rank, ret[1])) - cache_questions.append((rank, ret[0])) + cache_answers.append((rank, ret[0])) + cache_questions.append((rank, ret[1])) cache_ids.append((rank, primary_id)) else: # 不使用 reranker 时，走原来的逻辑 for cache_data in cache_data_list: primary_id = cache_data[1] ret = chat_cache.data_manager.get_scalar_data( - cache_data, extra_param=context.get("get_scalar_data", None) + cache_data, extra_param=context.get("get_scalar_data", None),model=model ) if ret is None: continue if manager.MPNet_base: - cache_answers.append((cosine_similarity, ret[1])) - cache_questions.append((cosine_similarity, ret[0])) + cache_answers.append((cosine_similarity, ret[0])) + cache_questions.append((cosine_similarity, ret[1])) cache_ids.append((cosine_similarity, primary_id)) else: if "deps" in context and hasattr(ret.question, "deps"): @@ -178,13 +178,13 @@ def adapt_query(cache_data_convert, *args, **kwargs): if len(pre_embedding_data) <= 256: if rank_threshold <= rank: - cache_answers.append((rank, ret[1])) - cache_questions.append((rank, ret[0])) + cache_answers.append((rank, ret[0])) + cache_questions.append((rank, ret[1])) cache_ids.append((rank, primary_id)) else: if rank_threshold_long <= rank: - cache_answers.append((rank, ret[1])) - cache_questions.append((rank, ret[0])) + cache_answers.append((rank, ret[0])) + cache_questions.append((rank, ret[1])) cache_ids.append((rank, primary_id)) cache_answers = sorted(cache_answers, key=lambda x: x[0], reverse=True) diff --git a/modelcache/embedding/mpnet_base.py b/modelcache/embedding/mpnet_base.py index a106948..b272c5c 100644 --- a/modelcache/embedding/mpnet_base.py +++ b/modelcache/embedding/mpnet_base.py @@ -7,7 +7,7 @@ def __init__(self): def to_embeddings(self, *args, **kwargs): if not args: - raise ValueError("No word provided for embedding.") + raise ValueError("No data provided for embedding.") embeddings = self.model.encode(args) return embeddings[0] if len(args) == 1 else embeddings diff --git a/modelcache/manager/data_manager.py b/modelcache/manager/data_manager.py index ce9ab0c..3b20fd3 100644 --- a/modelcache/manager/data_manager.py +++ b/modelcache/manager/data_manager.py @@ -22,6 +22,7 @@ from modelcache.manager.object_data.base import ObjectBase from modelcache.manager.eviction import EvictionBase from modelcache.manager.eviction_manager import EvictionManager +from modelcache.manager.eviction.memory_cache import MemoryCacheEviction from modelcache.utils.log import modelcache_log NORMALIZE = True @@ -38,9 +39,7 @@ def save_query_resp(self, query_resp_dict, **kwargs): pass @abstractmethod - def import_data( - self, questions: List[Any], answers: List[Any], embedding_datas: List[Any], model:Any - ): + def import_data(self, questions: List[Any], answers: List[Any], embedding_datas: List[Any], model:Any): pass @abstractmethod @@ -162,10 +161,18 @@ def __init__( self.v = v self.o = o + # added + self.eviction_base = MemoryCacheEviction( + policy=policy, + maxsize=max_size, + clean_size=clean_size, + on_evict=self._evict_ids) + def save(self, questions: List[any], answers: List[any], embedding_datas: List[any], **kwargs): model = kwargs.pop("model", None) self.import_data(questions, answers, embedding_datas, model) + def save_query_resp(self, query_resp_dict, **kwargs): save_query_start_time = time.time() self.s.insert_query_resp(query_resp_dict, **kwargs) @@ -217,14 +224,20 @@ def import_data( cache_datas.append([ans, question, embedding_data, model]) ids = self.s.batch_insert(cache_datas) - datas_ = [VectorData(id=ids[i], data=embedding_data.astype("float32")) for i, embedding_data in enumerate(embedding_datas)] - self.v.mul_add( - datas_, - model - - ) + datas = [] + for i, embedding_data in enumerate(embedding_datas): + _id = ids[i] + datas.append(VectorData(id=_id, data=embedding_data.astype("float32"))) + self.eviction_base.put([(_id, cache_datas[i])],model=model) + self.v.mul_add(datas,model) def get_scalar_data(self, res_data, **kwargs) -> Optional[CacheData]: + model = kwargs.pop("model") + #Get Data from RAM Cache + _id = res_data[1] + cache_hit = self.eviction_base.get(_id, model=model) + if cache_hit is not None: + return cache_hit cache_data = self.s.get_data_by_id(res_data[1]) if cache_data is None: return None @@ -244,8 +257,10 @@ def search(self, embedding_data, **kwargs): return self.v.search(data=embedding_data, top_k=top_k, model=model) def delete(self, id_list, **kwargs): - model = kwargs.pop("model", None) + model = kwargs.pop("model") try: + for id in id_list: + self.eviction_base.get_cache(model).pop(id, None) # Remove from in-memory LRU too v_delete_count = self.v.delete(ids=id_list, model=model) except Exception as e: return {'status': 'failed', 'milvus': 'delete milvus data failed, please check! e: {}'.format(e), @@ -262,10 +277,13 @@ def delete(self, id_list, **kwargs): def create_index(self, model, **kwargs): return self.v.create(model) - def truncate(self, model_name): + def truncate(self, model): + # drop memory cache data + self.eviction_base.clear(model) + # drop vector base data try: - vector_resp = self.v.rebuild_col(model_name) + vector_resp = self.v.rebuild_col(model) except Exception as e: return {'status': 'failed', 'VectorDB': 'truncate VectorDB data failed, please check! e: {}'.format(e), 'ScalarDB': 'unexecuted'} @@ -273,12 +291,37 @@ def truncate(self, model_name): return {'status': 'failed', 'VectorDB': vector_resp, 'ScalarDB': 'unexecuted'} # drop scalar base data try: - delete_count = self.s.model_deleted(model_name) + delete_count = self.s.model_deleted(model) except Exception as e: return {'status': 'failed', 'VectorDB': 'rebuild', 'ScalarDB': 'truncate scalar data failed, please check! e: {}'.format(e)} return {'status': 'success', 'VectorDB': 'rebuild', 'ScalarDB': 'delete_count: ' + str(delete_count)} + # added + def _evict_ids(self, ids, **kwargs): + model = kwargs.get("model") + if not ids or any(i is None for i in ids): + modelcache_log.warning("Skipping eviction for invalid IDs: %s", ids) + return + + if isinstance(ids,str): + ids = [ids] + + for _id in ids: + self.eviction_base.get_cache(model).pop(_id, None) + + try: + self.s.mark_deleted(ids) + modelcache_log.info("Evicted from scalar storage: %s", ids) + except Exception as e: + modelcache_log.error("Failed to delete from scalar storage: %s", str(e)) + + try: + self.v.delete(ids, model=model) + modelcache_log.info("Evicted from vector storage (model=%s): %s", model, ids) + except Exception as e: + modelcache_log.error("Failed to delete from vector storage (model=%s): %s", model, str(e)) + def flush(self): self.s.flush() self.v.flush() diff --git a/modelcache/manager/eviction/arc_cache.py b/modelcache/manager/eviction/arc_cache.py new file mode 100644 index 0000000..509fae0 --- /dev/null +++ b/modelcache/manager/eviction/arc_cache.py @@ -0,0 +1,131 @@ +from cachetools import Cache +from collections import OrderedDict + +class ARC(Cache): + """ + Adaptive Replacement Cache (ARC) implementation with on_evict callback. + Balances recency and frequency via two active lists (T1, T2) and two ghost lists (B1, B2). + Calls on_evict([key]) whenever an item is evicted from the active cache. + """ + + def __init__(self, maxsize, getsizeof=None, on_evict=None): + """ + Args: + maxsize (int): Maximum cache size. + getsizeof (callable, optional): Sizing function for items. + on_evict (callable, optional): Callback called as on_evict([key]) when a key is evicted. + """ + super().__init__(maxsize, getsizeof) + self.t1 = OrderedDict() + self.t2 = OrderedDict() + self.b1 = OrderedDict() + self.b2 = OrderedDict() + self.p = 0 # Adaptive target for T1 size. + self.on_evict = on_evict + + def __len__(self): + return len(self.t1) + len(self.t2) + + def __contains__(self, key): + return key in self.t1 or key in self.t2 + + def _evict_internal(self): + """ + Evicts items from T1 or T2 if cache is over capacity, and prunes ghost lists. + Calls on_evict for each evicted key. + """ + # Evict from T1 or T2 if active cache > maxsize + while len(self.t1) + len(self.t2) > self.maxsize: + if len(self.t1) > self.p or (len(self.t1) == 0 and len(self.t2) > 0): + key, value = self.t1.popitem(last=False) + self.b1[key] = value + if self.on_evict: + self.on_evict([key]) + else: + key, value = self.t2.popitem(last=False) + self.b2[key] = value + if self.on_evict: + self.on_evict([key]) + # Prune ghost lists to their max lengths + while len(self.b1) > (self.maxsize - self.p): + self.b1.popitem(last=False) + while len(self.b2) > self.p: + self.b2.popitem(last=False) + + def __setitem__(self, key, value): + # Remove from all lists before re-inserting + for l in (self.t1, self.t2, self.b1, self.b2): + l.pop(key, None) + self.t1[key] = value + self.t1.move_to_end(key) + self._evict_internal() + + def __getitem__(self, key): + # Case 1: Hit in T1 → promote to T2 + if key in self.t1: + value = self.t1.pop(key) + self.t2[key] = value + self.t2.move_to_end(key) + self.p = max(0, self.p - 1) + self._evict_internal() + return value + # Case 2: Hit in T2 → refresh in T2 + if key in self.t2: + value = self.t2.pop(key) + self.t2[key] = value + self.t2.move_to_end(key) + self.p = min(self.maxsize, self.p + 1) + self._evict_internal() + return value + # Case 3: Hit in B1 (ghost) → fetch and promote to T2 + if key in self.b1: + self.b1.pop(key) + self.p = min(self.maxsize, self.p + 1) + self._evict_internal() + value = super().__missing__(key) + self.t2[key] = value + self.t2.move_to_end(key) + return value + # Case 4: Hit in B2 (ghost) → fetch and promote to T2 + if key in self.b2: + self.b2.pop(key) + self.p = max(0, self.p - 1) + self._evict_internal() + value = super().__missing__(key) + self.t2[key] = value + self.t2.move_to_end(key) + return value + # Case 5: Cold miss → handled by Cache base class (calls __setitem__ after __missing__) + return super().__getitem__(key) + + def __missing__(self, key): + """ + Override this in a subclass, or rely on direct assignment (cache[key] = value). + """ + raise KeyError(key) + + def pop(self, key, default=None): + """ + Remove key from all lists. + """ + for l in (self.t1, self.t2, self.b1, self.b2): + if key in l: + return l.pop(key) + return default + + def clear(self): + self.t1.clear() + self.t2.clear() + self.b1.clear() + self.b2.clear() + self.p = 0 + super().clear() + + def __iter__(self): + yield from self.t1 + yield from self.t2 + + def __repr__(self): + return (f"ARC(maxsize={self.maxsize}, p={self.p}, len={len(self)}, " + f"t1_len={len(self.t1)}, t2_len={len(self.t2)}, " + f"b1_len={len(self.b1)}, b2_len={len(self.b2)})") diff --git a/modelcache/manager/eviction/base.py b/modelcache/manager/eviction/base.py index 352c9a3..56efa54 100644 --- a/modelcache/manager/eviction/base.py +++ b/modelcache/manager/eviction/base.py @@ -9,11 +9,11 @@ class EvictionBase(metaclass=ABCMeta): """ @abstractmethod - def put(self, objs: List[Any]): + def put(self, objs: List[Any], model:str): pass @abstractmethod - def get(self, obj: Any): + def get(self, obj: Any, model:str): pass @property diff --git a/modelcache/manager/eviction/memory_cache.py b/modelcache/manager/eviction/memory_cache.py index 3cb487f..c6272f6 100644 --- a/modelcache/manager/eviction/memory_cache.py +++ b/modelcache/manager/eviction/memory_cache.py @@ -1,8 +1,10 @@ # -*- coding: utf-8 -*- -from typing import Any, Callable, List +from typing import Any, Callable, List, Tuple import cachetools from modelcache.manager.eviction.base import EvictionBase +from .arc_cache import ARC +from .wtinylfu_cache import W2TinyLFU def popitem_wrapper(func, wrapper_func, clean_size): @@ -19,25 +21,51 @@ def wrapper(*args, **kwargs): class MemoryCacheEviction(EvictionBase): def __init__(self, policy: str, maxsize: int, clean_size: int, on_evict: Callable[[List[Any]], None], **kwargs): self._policy = policy.upper() + self.model_to_cache = dict() + self.maxsize = maxsize + self.clean_size = clean_size + self.on_evict = on_evict + self.kwargs = kwargs + + def create_cache(self, model: str): if self._policy == "LRU": - self._cache = cachetools.LRUCache(maxsize=maxsize, **kwargs) + cache = cachetools.LRUCache(maxsize=self.maxsize, **self.kwargs) elif self._policy == "LFU": - self._cache = cachetools.LFUCache(maxsize=maxsize, **kwargs) + cache = cachetools.LFUCache(maxsize=self.maxsize, **self.kwargs) elif self._policy == "FIFO": - self._cache = cachetools.FIFOCache(maxsize=maxsize, **kwargs) + cache = cachetools.FIFOCache(maxsize=self.maxsize, **self.kwargs) elif self._policy == "RR": - self._cache = cachetools.RRCache(maxsize=maxsize, **kwargs) + cache = cachetools.RRCache(maxsize=self.maxsize, **self.kwargs) + elif self._policy == "WTINYLFU": + cache = W2TinyLFU(maxsize=self.maxsize, on_evict=lambda x: self.on_evict(x,model=model)) + elif self._policy == "ARC": + cache = ARC(maxsize=self.maxsize, on_evict=lambda x: self.on_evict(x,model=model)) else: - raise ValueError(f"Unknown policy {policy}") + raise ValueError(f"Unknown policy {self.policy}") + cache.popitem = popitem_wrapper(cache.popitem, self.on_evict, self.clean_size) + return cache + + + def put(self, objs: List[Tuple[Any, Any]], model: str): + cache = self.get_cache(model) + for key, value in objs: + cache[key] = value + + + def get(self, obj: Any, model: str): + cache = self.get_cache(model) + return cache.get(obj) + + + def clear(self, model: str): + self.model_to_cache.pop(model, None) - self._cache.popitem = popitem_wrapper(self._cache.popitem, on_evict, clean_size) - def put(self, objs: List[Any]): - for obj in objs: - self._cache[obj] = True + def get_cache(self, model: str): + if not model in self.model_to_cache: + self.model_to_cache[model] = self.create_cache(model) + return self.model_to_cache[model] - def get(self, obj: Any): - return self._cache.get(obj) @property def policy(self) -> str: diff --git a/modelcache/manager/eviction/wtinylfu_cache.py b/modelcache/manager/eviction/wtinylfu_cache.py new file mode 100644 index 0000000..d7f4272 --- /dev/null +++ b/modelcache/manager/eviction/wtinylfu_cache.py @@ -0,0 +1,129 @@ +from cachetools import LRUCache, Cache +import random +from typing import Any, Callable + +class CountMinSketch: + def __init__(self, width=1024, depth=4, decay_interval=10000): + self.width = width + self.depth = depth + self.tables = [[0]*width for _ in range(depth)] + self.seeds = [random.randrange(1<<30) for _ in range(depth)] + self.ops = 0 + self.decay_interval = decay_interval + + def _hash(self, x, seed): + return hash((x, seed)) % self.width + + def add(self, x): + self.ops += 1 + # minimal increment + est = self.estimate(x) + for i, seed in enumerate(self.seeds): + idx = self._hash(x, seed) + if self.tables[i][idx] <= est: + self.tables[i][idx] += 1 + if self.ops >= self.decay_interval: + self.decay() + self.ops = 0 + + def estimate(self, x): + return min(self.tables[i][self._hash(x, seed)] + for i, seed in enumerate(self.seeds)) + + def decay(self): + for table in self.tables: + for i in range(len(table)): + table[i] >>= 1 + + +class W2TinyLFU(Cache): + def __init__(self, maxsize, window_pct=1, on_evict: Callable[[Any], None]=None): + super().__init__(maxsize) + self.window_size = max(1, int(maxsize * window_pct / 100)) + rest = maxsize - self.window_size + self.probation_size = rest // 2 + self.protected_size = rest - self.probation_size + + self.window = LRUCache(maxsize=self.window_size) + self.probation = LRUCache(maxsize=self.probation_size) + self.protected = LRUCache(maxsize=self.protected_size) + + self.cms = CountMinSketch() + self.on_evict = on_evict + self.data = {} + + def __setitem__(self, key, value): + self.data[key] = value + self._put(key) + + def __getitem__(self, key): + val = self.get(key, default=None) + if val is None: + raise KeyError(key) + return val + + def __contains__(self, key): + return key in self.window or key in self.probation or key in self.protected + + def __delitem__(self, key): + self.data.pop(key, None) + self.window.pop(key, None) + self.probation.pop(key, None) + self.protected.pop(key, None) + + def get(self, key, default=None): + if key in self.window: + self.window[key] = True + return self.data.get(key, default) + if key in self.protected: + self.protected[key] = True + return self.data.get(key, default) + if key in self.probation: + self.probation.pop(key) + # demote LRU from protected if full + if len(self.protected) >= self.protected_size: + demoted = next(iter(self.protected)) + self.protected.pop(demoted) + self.probation[demoted] = True + self.protected[key] = True + return self.data.get(key, default) + return default + + def _put(self, key): + self.cms.add(key) + if key in self: + return + + # admission to window + if len(self.window) < self.window_size: + self.window[key] = True + return + + # window full: victim is LRU + victim = next(iter(self.window)) + self.window.pop(victim) + + if self.cms.estimate(key) >= self.cms.estimate(victim): + self._admit_to_main(victim) + self._admit_to_main(key) + else: + # victim stronger or equal: victim enters main, key is dropped + self._admit_to_main(victim) + # actually evicts key entirely + if self.on_evict: + self.on_evict(key) + self.data.pop(key, None) + + def _admit_to_main(self, key): + if key in self.protected or key in self.probation: + return + if len(self.probation) < self.probation_size: + self.probation[key] = True + else: + evicted = next(iter(self.probation)) + self.probation.pop(evicted) + self.probation[key] = True + # this eviction removes it entirely + if self.on_evict: + self.on_evict(evicted) + self.data.pop(evicted, None) diff --git a/modelcache/manager/factory.py b/modelcache/manager/factory.py index 7246cc6..b6d5b08 100644 --- a/modelcache/manager/factory.py +++ b/modelcache/manager/factory.py @@ -8,9 +8,9 @@ def get_data_manager( cache_base: Union[CacheBase, str] = None, vector_base: Union[VectorBase, str] = None, object_base: Union[ObjectBase, str] = None, - max_size: int = 1000, - clean_size: int = None, - eviction: str = "LRU", + max_size: int = 3, + clean_size: int = 1, + eviction: str = "ARC", data_path: str = "data_map.txt", get_data_container: Callable = None, ): diff --git a/modelcache/manager/scalar_data/base.py b/modelcache/manager/scalar_data/base.py index 6db058d..607db95 100644 --- a/modelcache/manager/scalar_data/base.py +++ b/modelcache/manager/scalar_data/base.py @@ -128,3 +128,8 @@ def flush(self): @abstractmethod def close(self): pass + + @abstractmethod + def batch_insert(self, all_data: List[CacheData]): + pass + diff --git a/modelcache/manager/scalar_data/sql_storage.py b/modelcache/manager/scalar_data/sql_storage.py index dee2a73..c339285 100644 --- a/modelcache/manager/scalar_data/sql_storage.py +++ b/modelcache/manager/scalar_data/sql_storage.py @@ -131,7 +131,7 @@ def insert_query_resp(self, query_resp, **kwargs): def get_data_by_id(self, key: int): table_name = "modelcache_llm_answer" query_sql = f""" - SELECT question, answer, embedding_data, model + SELECT answer, question, embedding_data, model FROM {table_name} WHERE id = %s """ diff --git a/modelcache/manager/vector_data/milvus.py b/modelcache/manager/vector_data/milvus.py index b16d884..1d04430 100644 --- a/modelcache/manager/vector_data/milvus.py +++ b/modelcache/manager/vector_data/milvus.py @@ -168,7 +168,7 @@ def delete(self, ids, model=None): collection_name_model = self.collection_name + '_' + model col = self._get_collection(collection_name_model) - del_ids = ",".join([str(x) for x in ids]) + del_ids = ",".join([f'"{x}"' for x in ids]) resp = col.delete(f"id in [{del_ids}]") delete_count = resp.delete_count return delete_count From f8d4e72d8d9ad179ec2b0e0f143789d991a4837f Mon Sep 17 00:00:00 2001 From: Yuval Roth Date: Tue, 10 Jun 2025 01:31:11 +0300 Subject: [PATCH 28/36] Project-wide refactoring, created a single point of init and entry to caching logic * Separated API logic from cache logic and init logic and by that deduplicated all the cache init code and the parsing and handling logic that was inside the files 'fastpi4modelcache.py', 'fastpi4modelcache_demo.py', 'flask4modelcache.py' and 'flask4modelcache_demo.py' moved all that logic into Cache class. * The cache init code is now modular and extendable and is contained inside the static Cache.init() function. Added EmbeddingModel and MetricType enums that control which logic runs in the adapters and is configured in the init code * All the cache configuration is now held inside the Cache object instead of being spread around the entire project * Deduplicated classes, interfaces and factory methods. Removed some unused dead code. * Moved factory methods into their respective Interface instead of being static global functions with arbitrary names --- fastapi4modelcache.py | 144 +------- fastapi4modelcache_demo.py | 121 +------ flask4modelcache.py | 217 +----------- flask4modelcache_demo.py | 151 +-------- modelcache/__init__.py | 10 +- modelcache/adapter/adapter.py | 4 +- modelcache/adapter/adapter_insert.py | 9 +- modelcache/adapter/adapter_query.py | 299 ++++++++--------- modelcache/adapter/adapter_register.py | 3 +- modelcache/adapter/adapter_remove.py | 5 +- modelcache/cache.py | 311 ++++++++++++++++++ modelcache/config.py | 22 -- modelcache/core.py | 76 ----- modelcache/embedding/__init__.py | 40 +-- modelcache/embedding/base.py | 85 ++++- modelcache/embedding/huggingface.py | 48 +-- modelcache/embedding/mpnet_base.py | 17 - modelcache/embedding/string_text.py | 5 - modelcache/manager/__init__.py | 4 - modelcache/manager/data_manager.py | 58 ++-- modelcache/manager/eviction/__init__.py | 8 - modelcache/manager/eviction/manager.py | 25 -- modelcache/manager/eviction/memory_cache.py | 4 + modelcache/manager/factory.py | 27 -- modelcache/manager/object_data/__init__.py | 7 - modelcache/manager/object_data/base.py | 8 +- modelcache/manager/scalar_data/__init__.py | 8 - modelcache/manager/scalar_data/base.py | 30 +- modelcache/manager/scalar_data/manager.py | 36 -- modelcache/manager/scalar_data/sql_storage.py | 4 + .../manager/scalar_data/sql_storage_es.py | 4 + .../manager/scalar_data/sql_storage_sqlite.py | 3 + modelcache/manager/vector_data/__init__.py | 9 - modelcache/manager/vector_data/base.py | 115 ++++++- modelcache/manager/vector_data/chroma.py | 4 +- modelcache/manager/vector_data/faiss.py | 4 +- modelcache/manager/vector_data/manager.py | 127 ------- modelcache/manager/vector_data/milvus.py | 42 +-- modelcache/manager/vector_data/redis.py | 4 +- modelcache/similarity_evaluation/__init__.py | 9 - .../{similarity_evaluation.py => base.py} | 0 modelcache/similarity_evaluation/distance.py | 3 +- .../similarity_evaluation/exact_match.py | 2 +- modelcache/utils/cache_func.py | 3 - modelcache/utils/time.py | 9 +- 45 files changed, 831 insertions(+), 1293 deletions(-) create mode 100644 modelcache/cache.py delete mode 100644 modelcache/config.py delete mode 100644 modelcache/core.py delete mode 100644 modelcache/embedding/mpnet_base.py delete mode 100644 modelcache/embedding/string_text.py delete mode 100644 modelcache/manager/eviction/manager.py delete mode 100644 modelcache/manager/factory.py delete mode 100644 modelcache/manager/scalar_data/manager.py delete mode 100644 modelcache/manager/vector_data/manager.py rename modelcache/similarity_evaluation/{similarity_evaluation.py => base.py} (100%) delete mode 100644 modelcache/utils/cache_func.py diff --git a/fastapi4modelcache.py b/fastapi4modelcache.py index f9d0267..d3d39bf 100644 --- a/fastapi4modelcache.py +++ b/fastapi4modelcache.py @@ -1,80 +1,19 @@ # -*- coding: utf-8 -*- -import time import uvicorn -import asyncio -import logging -import configparser import json from fastapi import FastAPI, Request, HTTPException -from pydantic import BaseModel -from concurrent.futures import ThreadPoolExecutor -from starlette.responses import PlainTextResponse -import functools - -from modelcache import cache -from modelcache.adapter import adapter -from modelcache.manager import CacheBase, VectorBase, get_data_manager -from modelcache.similarity_evaluation.distance import SearchDistanceEvaluation -from modelcache.processor.pre import query_multi_splicing -from modelcache.processor.pre import insert_multi_splicing -from modelcache.utils.model_filter import model_blacklist_filter -from modelcache.embedding import Data2VecAudio +from modelcache.cache import Cache #创建一个FastAPI实例 app = FastAPI() -class RequestData(BaseModel): - type: str - scope: dict = None - query: str = None - chat_info: dict = None - remove_type: str = None - id_list: list = [] - -data2vec = Data2VecAudio() -mysql_config = configparser.ConfigParser() -mysql_config.read('modelcache/config/mysql_config.ini') - -milvus_config = configparser.ConfigParser() -milvus_config.read('modelcache/config/milvus_config.ini') - -# redis_config = configparser.ConfigParser() -# redis_config.read('modelcache/config/redis_config.ini') - -# 初始化datamanager -data_manager = get_data_manager( - CacheBase("mysql", config=mysql_config), - VectorBase("milvus", dimension=data2vec.dimension, milvus_config=milvus_config) -) - -# # 使用redis初始化datamanager -# data_manager = get_data_manager( -# CacheBase("mysql", config=mysql_config), -# VectorBase("redis", dimension=data2vec.dimension, redis_config=redis_config) -# ) +cache = Cache.init("mysql", "milvus") -cache.init( - embedding_func=data2vec.to_embeddings, - data_manager=data_manager, - similarity_evaluation=SearchDistanceEvaluation(), - query_pre_embedding_func=query_multi_splicing, - insert_pre_embedding_func=insert_multi_splicing, -) - -executor = ThreadPoolExecutor(max_workers=6) - -# 异步保存查询信息 -async def save_query_info(result, model, query, delta_time_log): - loop = asyncio.get_running_loop() - func = functools.partial(cache.data_manager.save_query_resp, result, model=model, query=json.dumps(query, ensure_ascii=False), delta_time=delta_time_log) - await loop.run_in_executor(None, func) - - - -@app.get("/welcome", response_class=PlainTextResponse) +@app.get("/welcome") async def first_fastapi(): return "hello, modelcache!" + @app.post("/modelcache") async def user_backend(request: Request): try: @@ -90,7 +29,7 @@ async def user_backend(request: Request): # 如果无法解析，返回格式错误 result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} - asyncio.create_task(save_query_info(result, model='', query='', delta_time_log=0)) + cache.save_query_info(result, model='', query='', delta_time_log=0) raise HTTPException(status_code=101, detail="Invalid JSON format") else: request_data = raw_body @@ -102,19 +41,7 @@ async def user_backend(request: Request): except json.JSONDecodeError: raise HTTPException(status_code=101, detail="Invalid JSON format") - request_type = request_data.get('type') - model = None - if 'scope' in request_data: - model = request_data['scope'].get('model', '').replace('-', '_').replace('.', '_') - query = request_data.get('query') - chat_info = request_data.get('chat_info') - - if not request_type or request_type not in ['query', 'insert', 'remove', 'register']: - result = {"errorCode": 102, - "errorDesc": "type exception, should one of ['query', 'insert', 'remove', 'register']", - "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} - asyncio.create_task(save_query_info(result, model=model, query='', delta_time_log=0)) - raise HTTPException(status_code=102, detail="Type exception, should be one of ['query', 'insert', 'remove', 'register']") + return cache.handle_request(request_data) except Exception as e: request_data = raw_body if 'raw_body' in locals() else None @@ -129,65 +56,6 @@ async def user_backend(request: Request): } return result - - # model filter - filter_resp = model_blacklist_filter(model, request_type) - if isinstance(filter_resp, dict): - return filter_resp - - if request_type == 'query': - try: - start_time = time.time() - response = adapter.ChatCompletion.create_query(scope={"model": model}, query=query) - delta_time = f"{round(time.time() - start_time, 2)}s" - - if response is None: - result = {"errorCode": 0, "errorDesc": '', "cacheHit": False, "delta_time": delta_time, "hit_query": '', "answer": ''} - elif response in ['adapt_query_exception']: - result = {"errorCode": 201, "errorDesc": response, "cacheHit": False, "delta_time": delta_time, - "hit_query": '', "answer": ''} - else: - answer = response['data'] - hit_query = response['hitQuery'] - result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, "hit_query": hit_query, "answer": answer} - - delta_time_log = round(time.time() - start_time, 2) - asyncio.create_task(save_query_info(result, model, query, delta_time_log)) - return result - except Exception as e: - result = {"errorCode": 202, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, - "hit_query": '', "answer": ''} - logging.info(f'result: {str(result)}') - return result - - if request_type == 'insert': - try: - response = adapter.ChatCompletion.create_insert(model=model, chat_info=chat_info) - if response == 'success': - return {"errorCode": 0, "errorDesc": "", "writeStatus": "success"} - else: - return {"errorCode": 301, "errorDesc": response, "writeStatus": "exception"} - except Exception as e: - return {"errorCode": 303, "errorDesc": str(e), "writeStatus": "exception"} - - if request_type == 'remove': - response = adapter.ChatCompletion.create_remove(model=model, remove_type=request_data.get("remove_type"), id_list=request_data.get("id_list")) - if not isinstance(response, dict): - return {"errorCode": 401, "errorDesc": "", "response": response, "removeStatus": "exception"} - - state = response.get('status') - if state == 'success': - return {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} - else: - return {"errorCode": 402, "errorDesc": "", "response": response, "writeStatus": "exception"} - - if request_type == 'register': - response = adapter.ChatCompletion.create_register(model=model) - if response in ['create_success', 'already_exists']: - return {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} - else: - return {"errorCode": 502, "errorDesc": "", "response": response, "writeStatus": "exception"} - # TODO: 可以修改为在命令行中使用`uvicorn your_module_name:app --host 0.0.0.0 --port 5000 --reload`的命令启动 if __name__ == '__main__': uvicorn.run(app, host='0.0.0.0', port=5000) \ No newline at end of file diff --git a/fastapi4modelcache_demo.py b/fastapi4modelcache_demo.py index 624fc75..84058fa 100644 --- a/fastapi4modelcache_demo.py +++ b/fastapi4modelcache_demo.py @@ -1,59 +1,16 @@ # -*- coding: utf-8 -*- -import time import uvicorn -import asyncio -import logging -# import configparser import json from fastapi import FastAPI, Request, HTTPException -from pydantic import BaseModel -from concurrent.futures import ThreadPoolExecutor -from starlette.responses import PlainTextResponse -import functools -from modelcache import cache -from modelcache.adapter import adapter -from modelcache.manager import CacheBase, VectorBase, get_data_manager -from modelcache.similarity_evaluation.distance import SearchDistanceEvaluation -from modelcache.processor.pre import query_multi_splicing -from modelcache.processor.pre import insert_multi_splicing -from modelcache.utils.model_filter import model_blacklist_filter -from modelcache.embedding import Data2VecAudio +from modelcache.cache import Cache # 创建一个FastAPI实例 app = FastAPI() -class RequestData(BaseModel): - type: str - scope: dict = None - query: str = None - chat_info: list = None - remove_type: str = None - id_list: list = [] +cache = Cache.init("sqlite", "faiss") -data2vec = Data2VecAudio() - -data_manager = get_data_manager(CacheBase("sqlite"), VectorBase("faiss", dimension=data2vec.dimension)) - -cache.init( - embedding_func=data2vec.to_embeddings, - data_manager=data_manager, - similarity_evaluation=SearchDistanceEvaluation(), - query_pre_embedding_func=query_multi_splicing, - insert_pre_embedding_func=insert_multi_splicing, -) - -executor = ThreadPoolExecutor(max_workers=6) - -# 异步保存查询信息 -async def save_query_info_fastapi(result, model, query, delta_time_log): - loop = asyncio.get_running_loop() - func = functools.partial(cache.data_manager.save_query_resp, result, model=model, query=json.dumps(query, ensure_ascii=False), delta_time=delta_time_log) - await loop.run_in_executor(None, func) - - - -@app.get("/welcome", response_class=PlainTextResponse) +@app.get("/welcome") async def first_fastapi(): return "hello, modelcache!" @@ -68,9 +25,12 @@ async def user_backend(request: Request): try: # 尝试将字符串解析为JSON对象 request_data = json.loads(raw_body) - except json.JSONDecodeError: + except json.JSONDecodeError as e: # 如果无法解析，返回格式错误 - raise HTTPException(status_code=400, detail="Invalid JSON format") + result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', + "answer": ''} + cache.save_query_info(result, model='', query='', delta_time_log=0) + raise HTTPException(status_code=101, detail="Invalid JSON format") else: request_data = raw_body @@ -79,17 +39,9 @@ async def user_backend(request: Request): try: request_data = json.loads(request_data) except json.JSONDecodeError: - raise HTTPException(status_code=400, detail="Invalid JSON format") - - request_type = request_data.get('type') - model = None - if 'scope' in request_data: - model = request_data['scope'].get('model', '').replace('-', '_').replace('.', '_') - query = request_data.get('query') - chat_info = request_data.get('chat_info') + raise HTTPException(status_code=101, detail="Invalid JSON format") - if not request_type or request_type not in ['query', 'insert', 'remove', 'detox']: - raise HTTPException(status_code=400, detail="Type exception, should be one of ['query', 'insert', 'remove', 'detox']") + return cache.handle_request(request_data) except Exception as e: request_data = raw_body if 'raw_body' in locals() else None @@ -104,59 +56,6 @@ async def user_backend(request: Request): } return result - - # model filter - filter_resp = model_blacklist_filter(model, request_type) - if isinstance(filter_resp, dict): - return filter_resp - - if request_type == 'query': - try: - start_time = time.time() - response = adapter.ChatCompletion.create_query(scope={"model": model}, query=query) - delta_time = f"{round(time.time() - start_time, 2)}s" - - if response is None: - result = {"errorCode": 0, "errorDesc": '', "cacheHit": False, "delta_time": delta_time, "hit_query": '', "answer": ''} - elif response in ['adapt_query_exception']: - # elif isinstance(response, str): - result = {"errorCode": 201, "errorDesc": response, "cacheHit": False, "delta_time": delta_time, - "hit_query": '', "answer": ''} - else: - answer = response['data'] - hit_query = response['hitQuery'] - result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, "hit_query": hit_query, "answer": answer} - - delta_time_log = round(time.time() - start_time, 2) - asyncio.create_task(save_query_info_fastapi(result, model, query, delta_time_log)) - return result - except Exception as e: - result = {"errorCode": 202, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, - "hit_query": '', "answer": ''} - logging.info(f'result: {str(result)}') - return result - - if request_type == 'insert': - try: - response = adapter.ChatCompletion.create_insert(model=model, chat_info=chat_info) - if response == 'success': - return {"errorCode": 0, "errorDesc": "", "writeStatus": "success"} - else: - return {"errorCode": 301, "errorDesc": response, "writeStatus": "exception"} - except Exception as e: - return {"errorCode": 303, "errorDesc": str(e), "writeStatus": "exception"} - - if request_type == 'remove': - response = adapter.ChatCompletion.create_remove(model=model, remove_type=request_data.get("remove_type"), id_list=request_data.get("id_list")) - if not isinstance(response, dict): - return {"errorCode": 401, "errorDesc": "", "response": response, "removeStatus": "exception"} - - state = response.get('status') - if state == 'success': - return {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} - else: - return {"errorCode": 402, "errorDesc": "", "response": response, "writeStatus": "exception"} - # TODO: 可以修改为在命令行中使用`uvicorn your_module_name:app --host 0.0.0.0 --port 5000 --reload`的命令启动 if __name__ == '__main__': uvicorn.run(app, host='0.0.0.0', port=5000) \ No newline at end of file diff --git a/flask4modelcache.py b/flask4modelcache.py index d793953..0ca949d 100644 --- a/flask4modelcache.py +++ b/flask4modelcache.py @@ -1,112 +1,12 @@ # -*- coding: utf-8 -*- -import time from flask import Flask, request -import logging -import configparser import json -from modelcache import cache -from modelcache.adapter import adapter -from modelcache.embedding.mpnet_base import MPNet_Base -from modelcache.manager.vector_data import manager -from modelcache.manager import CacheBase, VectorBase, get_data_manager, data_manager -from modelcache.similarity_evaluation.distance import SearchDistanceEvaluation -from modelcache.processor.pre import query_multi_splicing,insert_multi_splicing, query_with_role -from concurrent.futures import ThreadPoolExecutor -from modelcache.utils.model_filter import model_blacklist_filter -from modelcache.embedding import Data2VecAudio +from modelcache.cache import Cache # 创建一个Flask实例 app = Flask(__name__) - -def response_text(cache_resp): - return cache_resp['data'] - - -def save_query_info(result, model, query, delta_time_log): - cache.data_manager.save_query_resp(result, model=model, query=json.dumps(query, ensure_ascii=False), - delta_time=delta_time_log) - - -def response_hitquery(cache_resp): - return cache_resp['hitQuery'] - -manager.MPNet_base = True - -if manager.MPNet_base: - mpnet_base = MPNet_Base() - embedding_func = mpnet_base.to_embeddings - dimension = mpnet_base.dimension - data_manager.NORMALIZE = False - query_pre_embedding_func=query_with_role - insert_pre_embedding_func=query_with_role -else: - data2vec = Data2VecAudio() - embedding_func = data2vec.to_embeddings - dimension = data2vec.dimension - query_pre_embedding_func=query_multi_splicing - insert_pre_embedding_func=insert_multi_splicing - -mysql_config = configparser.ConfigParser() -mysql_config.read('modelcache/config/mysql_config.ini') - -milvus_config = configparser.ConfigParser() -milvus_config.read('modelcache/config/milvus_config.ini') - -# es_config = configparser.ConfigParser() -# es_config.read('modelcache/config/elasticsearch_config.ini') - -# redis_config = configparser.ConfigParser() -# redis_config.read('modelcache/config/redis_config.ini') - -# chromadb_config = configparser.ConfigParser() -# chromadb_config.read('modelcache/config/chromadb_config.ini') - -data_manager = get_data_manager( - CacheBase("mysql", config=mysql_config), - VectorBase("milvus", - dimension=dimension, - milvus_config=milvus_config, - index_params={ - "metric_type": "COSINE", - "index_type": "HNSW", - "params": {"M": 16, "efConstruction": 64}, - } if manager.MPNet_base else None, - search_params={ - "IVF_FLAT": {"metric_type": "COSINE", "params": {"nprobe": 10}}, - "IVF_SQ8": {"metric_type": "COSINE", "params": {"nprobe": 10}}, - "IVF_PQ": {"metric_type": "COSINE", "params": {"nprobe": 10}}, - "HNSW": {"metric_type": "COSINE", "params": {"ef": 10}}, - "RHNSW_FLAT": {"metric_type": "COSINE", "params": {"ef": 10}}, - "RHNSW_SQ": {"metric_type": "COSINE", "params": {"ef": 10}}, - "RHNSW_PQ": {"metric_type": "COSINE", "params": {"ef": 10}}, - "IVF_HNSW": {"metric_type": "COSINE", "params": {"nprobe": 10, "ef": 10}}, - "ANNOY": {"metric_type": "COSINE", "params": {"search_k": 10}}, - "AUTOINDEX": {"metric_type": "COSINE", "params": {}}, - } if manager.MPNet_base else None - ), - eviction='WTINYLFU', - max_size=100000 -) - - -# data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), -# VectorBase("chromadb", dimension=data2vec.dimension, chromadb_config=chromadb_config)) - -# data_manager = get_data_manager(CacheBase("mysql", config=mysql_config), -# VectorBase("redis", dimension=data2vec.dimension, redis_config=redis_config)) - -cache.init( - embedding_func=embedding_func, - data_manager=data_manager, - similarity_evaluation=SearchDistanceEvaluation(), - query_pre_embedding_func=query_pre_embedding_func, - insert_pre_embedding_func=insert_pre_embedding_func, -) - -global executor -executor = ThreadPoolExecutor(max_workers=6) - +cache = Cache.init("mysql","milvus") @app.route('/welcome') def first_flask(): # 视图函数 @@ -115,123 +15,18 @@ def first_flask(): # 视图函数 @app.route('/modelcache', methods=['GET', 'POST']) def user_backend(): - param_dict = [] + param_dict = {} try: if request.method == 'POST': param_dict = request.json elif request.method == 'GET': param_dict = request.args - except Exception as e: - result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', - "answer": ''} - cache.data_manager.save_query_resp(result, model='', query='', delta_time=0) - return json.dumps(result) - - # param parsing - try: - request_type = param_dict.get("type") - - scope = param_dict.get("scope") - if scope is not None: - model = scope.get('model') - model = model.replace('-', '_') - model = model.replace('.', '_') - query = param_dict.get("query") - chat_info = param_dict.get("chat_info") - if request_type is None or request_type not in ['query', 'insert', 'remove', 'register']: - result = {"errorCode": 102, - "errorDesc": "type exception, should one of ['query', 'insert', 'remove', 'register']", - "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} - cache.data_manager.save_query_resp(result, model=model, query='', delta_time=0) - return json.dumps(result) + return json.dumps(cache.handle_request(param_dict)) except Exception as e: - result = {"errorCode": 103, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', + result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} - return json.dumps(result) - - # model filter - filter_resp = model_blacklist_filter(model, request_type) - if isinstance(filter_resp, dict): - return json.dumps(filter_resp) - - if request_type == 'query': - try: - start_time = time.time() - response = adapter.ChatCompletion.create_query( - scope={"model": model}, - query=query - ) - delta_time = '{}s'.format(round(time.time() - start_time, 2)) - if response is None: - result = {"errorCode": 0, "errorDesc": '', "cacheHit": False, "delta_time": delta_time, "hit_query": '', - "answer": ''} - # elif response in ['adapt_query_exception']: - elif isinstance(response, str): - result = {"errorCode": 201, "errorDesc": response, "cacheHit": False, "delta_time": delta_time, - "hit_query": '', "answer": ''} - else: - answer = response_text(response) - hit_query = response_hitquery(response) - result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, - "hit_query": hit_query, "answer": answer} - delta_time_log = round(time.time() - start_time, 2) - future = executor.submit(save_query_info, result, model, query, delta_time_log) - except Exception as e: - result = {"errorCode": 202, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, - "hit_query": '', "answer": ''} - logging.info('result: {}'.format(result)) - - return json.dumps(result, ensure_ascii=False) - - if request_type == 'insert': - try: - try: - response = adapter.ChatCompletion.create_insert( - model=model, - chat_info=chat_info - ) - except Exception as e: - result = {"errorCode": 302, "errorDesc": str(e), "writeStatus": "exception"} - return json.dumps(result, ensure_ascii=False) - - if response == 'success': - result = {"errorCode": 0, "errorDesc": "", "writeStatus": "success"} - else: - result = {"errorCode": 301, "errorDesc": response, "writeStatus": "exception"} - return json.dumps(result, ensure_ascii=False) - except Exception as e: - result = {"errorCode": 303, "errorDesc": str(e), "writeStatus": "exception"} - return json.dumps(result, ensure_ascii=False) - - if request_type == 'remove': - remove_type = param_dict.get("remove_type") - id_list = param_dict.get("id_list", []) - - response = adapter.ChatCompletion.create_remove( - model=model, - remove_type=remove_type, - id_list=id_list - ) - if not isinstance(response, dict): - result = {"errorCode": 401, "errorDesc": "", "response": response, "removeStatus": "exception"} - return json.dumps(result) - - state = response.get('status') - if state == 'success': - result = {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} - else: - result = {"errorCode": 402, "errorDesc": "", "response": response, "writeStatus": "exception"} - return json.dumps(result) - - if request_type == 'register': - response = adapter.ChatCompletion.create_register( - model=model - ) - if response in ['create_success', 'already_exists']: - result = {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} - else: - result = {"errorCode": 502, "errorDesc": "", "response": response, "writeStatus": "exception"} + cache.save_query_resp(result, model='', query='', delta_time=0) return json.dumps(result) diff --git a/flask4modelcache_demo.py b/flask4modelcache_demo.py index 54b9e8e..e0d5cfb 100644 --- a/flask4modelcache_demo.py +++ b/flask4modelcache_demo.py @@ -1,51 +1,12 @@ # -*- coding: utf-8 -*- -import time from flask import Flask, request -import logging import json -from modelcache import cache -from modelcache.adapter import adapter -from modelcache.manager import CacheBase, VectorBase, get_data_manager -from modelcache.similarity_evaluation.distance import SearchDistanceEvaluation -from modelcache.processor.pre import query_multi_splicing -from modelcache.processor.pre import insert_multi_splicing -from concurrent.futures import ThreadPoolExecutor -from modelcache.utils.model_filter import model_blacklist_filter -from modelcache.embedding import Data2VecAudio +from modelcache.cache import Cache # 创建一个Flask实例 app = Flask(__name__) - -def response_text(cache_resp): - return cache_resp['data'] - - -def save_query_info(result, model, query, delta_time_log): - cache.data_manager.save_query_resp(result, model=model, query=json.dumps(query, ensure_ascii=False), - delta_time=delta_time_log) - - -def response_hitquery(cache_resp): - return cache_resp['hitQuery'] - - -data2vec = Data2VecAudio() -data_manager = get_data_manager(CacheBase("sqlite"), VectorBase("faiss", dimension=data2vec.dimension)) - - -cache.init( - embedding_func=data2vec.to_embeddings, - data_manager=data_manager, - similarity_evaluation=SearchDistanceEvaluation(), - query_pre_embedding_func=query_multi_splicing, - insert_pre_embedding_func=insert_multi_splicing, -) - -# cache.set_openai_key() -global executor -executor = ThreadPoolExecutor(max_workers=6) - +cache = Cache.init("sqlite","faiss") @app.route('/welcome') def first_flask(): # 视图函数 @@ -54,116 +15,20 @@ def first_flask(): # 视图函数 @app.route('/modelcache', methods=['GET', 'POST']) def user_backend(): + param_dict = {} try: if request.method == 'POST': - request_data = request.json + param_dict = request.json elif request.method == 'GET': - request_data = request.args - param_dict = json.loads(request_data) - except Exception as e: - result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', - "answer": ''} - cache.data_manager.save_query_resp(result, model='', query='', delta_time=0) - return json.dumps(result) + param_dict = request.args - # param parsing - try: - request_type = param_dict.get("type") - scope = param_dict.get("scope") - if scope is not None: - model = scope.get('model') - model = model.replace('-', '_') - model = model.replace('.', '_') - query = param_dict.get("query") - chat_info = param_dict.get("chat_info") - if request_type is None or request_type not in ['query', 'insert', 'detox', 'remove']: - result = {"errorCode": 102, - "errorDesc": "type exception, should one of ['query', 'insert', 'detox', 'remove']", - "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} - cache.data_manager.save_query_resp(result, model=model, query='', delta_time=0) - return json.dumps(result) + return json.dumps(cache.handle_request(param_dict)) except Exception as e: - result = {"errorCode": 103, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', + result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} - return json.dumps(result) - - # model filter - filter_resp = model_blacklist_filter(model, request_type) - if isinstance(filter_resp, dict): - return json.dumps(filter_resp) - - if request_type == 'query': - try: - start_time = time.time() - response = adapter.ChatCompletion.create_query( - scope={"model": model}, - query=query - ) - delta_time = '{}s'.format(round(time.time() - start_time, 2)) - if response is None: - result = {"errorCode": 0, "errorDesc": '', "cacheHit": False, "delta_time": delta_time, "hit_query": '', - "answer": ''} - elif response in ['adapt_query_exception']: - result = {"errorCode": 201, "errorDesc": response, "cacheHit": False, "delta_time": delta_time, - "hit_query": '', "answer": ''} - else: - answer = response_text(response) - hit_query = response_hitquery(response) - result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, - "hit_query": hit_query, "answer": answer} - delta_time_log = round(time.time() - start_time, 2) - future = executor.submit(save_query_info, result, model, query, delta_time_log) - except Exception as e: - result = {"errorCode": 202, "errorDesc": e, "cacheHit": False, "delta_time": 0, - "hit_query": '', "answer": ''} - logging.info('result: {}'.format(result)) - return json.dumps(result, ensure_ascii=False) - - if request_type == 'insert': - try: - try: - response = adapter.ChatCompletion.create_insert( - model=model, - chat_info=chat_info - ) - except Exception as e: - result = {"errorCode": 303, "errorDesc": e, "writeStatus": "exception"} - return json.dumps(result, ensure_ascii=False) - - if response in ['adapt_insert_exception']: - result = {"errorCode": 301, "errorDesc": response, "writeStatus": "exception"} - elif response == 'success': - result = {"errorCode": 0, "errorDesc": "", "writeStatus": "success"} - else: - result = {"errorCode": 302, "errorDesc": response, - "writeStatus": "exception"} - return json.dumps(result, ensure_ascii=False) - except Exception as e: - result = {"errorCode": 304, "errorDesc": e, "writeStatus": "exception"} - return json.dumps(result, ensure_ascii=False) - - if request_type == 'remove': - remove_type = param_dict.get("remove_type") - id_list = param_dict.get("id_list", []) - - response = adapter.ChatCompletion.create_remove( - model=model, - remove_type=remove_type, - id_list=id_list - ) - - if not isinstance(response, dict): - result = {"errorCode": 401, "errorDesc": "", "response": response, "removeStatus": "exception"} - return json.dumps(result) - - state = response.get('status') - if state == 'success': - result = {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} - else: - result = {"errorCode": 402, "errorDesc": "", "response": response, "writeStatus": "exception"} + cache.save_query_resp(result, model='', query='', delta_time=0) return json.dumps(result) if __name__ == '__main__': - # app.run(host='0.0.0.0', port=5000, debug=True) app.run(host='0.0.0.0', port=5000) diff --git a/modelcache/__init__.py b/modelcache/__init__.py index 9a82563..8bcb18a 100644 --- a/modelcache/__init__.py +++ b/modelcache/__init__.py @@ -1,5 +1,7 @@ # -*- coding: utf-8 -*- -from modelcache.core import Cache -from modelcache.core import cache -from modelcache.config import Config -import modelcache.adapter + + + + + + diff --git a/modelcache/adapter/adapter.py b/modelcache/adapter/adapter.py index ddad9af..204841b 100644 --- a/modelcache/adapter/adapter.py +++ b/modelcache/adapter/adapter.py @@ -20,6 +20,7 @@ def cache_data_convert(cache_data, cache_query): **kwargs ) except Exception as e: + print(e) return str(e) @classmethod @@ -41,7 +42,7 @@ def create_remove(cls, *args, **kwargs): **kwargs ) except Exception as e: - logging.info('adapt_remove_e: {}'.format(e)) + print(e) return str(e) @classmethod @@ -52,6 +53,7 @@ def create_register(cls, *args, **kwargs): **kwargs ) except Exception as e: + print(e) return str(e) diff --git a/modelcache/adapter/adapter_insert.py b/modelcache/adapter/adapter_insert.py index c2e2a29..a507ee1 100644 --- a/modelcache/adapter/adapter_insert.py +++ b/modelcache/adapter/adapter_insert.py @@ -1,18 +1,14 @@ # -*- coding: utf-8 -*- -from modelcache import cache from modelcache.utils.error import NotInitError from modelcache.utils.time import time_cal def adapt_insert(*args, **kwargs): - chat_cache = kwargs.pop("cache_obj", cache) + chat_cache = kwargs.pop("cache_obj") model = kwargs.pop("model", None) require_object_store = kwargs.pop("require_object_store", False) if require_object_store: assert chat_cache.data_manager.o, "Object store is required for adapter." - if not chat_cache.has_init: - raise NotInitError() - cache_enable = chat_cache.cache_enable_func(*args, **kwargs) context = kwargs.pop("cache_context", {}) chat_info = kwargs.pop("chat_info", []) @@ -24,7 +20,7 @@ def adapt_insert(*args, **kwargs): pre_embedding_data = chat_cache.insert_pre_embedding_func( row, extra_param=context.get("pre_embedding_func", None), - prompts=chat_cache.config.prompts, + prompts=chat_cache.prompts, ) pre_embedding_data_list.append(pre_embedding_data) llm_data_list.append(row['answer']) @@ -32,6 +28,7 @@ def adapt_insert(*args, **kwargs): chat_cache.embedding_func, func_name="embedding", report_func=chat_cache.report.embedding, + cache_obj=chat_cache )(pre_embedding_data) embedding_data_list.append(embedding_data) diff --git a/modelcache/adapter/adapter_query.py b/modelcache/adapter/adapter_query.py index aa42eae..2e56cc2 100644 --- a/modelcache/adapter/adapter_query.py +++ b/modelcache/adapter/adapter_query.py @@ -1,103 +1,150 @@ # -*- coding: utf-8 -*- import logging -import time - -from modelcache import cache -from modelcache.utils.error import NotInitError +from modelcache.embedding import MetricType from modelcache.utils.time import time_cal -from modelcache.processor.pre import multi_analysis from FlagEmbedding import FlagReranker -from modelcache.manager.vector_data import manager USE_RERANKER = False # 如果为 True 则启用 reranker，否则使用原有逻辑 def adapt_query(cache_data_convert, *args, **kwargs): - chat_cache = kwargs.pop("cache_obj", cache) + chat_cache = kwargs.pop("cache_obj") scope = kwargs.pop("scope") model = scope['model'] - if not chat_cache.has_init: - raise NotInitError() - cache_enable = chat_cache.cache_enable_func(*args, **kwargs) context = kwargs.pop("cache_context", {}) - embedding_data = None cache_factor = kwargs.pop("cache_factor", 1.0) pre_embedding_data = chat_cache.query_pre_embedding_func( kwargs, extra_param=context.get("pre_embedding_func", None), - prompts=chat_cache.config.prompts, + prompts=chat_cache.prompts, ) - if cache_enable: - embedding_data = time_cal( - chat_cache.embedding_func, - func_name="embedding", - report_func=chat_cache.report.embedding, - )(pre_embedding_data) - if cache_enable: - cache_data_list = time_cal( - chat_cache.data_manager.search, - func_name="vector_search", - report_func=chat_cache.report.search, - )( - embedding_data, - extra_param=context.get("search_func", None), - top_k=kwargs.pop("top_k", -1), - model=model - ) - cache_answers = [] - cache_questions = [] - cache_ids = [] + embedding_data = time_cal( + chat_cache.embedding_func, + func_name="embedding", + report_func=chat_cache.report.embedding, + cache_obj=chat_cache + )(pre_embedding_data) + + cache_data_list = time_cal( + chat_cache.data_manager.search, + func_name="vector_search", + report_func=chat_cache.report.search, + cache_obj=chat_cache + )( + embedding_data, + extra_param=context.get("search_func", None), + top_k=kwargs.pop("top_k", -1), + model=model + ) + cache_answers = [] + cache_questions = [] + cache_ids = [] + cosine_similarity = None + + if chat_cache.similarity_metric_type == MetricType.COSINE: cosine_similarity = cache_data_list[0][0] + # This code uses the built-in cosine similarity evaluation in milvus + if cosine_similarity < 0.9: + return None + elif chat_cache.similarity_metric_type == MetricType.L2: + ## this is the code that uses L2 for similarity evaluation + similarity_threshold = chat_cache.similarity_threshold + similarity_threshold_long = chat_cache.similarity_threshold_long - if manager.MPNet_base: - # This code uses the built-in cosine similarity evaluation in milvus - if cosine_similarity < 0.9: - return None + min_rank, max_rank = chat_cache.similarity_evaluation.range() + rank_threshold = (max_rank - min_rank) * similarity_threshold * cache_factor + rank_threshold_long = (max_rank - min_rank) * similarity_threshold_long * cache_factor + rank_threshold = ( + max_rank + if rank_threshold > max_rank + else min_rank + if rank_threshold < min_rank + else rank_threshold + ) + rank_threshold_long = ( + max_rank + if rank_threshold_long > max_rank + else min_rank + if rank_threshold_long < min_rank + else rank_threshold_long + ) + if cache_data_list is None or len(cache_data_list) == 0: + rank_pre = -1.0 else: - ## this is the code that uses L2 for similarity evaluation - similarity_threshold = chat_cache.config.similarity_threshold - similarity_threshold_long = chat_cache.config.similarity_threshold_long - - min_rank, max_rank = chat_cache.similarity_evaluation.range() - rank_threshold = (max_rank - min_rank) * similarity_threshold * cache_factor - rank_threshold_long = (max_rank - min_rank) * similarity_threshold_long * cache_factor - rank_threshold = ( - max_rank - if rank_threshold > max_rank - else min_rank - if rank_threshold < min_rank - else rank_threshold + cache_data_dict = {'search_result': cache_data_list[0]} + rank_pre = chat_cache.similarity_evaluation.evaluation( + None, + cache_data_dict, + extra_param=context.get("evaluation_func", None), ) - rank_threshold_long = ( - max_rank - if rank_threshold_long > max_rank - else min_rank - if rank_threshold_long < min_rank - else rank_threshold_long + if rank_pre < rank_threshold: + return None + else: + raise ValueError( + f"Unsupported similarity metric type: {chat_cache.similarity_metric_type}" + ) + + if USE_RERANKER: + reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=False) + for cache_data in cache_data_list: + primary_id = cache_data[1] + ret = chat_cache.data_manager.get_scalar_data( + cache_data, extra_param=context.get("get_scalar_data", None),model=model ) - if cache_data_list is None or len(cache_data_list) == 0: - rank_pre = -1.0 + if ret is None: + continue + + rank = reranker.compute_score([pre_embedding_data, ret[0]], normalize=True)[0] + + if "deps" in context and hasattr(ret.question, "deps"): + eval_query_data = { + "question": context["deps"][0]["data"], + "embedding": None + } + eval_cache_data = { + "question": ret.question.deps[0].data, + "answer": ret.answers[0].answer, + "search_result": cache_data, + "embedding": None, + } else: - cache_data_dict = {'search_result': cache_data_list[0]} - rank_pre = chat_cache.similarity_evaluation.evaluation( - None, - cache_data_dict, - extra_param=context.get("evaluation_func", None), - ) - if rank_pre < rank_threshold: - return None + eval_query_data = { + "question": pre_embedding_data, + "embedding": embedding_data, + } - if USE_RERANKER and not manager.MPNet_base: - reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=False) - for cache_data in cache_data_list: - primary_id = cache_data[1] - ret = chat_cache.data_manager.get_scalar_data( - cache_data, extra_param=context.get("get_scalar_data", None),model=model - ) - if ret is None: - continue + eval_cache_data = { + "question": ret[0], + "answer": ret[1], + "search_result": cache_data, + "embedding": None + } - rank = reranker.compute_score([pre_embedding_data, ret[0]], normalize=True)[0] + if len(pre_embedding_data) <= 256: + if rank_threshold <= rank: + cache_answers.append((rank, ret[0])) + cache_questions.append((rank, ret[1])) + cache_ids.append((rank, primary_id)) + else: + if rank_threshold_long <= rank: + cache_answers.append((rank, ret[0])) + cache_questions.append((rank, ret[1])) + cache_ids.append((rank, primary_id)) + else: + # 不使用 reranker 时，走原来的逻辑 + for cache_data in cache_data_list: + primary_id = cache_data[1] + ret = chat_cache.data_manager.get_scalar_data( + cache_data, extra_param=context.get("get_scalar_data", None),model=model + ) + if ret is None: + continue + if chat_cache.similarity_metric_type == MetricType.COSINE: + assert cosine_similarity is not None, "cosine_similarity should not be None" + cache_answers.append((cosine_similarity, ret[0])) + cache_questions.append((cosine_similarity, ret[1])) + cache_ids.append((cosine_similarity, primary_id)) + elif chat_cache.similarity_metric_type == MetricType.L2: if "deps" in context and hasattr(ret.question, "deps"): eval_query_data = { "question": context["deps"][0]["data"], @@ -121,6 +168,11 @@ def adapt_query(cache_data_convert, *args, **kwargs): "search_result": cache_data, "embedding": None } + rank = chat_cache.similarity_evaluation.evaluation( + eval_query_data, + eval_cache_data, + extra_param=context.get("evaluation_func", None), + ) if len(pre_embedding_data) <= 256: if rank_threshold <= rank: @@ -132,81 +184,30 @@ def adapt_query(cache_data_convert, *args, **kwargs): cache_answers.append((rank, ret[0])) cache_questions.append((rank, ret[1])) cache_ids.append((rank, primary_id)) - else: - # 不使用 reranker 时，走原来的逻辑 - for cache_data in cache_data_list: - primary_id = cache_data[1] - ret = chat_cache.data_manager.get_scalar_data( - cache_data, extra_param=context.get("get_scalar_data", None),model=model + else: + raise ValueError( + f"Unsupported similarity metric type: {chat_cache.similarity_metric_type}" ) - if ret is None: - continue - - if manager.MPNet_base: - cache_answers.append((cosine_similarity, ret[0])) - cache_questions.append((cosine_similarity, ret[1])) - cache_ids.append((cosine_similarity, primary_id)) - else: - if "deps" in context and hasattr(ret.question, "deps"): - eval_query_data = { - "question": context["deps"][0]["data"], - "embedding": None - } - eval_cache_data = { - "question": ret.question.deps[0].data, - "answer": ret.answers[0].answer, - "search_result": cache_data, - "embedding": None, - } - else: - eval_query_data = { - "question": pre_embedding_data, - "embedding": embedding_data, - } - - eval_cache_data = { - "question": ret[0], - "answer": ret[1], - "search_result": cache_data, - "embedding": None - } - rank = chat_cache.similarity_evaluation.evaluation( - eval_query_data, - eval_cache_data, - extra_param=context.get("evaluation_func", None), - ) - if len(pre_embedding_data) <= 256: - if rank_threshold <= rank: - cache_answers.append((rank, ret[0])) - cache_questions.append((rank, ret[1])) - cache_ids.append((rank, primary_id)) - else: - if rank_threshold_long <= rank: - cache_answers.append((rank, ret[0])) - cache_questions.append((rank, ret[1])) - cache_ids.append((rank, primary_id)) - - cache_answers = sorted(cache_answers, key=lambda x: x[0], reverse=True) - cache_questions = sorted(cache_questions, key=lambda x: x[0], reverse=True) - cache_ids = sorted(cache_ids, key=lambda x: x[0], reverse=True) - if len(cache_answers) != 0: - return_message = chat_cache.post_process_messages_func( - [t[1] for t in cache_answers] - ) - return_query = chat_cache.post_process_messages_func( - [t[1] for t in cache_questions] - ) - return_id = chat_cache.post_process_messages_func( - [t[1] for t in cache_ids] - ) - # 更新命中次数 - try: - chat_cache.data_manager.update_hit_count(return_id) - except Exception: - logging.info('update_hit_count except, please check!') + cache_answers = sorted(cache_answers, key=lambda x: x[0], reverse=True) + cache_questions = sorted(cache_questions, key=lambda x: x[0], reverse=True) + cache_ids = sorted(cache_ids, key=lambda x: x[0], reverse=True) + if len(cache_answers) != 0: + return_message = chat_cache.post_process_messages_func( + [t[1] for t in cache_answers] + ) + return_query = chat_cache.post_process_messages_func( + [t[1] for t in cache_questions] + ) + return_id = chat_cache.post_process_messages_func( + [t[1] for t in cache_ids] + ) + # 更新命中次数 + try: + chat_cache.data_manager.update_hit_count(return_id) + except Exception: + logging.info('update_hit_count except, please check!') - chat_cache.report.hint_cache() - return cache_data_convert(return_message, return_query) - return None + chat_cache.report.hint_cache() + return cache_data_convert(return_message, return_query) return None \ No newline at end of file diff --git a/modelcache/adapter/adapter_register.py b/modelcache/adapter/adapter_register.py index dafe597..811bcec 100644 --- a/modelcache/adapter/adapter_register.py +++ b/modelcache/adapter/adapter_register.py @@ -1,9 +1,8 @@ # -*- coding: utf-8 -*- -from modelcache import cache def adapt_register(*args, **kwargs): - chat_cache = kwargs.pop("cache_obj", cache) + chat_cache = kwargs.pop("cache_obj") model = kwargs.pop("model", None) if model is None or len(model) == 0: return ValueError('') diff --git a/modelcache/adapter/adapter_remove.py b/modelcache/adapter/adapter_remove.py index 25f1ba3..ca00fc1 100644 --- a/modelcache/adapter/adapter_remove.py +++ b/modelcache/adapter/adapter_remove.py @@ -1,17 +1,14 @@ # -*- coding: utf-8 -*- -from modelcache import cache from modelcache.utils.error import NotInitError, RemoveError def adapt_remove(*args, **kwargs): - chat_cache = kwargs.pop("cache_obj", cache) + chat_cache = kwargs.pop("cache_obj") model = kwargs.pop("model", None) remove_type = kwargs.pop("remove_type", None) require_object_store = kwargs.pop("require_object_store", False) if require_object_store: assert chat_cache.data_manager.o, "Object store is required for adapter." - if not chat_cache.has_init: - raise NotInitError() # delete data if remove_type == 'delete_by_id': diff --git a/modelcache/cache.py b/modelcache/cache.py new file mode 100644 index 0000000..8144138 --- /dev/null +++ b/modelcache/cache.py @@ -0,0 +1,311 @@ +# -*- coding: utf-8 -*- +import atexit +import json +import logging +import time +from typing import Callable, Optional, List +from modelcache.adapter import adapter +from modelcache.utils.model_filter import model_blacklist_filter +from concurrent.futures import ThreadPoolExecutor +import configparser +from modelcache.embedding.base import BaseEmbedding, EmbeddingModel, MetricType +from modelcache.manager.scalar_data.sql_storage import SQLStorage +from modelcache.manager.vector_data.base import VectorStorage +from modelcache.processor.post import first +from modelcache.processor.pre import query_with_role, query_multi_splicing, insert_multi_splicing +from modelcache.similarity_evaluation.base import SimilarityEvaluation +from modelcache.report import Report +from modelcache.similarity_evaluation.distance import SearchDistanceEvaluation +from modelcache.utils.error import CacheError +from modelcache.utils.log import modelcache_log +from modelcache.manager.data_manager import DataManager + + #=====================================================================# + #==================== Cache class definition =========================# + #=====================================================================# + +executor = ThreadPoolExecutor(max_workers=6) + +def response_text(cache_resp): + return cache_resp['data'] + +def response_hitquery(cache_resp): + return cache_resp['hitQuery'] + +# noinspection PyMethodMayBeStatic +class Cache: + def __init__( + self, + embedding_model: EmbeddingModel, + similarity_metric_type: MetricType, + data_manager: DataManager, + query_pre_embedding_func: Callable, + insert_pre_embedding_func: Callable, + embedding_func: Callable, + report: Report, # TODO: figure out why this is needed + similarity_evaluation: Optional[SimilarityEvaluation], + post_process_messages_func: Callable, + similarity_threshold: float = 0.95, + similarity_threshold_long: float = 0.95, + prompts: Optional[List[str]] = None, + log_time_func: Callable[[str, float], None] = None, + ): + if similarity_threshold < 0 or similarity_threshold > 1: + raise CacheError( + "Invalid the similarity threshold param, reasonable range: 0-1" + ) + self.data_manager: DataManager = data_manager + self.embedding_model: EmbeddingModel = embedding_model + self.similarity_metric_type: MetricType = similarity_metric_type + self.report: Report = report + self.query_pre_embedding_func: Callable = query_pre_embedding_func + self.insert_pre_embedding_func: Callable = insert_pre_embedding_func + self.embedding_func: Callable = embedding_func + self.similarity_evaluation: Optional[SimilarityEvaluation] = similarity_evaluation + self.post_process_messages_func: Callable = post_process_messages_func + self.similarity_threshold = similarity_threshold + self.similarity_threshold_long = similarity_threshold_long + self.prompts = prompts + self.log_time_func: Callable[[str, float], None] = log_time_func + + @atexit.register + def close(): + try: + self.data_manager.close() + except Exception as e: + modelcache_log.error(e) + + def save_query_resp(self, query_resp_dict, **kwargs): + self.data_manager.save_query_resp(query_resp_dict, **kwargs) + + def save_query_info(self,result, model, query, delta_time_log): + self.data_manager.save_query_resp(result, model=model, query=json.dumps(query, ensure_ascii=False), + delta_time=delta_time_log) + + def handle_request(self, param_dict: dict): + # param parsing + try: + request_type = param_dict.get("type") + + scope = param_dict.get("scope") + model = None + if scope is not None: + model = scope.get('model') + model = model.replace('-', '_') + model = model.replace('.', '_') + query = param_dict.get("query") + chat_info = param_dict.get("chat_info") + if request_type is None or request_type not in ['query', 'insert', 'remove', 'register']: + result = {"errorCode": 102, + "errorDesc": "type exception, should one of ['query', 'insert', 'remove', 'register']", + "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} + self.data_manager.save_query_resp(result, model=model, query='', delta_time=0) + return result + except Exception as e: + return {"errorCode": 103, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', + "answer": ''} + + # model filter + filter_resp = model_blacklist_filter(model, request_type) + if isinstance(filter_resp, dict): + return filter_resp + + # handle request + if request_type == 'query': + return self.handle_query(model, query) + elif request_type == 'insert': + return self.handle_insert(chat_info, model) + elif request_type == 'remove': + return self.handle_remove(model, param_dict) + elif request_type == 'register': + return self.handle_register(model) + else: + return {"errorCode": 400, "errorDesc": "bad request"} + + def handle_register(self, model): + response = adapter.ChatCompletion.create_register( + model=model, + cache_obj=self + ) + if response in ['create_success', 'already_exists']: + result = {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} + else: + result = {"errorCode": 502, "errorDesc": "", "response": response, "writeStatus": "exception"} + return result + + def handle_remove(self, model, param_dict): + remove_type = param_dict.get("remove_type") + id_list = param_dict.get("id_list", []) + response = adapter.ChatCompletion.create_remove( + model=model, + remove_type=remove_type, + id_list=id_list, + cache_obj=self + ) + if not isinstance(response, dict): + return {"errorCode": 401, "errorDesc": "", "response": response, "removeStatus": "exception"} + state = response.get('status') + if state == 'success': + result = {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} + else: + result = {"errorCode": 402, "errorDesc": "", "response": response, "writeStatus": "exception"} + return result + + def handle_insert(self, chat_info, model): + try: + try: + response = adapter.ChatCompletion.create_insert( + model=model, + chat_info=chat_info, + cache_obj=self + ) + except Exception as e: + return {"errorCode": 302, "errorDesc": str(e), "writeStatus": "exception"} + + if response == 'success': + result = {"errorCode": 0, "errorDesc": "", "writeStatus": "success"} + else: + result = {"errorCode": 301, "errorDesc": response, "writeStatus": "exception"} + return result + except Exception as e: + return {"errorCode": 303, "errorDesc": str(e), "writeStatus": "exception"} + + def handle_query(self, model, query): + try: + start_time = time.time() + response = adapter.ChatCompletion.create_query( + scope={"model": model}, + query=query, + cache_obj=self + ) + delta_time = '{}s'.format(round(time.time() - start_time, 2)) + if response is None: + result = {"errorCode": 0, "errorDesc": '', "cacheHit": False, "delta_time": delta_time, "hit_query": '', + "answer": ''} + # elif response in ['adapt_query_exception']: + elif isinstance(response, str): + result = {"errorCode": 201, "errorDesc": response, "cacheHit": False, "delta_time": delta_time, + "hit_query": '', "answer": ''} + else: + answer = response_text(response) + hit_query = response_hitquery(response) + result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, + "hit_query": hit_query, "answer": answer} + delta_time_log = round(time.time() - start_time, 2) + executor.submit(self.save_query_info, result, model, query, delta_time_log) + except Exception as e: + result = {"errorCode": 202, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, + "hit_query": '', "answer": ''} + logging.info('result: {}'.format(result)) + return result + + def flush(self): + self.data_manager.flush() + + @staticmethod + def init(sql_storage: str, vector_storage: str) -> 'Cache': + #================= configurations for databases ===================# + + sql_config = configparser.ConfigParser() + vector_config = configparser.ConfigParser() + + if sql_storage == "mysql": + sql_config.read('modelcache/config/mysql_config.ini') + elif sql_storage == "elasticsearch": + sql_config.read('modelcache/config/elasticsearch_config.ini') + elif sql_storage == "sqlite": + sql_config.read('modelcache/config/sqlite_config.ini') + else: + modelcache_log.error(f"Unsupported cache storage: {sql_storage}.") + raise CacheError(f"Unsupported cache storage: {sql_storage}.") + + if vector_storage == "milvus" : + vector_config.read('modelcache/config/milvus_config.ini') + elif vector_storage == "chromadb" : + vector_config.read('modelcache/config/chromadb_config.ini') + elif vector_storage == "redis" : + vector_config.read('modelcache/config/redis_config.ini') + elif vector_storage == "faiss" : + vector_config = None # faiss does not require additional configuration + else: + modelcache_log.error(f"Unsupported vector storage: {vector_storage}.") + raise CacheError(f"Unsupported vector storage: {vector_storage}.") + + + #=============== model-specific configuration =====================# + + embedding_model = EmbeddingModel.HUGGINGFACE + model_path = "sentence-transformers/all-mpnet-base-v2" + base_embedding = BaseEmbedding.get(embedding_model, model_path=model_path) + + #=== These will be used to initialize the cache ===# + query_pre_embedding_func: Callable = None + insert_pre_embedding_func: Callable = None + post_process_messages_func: Callable = None + similarity_evaluation: Optional[SimilarityEvaluation] = None + similarity_metric_type: MetricType = None + similarity_threshold: float = None + similarity_threshold_long: float = None + normalize: bool = None + #==================================================# + + # switching based on embedding_model + if embedding_model == EmbeddingModel.HUGGINGFACE: + query_pre_embedding_func = query_with_role + insert_pre_embedding_func = query_with_role + post_process_messages_func = first + similarity_evaluation = None # Uses the built-in cosine similarity evaluation in milvus + similarity_metric_type = MetricType.COSINE + similarity_threshold = 0.9 + similarity_threshold_long = 0.9 + normalize = False + + elif embedding_model == EmbeddingModel.DATA2VEC_AUDIO: + query_pre_embedding_func = query_multi_splicing + insert_pre_embedding_func = insert_multi_splicing + post_process_messages_func = first + similarity_evaluation = SearchDistanceEvaluation() + similarity_metric_type = MetricType.L2 + similarity_threshold = 0.95 + similarity_threshold_long = 0.95 + normalize = True + + # add more configurations for other embedding models as needed + else: + modelcache_log.error(f"Please add configuration for {embedding_model} in modelcache/__init__.py.") + raise CacheError(f"Please add configuration for {embedding_model} in modelcache/__init__.py.") + + # ====================== Data manager ==============================# + + data_manager = DataManager.get( + SQLStorage.get(sql_storage, config=sql_config), + VectorStorage.get( + name=vector_storage, + dimension=base_embedding.dimension, + config=vector_config, + metric_type=similarity_metric_type, + ), + eviction='WTINYLFU', + max_size=100000, + normalize=normalize, + ) + + + #================== Cache Initialization ====================# + + cache = Cache( + embedding_model = embedding_model, + similarity_metric_type = similarity_metric_type, + data_manager = data_manager, + report = Report(), + embedding_func = base_embedding.to_embeddings, + query_pre_embedding_func = query_pre_embedding_func, + insert_pre_embedding_func = insert_pre_embedding_func, + similarity_evaluation = similarity_evaluation, + post_process_messages_func = post_process_messages_func, + similarity_threshold = similarity_threshold, + similarity_threshold_long = similarity_threshold_long, + prompts = None, + log_time_func = None, + ) + return cache \ No newline at end of file diff --git a/modelcache/config.py b/modelcache/config.py deleted file mode 100644 index 69b3246..0000000 --- a/modelcache/config.py +++ /dev/null @@ -1,22 +0,0 @@ -# -*- coding: utf-8 -*- -from typing import Optional, Callable, List -from modelcache.utils.error import CacheError - - -class Config: - - def __init__( - self, - log_time_func: Optional[Callable[[str, float], None]] = None, - similarity_threshold: float = 0.95, - similarity_threshold_long: float = 0.95, - prompts: Optional[List[str]] = None - ): - if similarity_threshold < 0 or similarity_threshold > 1: - raise CacheError( - "Invalid the similarity threshold param, reasonable range: 0-1" - ) - self.log_time_func = log_time_func - self.similarity_threshold = similarity_threshold - self.similarity_threshold_long = similarity_threshold_long - self.prompts = prompts diff --git a/modelcache/core.py b/modelcache/core.py deleted file mode 100644 index bd57029..0000000 --- a/modelcache/core.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -import atexit -from typing import Optional, List, Any -from modelcache.processor.post import first -from modelcache.similarity_evaluation import ExactMatchEvaluation -from modelcache.similarity_evaluation import SimilarityEvaluation -from modelcache.embedding.string_text import to_embeddings as string_embedding -from modelcache.report import Report -from modelcache.config import Config -from modelcache.utils.cache_func import cache_all -from modelcache.utils.log import modelcache_log -from modelcache.manager import get_data_manager -from modelcache.manager.data_manager import DataManager - - -class Cache: - def __init__(self): - self.has_init = False - self.cache_enable_func = None - self.query_pre_embedding_func = None - self.insert_pre_embedding_func = None - self.mm_query_pre_embedding_func = None - self.mm_insert_pre_embedding_func = None - self.embedding_func = None - self.embedding_concurrent_func = None - self.data_manager: Optional[DataManager] = None - self.similarity_evaluation: Optional[SimilarityEvaluation] = None - self.post_process_messages_func = None - self.config = Config() - self.report = Report() - self.next_cache = None - - def init( - self, - cache_enable_func=cache_all, - query_pre_embedding_func=None, - insert_pre_embedding_func=None, - embedding_func=string_embedding, - data_manager: DataManager = get_data_manager(), - similarity_evaluation=ExactMatchEvaluation(), - post_process_messages_func=first, - config=Config(), - next_cache=None, - ): - self.has_init = True - self.cache_enable_func = cache_enable_func - self.query_pre_embedding_func = query_pre_embedding_func - self.insert_pre_embedding_func = insert_pre_embedding_func - self.embedding_func = embedding_func - self.data_manager: DataManager = data_manager - self.similarity_evaluation = similarity_evaluation - self.post_process_messages_func = post_process_messages_func - self.config = config - self.next_cache = next_cache - - @atexit.register - def close(): - try: - self.data_manager.close() - except Exception as e: - modelcache_log.error(e) - - def import_data(self, questions: List[Any], answers: List[Any]) -> None: - self.data_manager.import_data( - questions=questions, - answers=answers, - embedding_datas=[self.embedding_func(question) for question in questions], - ) - - def flush(self): - self.data_manager.flush() - if self.next_cache: - self.next_cache.data_manager.flush() - - -cache = Cache() diff --git a/modelcache/embedding/__init__.py b/modelcache/embedding/__init__.py index a7ab511..19f9b94 100644 --- a/modelcache/embedding/__init__.py +++ b/modelcache/embedding/__init__.py @@ -1,40 +1,2 @@ # -*- coding: utf-8 -*- -from modelcache.utils.lazy_import import LazyImport -huggingface = LazyImport("huggingface", globals(), "modelcache.embedding.huggingface") -data2vec = LazyImport("data2vec", globals(), "modelcache.embedding.data2vec") -llmEmb = LazyImport("llmEmb", globals(), "modelcache.embedding.llmEmb") -fasttext = LazyImport("fasttext", globals(), "modelcache.embedding.fasttext") -paddlenlp = LazyImport("paddlenlp", globals(), "modelcache.embedding.paddlenlp") -timm = LazyImport("timm", globals(), "modelcache.embedding.timm") -huggingface_tei = LazyImport("huggingface_tei", globals(), "modelcache.embedding.huggingface_tei") -bge_m3 = LazyImport("bge_m3", globals(), "modelcache.embedding.bge_m3") - - -def Huggingface(model="sentence-transformers/all-mpnet-base-v2"): - return huggingface.Huggingface(model) - - -def Data2VecAudio(model="model/text2vec-base-chinese/"): - return data2vec.Data2VecAudio(model) - - -def LlmEmb2vecAudio(): - return llmEmb.LlmEmb2Vec() - - -def FastText(model="en", dim=None): - return fasttext.FastText(model, dim) - - -def PaddleNLP(model="ernie-3.0-medium-zh"): - return paddlenlp.PaddleNLP(model) - - -def Timm(model="resnet50", device="default"): - return timm.Timm(model, device) - -def HuggingfaceTEI(base_url, model): - return huggingface_tei.HuggingfaceTEI(base_url, model) - -def BgeM3Embedding(model_path="model/bge-m3"): - return bge_m3.BgeM3Embedding(model_path) \ No newline at end of file +from modelcache.embedding.base import EmbeddingModel, MetricType, BaseEmbedding \ No newline at end of file diff --git a/modelcache/embedding/base.py b/modelcache/embedding/base.py index 4b316aa..3cd5936 100644 --- a/modelcache/embedding/base.py +++ b/modelcache/embedding/base.py @@ -1,5 +1,39 @@ # -*- coding: utf-8 -*- -from abc import ABCMeta, abstractmethod +from abc import abstractmethod, ABCMeta + +from modelcache.utils.lazy_import import LazyImport +from enum import Enum +huggingface = LazyImport("huggingface", globals(), "modelcache.embedding.huggingface") +data2vec = LazyImport("data2vec", globals(), "modelcache.embedding.data2vec") +llmEmb = LazyImport("llmEmb", globals(), "modelcache.embedding.llmEmb") +fasttext = LazyImport("fasttext", globals(), "modelcache.embedding.fasttext") +paddlenlp = LazyImport("paddlenlp", globals(), "modelcache.embedding.paddlenlp") +timm = LazyImport("timm", globals(), "modelcache.embedding.timm") +huggingface_tei = LazyImport("huggingface_tei", globals(), "modelcache.embedding.huggingface_tei") +bge_m3 = LazyImport("bge_m3", globals(), "modelcache.embedding.bge_m3") + +# define the embedding model enum +class EmbeddingModel(Enum): + """ + Enum for different embedding models. + """ + HUGGINGFACE = "huggingface" + DATA2VEC_AUDIO = "data2vec_audio" + LLM_EMB2VEC_AUDIO = "llmEmb2vec_audio" + FASTTEXT = "fasttext" + PADDLE_NLP = "paddlenlp" + TIMM = "timm" + HUGGINGFACE_TEI = "huggingface_tei" + BGE_M3 = "bge_m3" + + +class MetricType(Enum): + """ + Enum for different metric types used in similarity evaluation. + Different models may require different metrics for optimal performance. + """ + COSINE = "COSINE" + L2 = "L2" class BaseEmbedding(metaclass=ABCMeta): @@ -15,3 +49,52 @@ def to_embeddings(self, data, **kwargs): @abstractmethod def dimension(self) -> int: return 0 + + @staticmethod + def get(model:EmbeddingModel, **kwargs): + """ + Get the embedding model instance based on the specified model type. + :param model: The embedding model type. + :type model: EmbeddingModel + :param kwargs: Additional parameters for the model. + :return: An instance of the specified embedding model. + :rtype: BaseEmbedding + :raises ValueError: If the specified model type is not supported. + """ + if model == EmbeddingModel.HUGGINGFACE: + model_path = kwargs.pop("model_path","sentence-transformers/all-mpnet-base-v2") + return huggingface.Huggingface(model_path) + + elif model == EmbeddingModel.DATA2VEC_AUDIO: + model_path = kwargs.pop("model_path","model/text2vec-base-chinese/") + return data2vec.Data2VecAudio(model_path) + + elif model == EmbeddingModel.LLM_EMB2VEC_AUDIO: + return llmEmb.LlmEmb2Vec() + + elif model == EmbeddingModel.FASTTEXT: + model_path = kwargs.pop("model_path","en") + dim = kwargs.pop("dim", None) + return fasttext.FastText(model_path, dim) + + elif model == EmbeddingModel.PADDLE_NLP: + model_path = kwargs.pop("model_path", "ernie-3.0-medium-zh") + return paddlenlp.PaddleNLP(model_path) + + elif model == EmbeddingModel.TIMM: + model_path = kwargs.pop("model_path", "resnet50") + device = kwargs.pop("device", "default") + return timm.Timm(model_path, device) + + elif model == EmbeddingModel.HUGGINGFACE_TEI: + base_url = kwargs.pop("base_url") + model_path = kwargs.pop("model_path") + return huggingface_tei.HuggingfaceTEI(base_url, model_path) + + elif model == EmbeddingModel.BGE_M3: + model_path = kwargs.pop("model_path","model/bge-m3") + return bge_m3.BgeM3Embedding(model_path) + + else: + raise ValueError(f"Unsupported embedding model: {model}") + diff --git a/modelcache/embedding/huggingface.py b/modelcache/embedding/huggingface.py index 8c1434d..d48cb9d 100644 --- a/modelcache/embedding/huggingface.py +++ b/modelcache/embedding/huggingface.py @@ -1,34 +1,19 @@ # -*- coding: utf-8 -*- -import numpy as np - -from modelcache.utils import import_huggingface, import_torch from modelcache.embedding.base import BaseEmbedding - -import_torch() -import_huggingface() - -import torch # pylint: disable=C0413 -from transformers import AutoTokenizer, AutoModel # pylint: disable=C0413 - +from sentence_transformers import SentenceTransformer class Huggingface(BaseEmbedding): - def __init__(self, model: str = "sentence-transformers/all-MiniLM-L6-v2"): - self.model = AutoModel.from_pretrained(model, local_files_only=True) - self.model.eval() - - # self.tokenizer = AutoTokenizer.from_pretrained(model) - self.tokenizer = AutoTokenizer.from_pretrained(model, local_files_only=True) - if not self.tokenizer.pad_token: - self.tokenizer.pad_token = "[PAD]" + def __init__(self, model: str): + self.model = SentenceTransformer(model) try: self.__dimension = self.model.config.hidden_size - except Exception: # pylint: disable=W0703 - from transformers import AutoConfig # pylint: disable=C0415 + except Exception: + from transformers import AutoConfig config = AutoConfig.from_pretrained(model) self.__dimension = config.hidden_size - def to_embeddings(self, data, **_): + def to_embeddings(self, data: str, **_): """Generate embedding given text input :param data: text in string. @@ -36,24 +21,11 @@ def to_embeddings(self, data, **_): :return: a text embedding in shape of (dim,). """ - if not isinstance(data, list): - data = [data] - inputs = self.tokenizer( - data, padding=True, truncation=True, return_tensors="pt" - ) - outs = self.model(**inputs).last_hidden_state - emb = self.post_proc(outs, inputs).squeeze(0).detach().numpy() - return np.array(emb).astype("float32") - def post_proc(self, token_embeddings, inputs): - attention_mask = inputs["attention_mask"] - input_mask_expanded = ( - attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() - ) - sentence_embs = torch.sum( - token_embeddings * input_mask_expanded, 1 - ) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) - return sentence_embs + if not data: + raise ValueError("No data provided for embedding.") + embeddings = self.model.encode(data) + return embeddings[0] if len(data) == 1 else embeddings @property def dimension(self): diff --git a/modelcache/embedding/mpnet_base.py b/modelcache/embedding/mpnet_base.py deleted file mode 100644 index b272c5c..0000000 --- a/modelcache/embedding/mpnet_base.py +++ /dev/null @@ -1,17 +0,0 @@ -from sentence_transformers import SentenceTransformer - -class MPNet_Base: - def __init__(self): - self.dimension = 768 - self.model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2') - - def to_embeddings(self, *args, **kwargs): - if not args: - raise ValueError("No data provided for embedding.") - embeddings = self.model.encode(args) - return embeddings[0] if len(args) == 1 else embeddings - - def similarity(self, a, b): - if not a or not b: - raise ValueError("Both inputs must be non-empty for similarity calculation.") - return self.model.similarity(a, b) diff --git a/modelcache/embedding/string_text.py b/modelcache/embedding/string_text.py deleted file mode 100644 index 4fd08e7..0000000 --- a/modelcache/embedding/string_text.py +++ /dev/null @@ -1,5 +0,0 @@ -# -*- coding: utf-8 -*- - - -def to_embeddings(data, **_): - return data diff --git a/modelcache/manager/__init__.py b/modelcache/manager/__init__.py index 59a5ffe..40a96af 100644 --- a/modelcache/manager/__init__.py +++ b/modelcache/manager/__init__.py @@ -1,5 +1 @@ # -*- coding: utf-8 -*- -from modelcache.manager.scalar_data import CacheBase -from modelcache.manager.vector_data import VectorBase -from modelcache.manager.object_data import ObjectBase -from modelcache.manager.factory import get_data_manager diff --git a/modelcache/manager/data_manager.py b/modelcache/manager/data_manager.py index 3b20fd3..13d29b1 100644 --- a/modelcache/manager/data_manager.py +++ b/modelcache/manager/data_manager.py @@ -6,26 +6,15 @@ import numpy as np import cachetools from abc import abstractmethod, ABCMeta -from typing import List, Any, Optional, Union - -from numpy import ndarray - -from modelcache.manager.scalar_data.base import ( - CacheStorage, - CacheData, - DataType, - Answer, - Question -) +from typing import List, Any, Optional +from typing import Union, Callable +from modelcache.manager.scalar_data.base import CacheStorage,CacheData,DataType,Answer,Question from modelcache.utils.error import CacheError, ParamError -from modelcache.manager.vector_data.base import VectorBase, VectorData +from modelcache.manager.vector_data.base import VectorStorage, VectorData from modelcache.manager.object_data.base import ObjectBase -from modelcache.manager.eviction import EvictionBase -from modelcache.manager.eviction_manager import EvictionManager from modelcache.manager.eviction.memory_cache import MemoryCacheEviction from modelcache.utils.log import modelcache_log -NORMALIZE = True class DataManager(metaclass=ABCMeta): """DataManager manage the cache data, including save and search""" @@ -61,9 +50,11 @@ def search(self, embedding_data, **kwargs): def delete(self, id_list, **kwargs): pass + @abstractmethod def truncate(self, model_name): pass + @abstractmethod def flush(self): pass @@ -71,6 +62,30 @@ def flush(self): def close(self): pass + @staticmethod + def get( + cache_base: Union[CacheStorage, str] = None, + vector_base: Union[VectorStorage, str] = None, + object_base: Union[ObjectBase, str] = None, + max_size: int = 3, + clean_size: int = 1, + eviction: str = "ARC", + data_path: str = "data_map.txt", + get_data_container: Callable = None, + normalize: bool = True + ): + if not cache_base and not vector_base: + return MapDataManager(data_path, max_size, get_data_container) + + if isinstance(cache_base, str): + cache_base = CacheStorage.get(name=cache_base) + if isinstance(vector_base, str): + vector_base = VectorStorage.get(name=vector_base) + if isinstance(object_base, str): + object_base = ObjectBase.get(name=object_base) + assert cache_base and vector_base + return SSDataManager(cache_base, vector_base, object_base, max_size, clean_size,normalize, eviction) + class MapDataManager(DataManager): def __init__(self, data_path, max_size, get_data_container=None): @@ -149,10 +164,11 @@ class SSDataManager(DataManager): def __init__( self, s: CacheStorage, - v: VectorBase, + v: VectorStorage, o: Optional[ObjectBase], max_size, clean_size, + normalize: bool, policy="LRU", ): self.max_size = max_size @@ -160,6 +176,7 @@ def __init__( self.s = s self.v = v self.o = o + self.normalize = normalize # added self.eviction_base = MemoryCacheEviction( @@ -208,7 +225,7 @@ def import_data( raise ParamError("Make sure that all parameters have the same length") cache_datas = [] - if NORMALIZE: + if self.normalize: embedding_datas = [ normalize(embedding_data) for embedding_data in embedding_datas ] @@ -251,7 +268,7 @@ def hit_cache_callback(self, res_data, **kwargs): def search(self, embedding_data, **kwargs): model = kwargs.pop("model", None) - if NORMALIZE: + if self.normalize: embedding_data = normalize(embedding_data) top_k = kwargs.get("top_k", -1) return self.v.search(data=embedding_data, top_k=top_k, model=model) @@ -330,8 +347,3 @@ def close(self): self.s.close() self.v.close() - -# if __name__ == '__main__': -# from modelcache.manager import CacheBase, VectorBase, get_data_manager -# data_manager = get_data_manager(CacheBase('mysql'), VectorBase('milvus', dimension=128)) -# data_manager.save('hello', 'hi', np.random.random((128,)).astype('float32'), model='gptcode_6b') diff --git a/modelcache/manager/eviction/__init__.py b/modelcache/manager/eviction/__init__.py index 8ca7a3d..633f866 100644 --- a/modelcache/manager/eviction/__init__.py +++ b/modelcache/manager/eviction/__init__.py @@ -1,10 +1,2 @@ # -*- coding: utf-8 -*- -from modelcache.utils.lazy_import import LazyImport -eviction_manager = LazyImport( - "eviction_manager", globals(), "modelcache.manager.eviction.manager" -) - - -def EvictionBase(name: str, **kwargs): - return eviction_manager.EvictionBase.get(name, **kwargs) diff --git a/modelcache/manager/eviction/manager.py b/modelcache/manager/eviction/manager.py deleted file mode 100644 index 61579f0..0000000 --- a/modelcache/manager/eviction/manager.py +++ /dev/null @@ -1,25 +0,0 @@ -# -*- coding: utf-8 -*- -from typing import Callable, List, Any -from modelcache.utils.error import NotFoundError - - -class EvictionBase: - """ - EvictionBase to evict the cache data. - """ - - def __init__(self): - raise EnvironmentError( - "EvictionBase is designed to be instantiated, " - "please using the `EvictionBase.get(name, policy, maxsize, clean_size)`." - ) - - @staticmethod - def get(name: str, policy: str, maxsize: int, clean_size: int, on_evict: Callable[[List[Any]], None], **kwargs): - if name in "memory": - from modelcache.manager.eviction.memory_cache import MemoryCacheEviction - - eviction_base = MemoryCacheEviction(policy, maxsize, clean_size, on_evict, **kwargs) - else: - raise NotFoundError("eviction base", name) - return eviction_base diff --git a/modelcache/manager/eviction/memory_cache.py b/modelcache/manager/eviction/memory_cache.py index c6272f6..8561075 100644 --- a/modelcache/manager/eviction/memory_cache.py +++ b/modelcache/manager/eviction/memory_cache.py @@ -28,6 +28,8 @@ def __init__(self, policy: str, maxsize: int, clean_size: int, on_evict: Callabl self.kwargs = kwargs def create_cache(self, model: str): + + ################# Not integrated with on_evict yet ####################### if self._policy == "LRU": cache = cachetools.LRUCache(maxsize=self.maxsize, **self.kwargs) elif self._policy == "LFU": @@ -36,6 +38,8 @@ def create_cache(self, model: str): cache = cachetools.FIFOCache(maxsize=self.maxsize, **self.kwargs) elif self._policy == "RR": cache = cachetools.RRCache(maxsize=self.maxsize, **self.kwargs) + ########################################################################### + elif self._policy == "WTINYLFU": cache = W2TinyLFU(maxsize=self.maxsize, on_evict=lambda x: self.on_evict(x,model=model)) elif self._policy == "ARC": diff --git a/modelcache/manager/factory.py b/modelcache/manager/factory.py deleted file mode 100644 index b6d5b08..0000000 --- a/modelcache/manager/factory.py +++ /dev/null @@ -1,27 +0,0 @@ -# -*- coding: utf-8 -*- -from typing import Union, Callable -from modelcache.manager import CacheBase, VectorBase, ObjectBase -from modelcache.manager.data_manager import SSDataManager, MapDataManager - - -def get_data_manager( - cache_base: Union[CacheBase, str] = None, - vector_base: Union[VectorBase, str] = None, - object_base: Union[ObjectBase, str] = None, - max_size: int = 3, - clean_size: int = 1, - eviction: str = "ARC", - data_path: str = "data_map.txt", - get_data_container: Callable = None, -): - if not cache_base and not vector_base: - return MapDataManager(data_path, max_size, get_data_container) - - if isinstance(cache_base, str): - cache_base = CacheBase(name=cache_base) - if isinstance(vector_base, str): - vector_base = VectorBase(name=vector_base) - if isinstance(object_base, str): - object_base = ObjectBase(name=object_base) - assert cache_base and vector_base - return SSDataManager(cache_base, vector_base, object_base, max_size, clean_size, eviction) diff --git a/modelcache/manager/object_data/__init__.py b/modelcache/manager/object_data/__init__.py index f1186c0..633f866 100644 --- a/modelcache/manager/object_data/__init__.py +++ b/modelcache/manager/object_data/__init__.py @@ -1,9 +1,2 @@ # -*- coding: utf-8 -*- -from modelcache.utils.lazy_import import LazyImport -object_manager = LazyImport( - "object_manager", globals(), "modelcache.manager.object_data.manager" -) - -def ObjectBase(name: str, **kwargs): - return object_manager.ObjectBase.get(name, **kwargs) diff --git a/modelcache/manager/object_data/base.py b/modelcache/manager/object_data/base.py index 9ed00cf..41466f9 100644 --- a/modelcache/manager/object_data/base.py +++ b/modelcache/manager/object_data/base.py @@ -12,10 +12,6 @@ class ObjectBase(ABC): def put(self, obj: Any) -> str: pass - @abstractmethod - def get(self, obj: str) -> Any: - pass - @abstractmethod def get_access_link(self, obj: str) -> str: pass @@ -23,3 +19,7 @@ def get_access_link(self, obj: str) -> str: @abstractmethod def delete(self, to_delete: List[str]): pass + + @staticmethod + def get(name: str) -> Any: + pass diff --git a/modelcache/manager/scalar_data/__init__.py b/modelcache/manager/scalar_data/__init__.py index b63c430..40a96af 100644 --- a/modelcache/manager/scalar_data/__init__.py +++ b/modelcache/manager/scalar_data/__init__.py @@ -1,9 +1 @@ # -*- coding: utf-8 -*- -from modelcache.utils.lazy_import import LazyImport -scalar_manager = LazyImport( - "scalar_manager", globals(), "modelcache.manager.scalar_data.manager" -) - - -def CacheBase(name: str, **kwargs): - return scalar_manager.CacheBase.get(name, **kwargs) diff --git a/modelcache/manager/scalar_data/base.py b/modelcache/manager/scalar_data/base.py index 607db95..5ed898b 100644 --- a/modelcache/manager/scalar_data/base.py +++ b/modelcache/manager/scalar_data/base.py @@ -5,6 +5,9 @@ from enum import IntEnum import numpy as np +from modelcache.utils import import_sql_client +from modelcache.utils.error import NotFoundError + class DataType(IntEnum): STR = 0 @@ -107,7 +110,7 @@ def mark_deleted(self, keys): pass @abstractmethod - def model_deleted(self, model_name): + def model_deleted(self, model): pass @abstractmethod @@ -122,6 +125,7 @@ def get_ids(self, deleted=True): def count(self): pass + @abstractmethod def flush(self): pass @@ -133,3 +137,27 @@ def close(self): def batch_insert(self, all_data: List[CacheData]): pass + @abstractmethod + def update_hit_count_by_id(self, primary_id): + pass + + @staticmethod + def get(name, **kwargs): + if name in ["mysql", "oceanbase"]: + from modelcache.manager.scalar_data.sql_storage import SQLStorage + config = kwargs.get("config") + import_sql_client(name) + cache_base = SQLStorage(db_type=name, config=config) + elif name == 'sqlite': + SQL_URL = {"sqlite": "./sqlite.db"} + from modelcache.manager.scalar_data.sql_storage_sqlite import SQLStorage + sql_url = kwargs.get("sql_url", SQL_URL[name]) + cache_base = SQLStorage(db_type=name, url=sql_url) + elif name == 'elasticsearch': + from modelcache.manager.scalar_data.sql_storage_es import SQLStorage + config = kwargs.get("config") + cache_base = SQLStorage(db_type=name, config=config) + else: + raise NotFoundError("cache store", name) + return cache_base + diff --git a/modelcache/manager/scalar_data/manager.py b/modelcache/manager/scalar_data/manager.py deleted file mode 100644 index 8ff3aee..0000000 --- a/modelcache/manager/scalar_data/manager.py +++ /dev/null @@ -1,36 +0,0 @@ -# -*- coding: utf-8 -*- -from modelcache.utils import import_sql_client -from modelcache.utils.error import NotFoundError - -SQL_URL = {"sqlite": "./sqlite.db"} - - -class CacheBase: - """ - CacheBase to manager the cache storage. - """ - - def __init__(self): - raise EnvironmentError( - "CacheBase is designed to be instantiated, please using the `CacheBase.get(name)`." - ) - - @staticmethod - def get(name, **kwargs): - - if name in ["mysql", "oceanbase"]: - from modelcache.manager.scalar_data.sql_storage import SQLStorage - config = kwargs.get("config") - import_sql_client(name) - cache_base = SQLStorage(db_type=name, config=config) - elif name == 'sqlite': - from modelcache.manager.scalar_data.sql_storage_sqlite import SQLStorage - sql_url = kwargs.get("sql_url", SQL_URL[name]) - cache_base = SQLStorage(db_type=name, url=sql_url) - elif name == 'elasticsearch': - from modelcache.manager.scalar_data.sql_storage_es import SQLStorage - config = kwargs.get("config") - cache_base = SQLStorage(db_type=name, config=config) - else: - raise NotFoundError("cache store", name) - return cache_base diff --git a/modelcache/manager/scalar_data/sql_storage.py b/modelcache/manager/scalar_data/sql_storage.py index c339285..c24a024 100644 --- a/modelcache/manager/scalar_data/sql_storage.py +++ b/modelcache/manager/scalar_data/sql_storage.py @@ -12,6 +12,7 @@ class SQLStorage(CacheStorage): + def __init__( self, db_type: str = "mysql", @@ -280,3 +281,6 @@ def close(self): def count_answers(self): pass + + def flush(self): + pass \ No newline at end of file diff --git a/modelcache/manager/scalar_data/sql_storage_es.py b/modelcache/manager/scalar_data/sql_storage_es.py index 7e0184a..3787623 100644 --- a/modelcache/manager/scalar_data/sql_storage_es.py +++ b/modelcache/manager/scalar_data/sql_storage_es.py @@ -8,6 +8,7 @@ class SQLStorage(CacheStorage): + def __init__( self, db_type: str = "elasticsearch", @@ -193,3 +194,6 @@ def count_answers(self): query = {"query": {"match_all": {}}} response = self.client.count(index=self.ans_index, body=query) return response["count"] + + def flush(self): + pass diff --git a/modelcache/manager/scalar_data/sql_storage_sqlite.py b/modelcache/manager/scalar_data/sql_storage_sqlite.py index b7463c5..3b69979 100644 --- a/modelcache/manager/scalar_data/sql_storage_sqlite.py +++ b/modelcache/manager/scalar_data/sql_storage_sqlite.py @@ -199,3 +199,6 @@ def close(self): def count_answers(self): pass + + def flush(self): + pass diff --git a/modelcache/manager/vector_data/__init__.py b/modelcache/manager/vector_data/__init__.py index fdb98cb..40a96af 100644 --- a/modelcache/manager/vector_data/__init__.py +++ b/modelcache/manager/vector_data/__init__.py @@ -1,10 +1 @@ # -*- coding: utf-8 -*- -from modelcache.utils.lazy_import import LazyImport - -vector_manager = LazyImport( - "vector_manager", globals(), "modelcache.manager.vector_data.manager" -) - - -def VectorBase(name: str, **kwargs): - return vector_manager.VectorBase.get(name, **kwargs) diff --git a/modelcache/manager/vector_data/base.py b/modelcache/manager/vector_data/base.py index 7bdf12a..6742c94 100644 --- a/modelcache/manager/vector_data/base.py +++ b/modelcache/manager/vector_data/base.py @@ -4,6 +4,19 @@ from typing import List from dataclasses import dataclass +from modelcache.embedding import MetricType +from modelcache.utils.error import ParamError, NotFoundError + +TOP_K = 1 +FAISS_INDEX_PATH = "faiss.index" +DIMENSION = 0 +MILVUS_HOST = "localhost" +MILVUS_PORT = 19530 +MILVUS_USER = "" +MILVUS_PSW = "" +MILVUS_SECURE = False + +COLLECTION_NAME = "modelcache" @dataclass class VectorData: @@ -11,8 +24,7 @@ class VectorData: data: np.ndarray -class VectorBase(ABC): - """VectorBase: base vector store interface""" +class VectorStorage(ABC): @abstractmethod def mul_add(self, datas: List[VectorData], model=None): @@ -34,8 +46,107 @@ def delete(self, ids) -> bool: def rebuild_col(self, model): pass + @abstractmethod def flush(self): pass + @abstractmethod def close(self): pass + + @staticmethod + def get(name, **kwargs): + top_k = kwargs.get("top_k", TOP_K) + if name == "milvus": + from modelcache.manager.vector_data.milvus import Milvus + dimension = kwargs.get("dimension", DIMENSION) + milvus_config = kwargs.get("config") + check_dimension(dimension) + host = milvus_config.get('milvus', 'host') + port = milvus_config.get('milvus', 'port') + user = milvus_config.get('milvus', 'user') + password = milvus_config.get('milvus', 'password') + + metric_type = kwargs.get("metric_type",MetricType.COSINE) + secure = kwargs.get("secure", MILVUS_SECURE) + collection_name = kwargs.get("collection_name", COLLECTION_NAME) + index_params = kwargs.get("index_params", None) + search_params = kwargs.get("search_params", None) + local_mode = kwargs.get("local_mode", False) + local_data = kwargs.get("local_data", "./milvus_data") + vector_base = Milvus( + host=host, + port=port, + user=user, + password=password, + secure=secure, + collection_name=collection_name, + dimension=dimension, + top_k=top_k, + index_params=index_params, + search_params=search_params, + local_mode=local_mode, + local_data=local_data, + metric_type=metric_type + ) + elif name == "redis": + from modelcache.manager.vector_data.redis import RedisVectorStore + dimension = kwargs.get("dimension", DIMENSION) + check_dimension(dimension) + + redis_config = kwargs.get("config") + host = redis_config.get('redis', 'host') + port = redis_config.get('redis', 'port') + user = redis_config.get('redis', 'user') + password = redis_config.get('redis', 'password') + namespace = kwargs.get("namespace", "") + # collection_name = kwargs.get("collection_name", COLLECTION_NAME) + + vector_base = RedisVectorStore( + host=host, + port=port, + username=user, + password=password, + namespace=namespace, + top_k=top_k, + dimension=dimension, + ) + elif name == "faiss": + from modelcache.manager.vector_data.faiss import Faiss + + dimension = kwargs.get("dimension", DIMENSION) + index_path = kwargs.pop("index_path", FAISS_INDEX_PATH) + check_dimension(dimension) + vector_base = Faiss( + index_file_path=index_path, dimension=dimension, top_k=top_k + ) + elif name == "chromadb": + from modelcache.manager.vector_data.chroma import Chromadb + + chromadb_config = kwargs.get("config", None) + persist_directory = chromadb_config.get('chromadb','persist_directory') + + vector_base = Chromadb( + persist_directory=persist_directory, + top_k=top_k, + ) + elif name == "hnswlib": + from modelcache.manager.vector_data.hnswlib_store import Hnswlib + + dimension = kwargs.get("dimension", DIMENSION) + index_path = kwargs.pop("index_path", "./hnswlib_index.bin") + max_elements = kwargs.pop("max_elements", 100000) + VectorStorage.check_dimension(dimension) + vector_base = Hnswlib( + index_file_path=index_path, dimension=dimension, + top_k=top_k, max_elements=max_elements + ) + else: + raise NotFoundError("vector store", name) + return vector_base + + +def check_dimension(dimension): + if dimension <= 0: + raise ParamError(f"the dimension should be greater than zero, current value: {dimension}.") + diff --git a/modelcache/manager/vector_data/chroma.py b/modelcache/manager/vector_data/chroma.py index 446b354..b92ef3c 100644 --- a/modelcache/manager/vector_data/chroma.py +++ b/modelcache/manager/vector_data/chroma.py @@ -2,7 +2,7 @@ import numpy as np import logging -from modelcache.manager.vector_data.base import VectorBase, VectorData +from modelcache.manager.vector_data.base import VectorStorage, VectorData from modelcache.utils import import_chromadb, import_torch import_torch() @@ -11,7 +11,7 @@ import chromadb -class Chromadb(VectorBase): +class Chromadb(VectorStorage): def __init__( self, diff --git a/modelcache/manager/vector_data/faiss.py b/modelcache/manager/vector_data/faiss.py index 0f8445c..77e7617 100644 --- a/modelcache/manager/vector_data/faiss.py +++ b/modelcache/manager/vector_data/faiss.py @@ -2,13 +2,13 @@ import os from typing import List import numpy as np -from modelcache.manager.vector_data.base import VectorBase, VectorData +from modelcache.manager.vector_data.base import VectorStorage, VectorData from modelcache.utils import import_faiss import_faiss() import faiss # pylint: disable=C0413 -class Faiss(VectorBase): +class Faiss(VectorStorage): def __init__(self, index_file_path, dimension, top_k): self._index_file_path = index_file_path self._dimension = dimension diff --git a/modelcache/manager/vector_data/manager.py b/modelcache/manager/vector_data/manager.py deleted file mode 100644 index 2167fbc..0000000 --- a/modelcache/manager/vector_data/manager.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- coding: utf-8 -*- -from modelcache.utils.error import NotFoundError, ParamError - -TOP_K = 1 -FAISS_INDEX_PATH = "faiss.index" -DIMENSION = 0 -MILVUS_HOST = "localhost" -MILVUS_PORT = 19530 -MILVUS_USER = "" -MILVUS_PSW = "" -MILVUS_SECURE = False -MILVUS_INDEX_PARAMS = { - "metric_type": "L2", - "index_type": "HNSW", - "params": {"M": 8, "efConstruction": 64}, -} - -COLLECTION_NAME = "modelcache" - -MPNet_base = False # whether to use MPNet base model for embedding, if True, will use cosine similarity evaluation in milvus - - -class VectorBase: - """ - VectorBase to manager the vector base. - """ - - def __init__(self): - raise EnvironmentError( - "VectorBase is designed to be instantiated, please using the `VectorBase.get(name)`." - ) - - @staticmethod - def check_dimension(dimension): - if dimension <= 0: - raise ParamError( - f"the dimension should be greater than zero, current value: {dimension}." - ) - - @staticmethod - def get(name, **kwargs): - top_k = kwargs.get("top_k", TOP_K) - if name == "milvus": - from modelcache.manager.vector_data.milvus import Milvus - dimension = kwargs.get("dimension", DIMENSION) - milvus_config = kwargs.get("milvus_config") - VectorBase.check_dimension(dimension) - host = milvus_config.get('milvus', 'host') - port = milvus_config.get('milvus', 'port') - user = milvus_config.get('milvus', 'user') - password = milvus_config.get('milvus', 'password') - - secure = kwargs.get("secure", MILVUS_SECURE) - collection_name = kwargs.get("collection_name", COLLECTION_NAME) - index_params = kwargs.get("index_params", MILVUS_INDEX_PARAMS) - search_params = kwargs.get("search_params", None) - local_mode = kwargs.get("local_mode", False) - local_data = kwargs.get("local_data", "./milvus_data") - vector_base = Milvus( - host=host, - port=port, - user=user, - password=password, - secure=secure, - collection_name=collection_name, - dimension=dimension, - top_k=top_k, - index_params=index_params, - search_params=search_params, - local_mode=local_mode, - local_data=local_data - ) - elif name == "redis": - from modelcache.manager.vector_data.redis import RedisVectorStore - dimension = kwargs.get("dimension", DIMENSION) - VectorBase.check_dimension(dimension) - - redis_config = kwargs.get("redis_config") - host = redis_config.get('redis', 'host') - port = redis_config.get('redis', 'port') - user = redis_config.get('redis', 'user') - password = redis_config.get('redis', 'password') - namespace = kwargs.get("namespace", "") - # collection_name = kwargs.get("collection_name", COLLECTION_NAME) - - vector_base = RedisVectorStore( - host=host, - port=port, - username=user, - password=password, - namespace=namespace, - top_k=top_k, - dimension=dimension, - ) - elif name == "faiss": - from modelcache.manager.vector_data.faiss import Faiss - - dimension = kwargs.get("dimension", DIMENSION) - index_path = kwargs.pop("index_path", FAISS_INDEX_PATH) - VectorBase.check_dimension(dimension) - vector_base = Faiss( - index_file_path=index_path, dimension=dimension, top_k=top_k - ) - elif name == "chromadb": - from modelcache.manager.vector_data.chroma import Chromadb - - chromadb_config = kwargs.get("chromadb_config", None) - persist_directory = chromadb_config.get('chromadb','persist_directory') - - vector_base = Chromadb( - persist_directory=persist_directory, - top_k=top_k, - ) - elif name == "hnswlib": - from modelcache.manager.vector_data.hnswlib_store import Hnswlib - - dimension = kwargs.get("dimension", DIMENSION) - index_path = kwargs.pop("index_path", "./hnswlib_index.bin") - max_elements = kwargs.pop("max_elements", 100000) - VectorBase.check_dimension(dimension) - vector_base = Hnswlib( - index_file_path=index_path, dimension=dimension, - top_k=top_k, max_elements=max_elements - ) - else: - raise NotFoundError("vector store", name) - return vector_base diff --git a/modelcache/manager/vector_data/milvus.py b/modelcache/manager/vector_data/milvus.py index 1d04430..f5c7682 100644 --- a/modelcache/manager/vector_data/milvus.py +++ b/modelcache/manager/vector_data/milvus.py @@ -3,9 +3,11 @@ from typing import List from uuid import uuid4 import numpy as np + +from modelcache.embedding import MetricType from modelcache.utils import import_pymilvus from modelcache.utils.log import modelcache_log -from modelcache.manager.vector_data.base import VectorBase, VectorData +from modelcache.manager.vector_data.base import VectorStorage, VectorData import_pymilvus() @@ -21,19 +23,7 @@ ) -class Milvus(VectorBase): - SEARCH_PARAM = { - "IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}}, - "IVF_SQ8": {"metric_type": "L2", "params": {"nprobe": 10}}, - "IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}}, - "HNSW": {"metric_type": "L2", "params": {"ef": 10}}, - "RHNSW_FLAT": {"metric_type": "L2", "params": {"ef": 10}}, - "RHNSW_SQ": {"metric_type": "L2", "params": {"ef": 10}}, - "RHNSW_PQ": {"metric_type": "L2", "params": {"ef": 10}}, - "IVF_HNSW": {"metric_type": "L2", "params": {"nprobe": 10, "ef": 10}}, - "ANNOY": {"metric_type": "L2", "params": {"search_k": 10}}, - "AUTOINDEX": {"metric_type": "L2", "params": {}}, - } +class Milvus(VectorStorage): def __init__( self, @@ -48,7 +38,8 @@ def __init__( index_params: dict = None, search_params: dict = None, local_mode: bool = False, - local_data: str = "./milvus_data" + local_data: str = "./milvus_data", + metric_type: MetricType = MetricType.COSINE, ): if dimension <= 0: raise ValueError( @@ -58,14 +49,27 @@ def __init__( self._local_data = local_data self.dimension = dimension self.top_k = top_k - self.index_params = index_params if self._local_mode: self._create_local(port, local_data) self._connect(host, port, user, password, secure) self.collection_name = collection_name - self.search_params = ( - search_params or self.SEARCH_PARAM[self.index_params["index_type"]] - ) + self.search_params = { + "IVF_FLAT": {"metric_type": metric_type.value, "params": {"nprobe": 10}}, + "IVF_SQ8": {"metric_type": metric_type.value, "params": {"nprobe": 10}}, + "IVF_PQ": {"metric_type": metric_type.value, "params": {"nprobe": 10}}, + "HNSW": {"metric_type": metric_type.value, "params": {"ef": 10}}, + "RHNSW_FLAT": {"metric_type": metric_type.value, "params": {"ef": 10}}, + "RHNSW_SQ": {"metric_type": metric_type.value, "params": {"ef": 10}}, + "RHNSW_PQ": {"metric_type": metric_type.value, "params": {"ef": 10}}, + "IVF_HNSW": {"metric_type": metric_type.value, "params": {"nprobe": 10, "ef": 10}}, + "ANNOY": {"metric_type": metric_type.value, "params": {"search_k": 10}}, + "AUTOINDEX": {"metric_type": metric_type.value, "params": {}}, + } + self.index_params ={ + "metric_type": metric_type.value, + "index_type": "HNSW", + "params": {"M": 16, "efConstruction": 64}, + } self.collections = dict() diff --git a/modelcache/manager/vector_data/redis.py b/modelcache/manager/vector_data/redis.py index afa1088..6e51511 100644 --- a/modelcache/manager/vector_data/redis.py +++ b/modelcache/manager/vector_data/redis.py @@ -6,7 +6,7 @@ from redis.commands.search.field import TagField, VectorField, NumericField from redis.client import Redis -from modelcache.manager.vector_data.base import VectorBase, VectorData +from modelcache.manager.vector_data.base import VectorStorage, VectorData from modelcache.utils import import_redis from modelcache.utils.log import modelcache_log from modelcache.utils.index_util import get_index_name @@ -14,7 +14,7 @@ import_redis() -class RedisVectorStore(VectorBase): +class RedisVectorStore(VectorStorage): def __init__( self, host: str = "localhost", diff --git a/modelcache/similarity_evaluation/__init__.py b/modelcache/similarity_evaluation/__init__.py index 22dff8f..633f866 100644 --- a/modelcache/similarity_evaluation/__init__.py +++ b/modelcache/similarity_evaluation/__init__.py @@ -1,11 +1,2 @@ # -*- coding: utf-8 -*- -from modelcache.similarity_evaluation.similarity_evaluation import SimilarityEvaluation -from modelcache.utils.lazy_import import LazyImport -exact_match = LazyImport( - "exact_match", globals(), "modelcache.similarity_evaluation.exact_match" -) - - -def ExactMatchEvaluation(): - return exact_match.ExactMatchEvaluation() diff --git a/modelcache/similarity_evaluation/similarity_evaluation.py b/modelcache/similarity_evaluation/base.py similarity index 100% rename from modelcache/similarity_evaluation/similarity_evaluation.py rename to modelcache/similarity_evaluation/base.py diff --git a/modelcache/similarity_evaluation/distance.py b/modelcache/similarity_evaluation/distance.py index 44ca595..281bcd0 100644 --- a/modelcache/similarity_evaluation/distance.py +++ b/modelcache/similarity_evaluation/distance.py @@ -1,7 +1,6 @@ # -*- coding: utf-8 -*- from typing import Tuple, Dict, Any -from modelcache.similarity_evaluation import SimilarityEvaluation - +from modelcache.similarity_evaluation.base import SimilarityEvaluation class SearchDistanceEvaluation(SimilarityEvaluation): def __init__(self, max_distance=4.0, positive=False): diff --git a/modelcache/similarity_evaluation/exact_match.py b/modelcache/similarity_evaluation/exact_match.py index 553bd59..e207387 100644 --- a/modelcache/similarity_evaluation/exact_match.py +++ b/modelcache/similarity_evaluation/exact_match.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- from typing import Tuple, Dict, Any -from modelcache.similarity_evaluation.similarity_evaluation import SimilarityEvaluation +from modelcache.similarity_evaluation.base import SimilarityEvaluation class ExactMatchEvaluation(SimilarityEvaluation): diff --git a/modelcache/utils/cache_func.py b/modelcache/utils/cache_func.py deleted file mode 100644 index 461f542..0000000 --- a/modelcache/utils/cache_func.py +++ /dev/null @@ -1,3 +0,0 @@ -# -*- coding: utf-8 -*- -def cache_all(*_, **__): - return True \ No newline at end of file diff --git a/modelcache/utils/time.py b/modelcache/utils/time.py index 7074b58..569af99 100644 --- a/modelcache/utils/time.py +++ b/modelcache/utils/time.py @@ -1,15 +1,14 @@ # -*- coding: utf-8 -*- import time -from modelcache import cache - -def time_cal(func, func_name=None, report_func=None): +def time_cal(func, func_name=None, report_func=None, **kwargs): + cache = kwargs.pop("cache_obj") def inner(*args, **kwargs): time_start = time.time() res = func(*args, **kwargs) delta_time = time.time() - time_start - if cache.config.log_time_func: - cache.config.log_time_func( + if cache.log_time_func: + cache.log_time_func( func.__name__ if func_name is None else func_name, delta_time ) if report_func is not None: From 4a20eafce8a11aee559f2695b9fe2c13ac676588 Mon Sep 17 00:00:00 2001 From: Yuval Roth Date: Wed, 11 Jun 2025 02:45:13 +0300 Subject: [PATCH 29/36] New feature: websocket4modelcache A websocket-based API for the ModelCache system. The goal of this API is to save the overhead of creating a new http connection for every request and allow faster querying --- requirements.txt | 1 + websocket4modelcache.py | 45 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 46 insertions(+) create mode 100644 websocket4modelcache.py diff --git a/requirements.txt b/requirements.txt index a84a988..19db268 100644 --- a/requirements.txt +++ b/requirements.txt @@ -20,3 +20,4 @@ snowflake-id==1.0.2 flagembedding==1.3.4 cryptography==45.0.2 sentence-transformers==4.1.0 +websockets==15.0.1 diff --git a/websocket4modelcache.py b/websocket4modelcache.py new file mode 100644 index 0000000..4541a91 --- /dev/null +++ b/websocket4modelcache.py @@ -0,0 +1,45 @@ +# -*- coding: utf-8 -*- +import asyncio +import websockets +import json +from modelcache.cache import Cache + +# Initialize the cache +cache = Cache.init("mysql", "milvus") + + +async def handle_client(websocket): + async for message in websocket: + # Parse JSON + try: + param_dict = json.loads(message) + except json.JSONDecodeError: + await websocket.send(json.dumps({"errorCode": 400, "errorDesc": "bad request"})) + continue + + request_id = param_dict.get("requestId") + request_payload = param_dict.get("payload") + if not request_id or not request_payload: + await websocket.send(json.dumps({"errorCode": 400, "errorDesc": "bad request"})) + continue + asyncio.create_task(process_and_respond(websocket, request_id, request_payload)) + + +async def process_and_respond(websocket,request_id, request_payload): + try: + result = cache.handle_request(request_payload) + await websocket.send(json.dumps({"requestId": request_id,"result": result})) + except Exception as e: + error_result = {"errorCode": 102, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', + "answer": ''} + cache.save_query_resp(error_result, model='', query='', delta_time=0) + await websocket.send(json.dumps(error_result)) + + +async def main(): + print("WebSocket server starting on ws://0.0.0.0:5000") + async with websockets.serve(handle_client, "0.0.0.0", 5000): + await asyncio.Future() # Run forever + +if __name__ == "__main__": + asyncio.run(main()) From a37f92e87f57c98adfced0d2003e2fb4a36502a1 Mon Sep 17 00:00:00 2001 From: Yuval Roth Date: Thu, 12 Jun 2025 01:51:15 +0300 Subject: [PATCH 30/36] New feature: Embedding Dispatcher for parallel embedding Based on the multiprocessing module, provides true parallel embedding. The number of workers can be adjusted in the cache initialization. Access to the caching logic is now done asynchronously using asyncio module --- fastapi4modelcache.py | 75 +++++++----------- fastapi4modelcache_demo.py | 75 +++++++----------- flask4modelcache.py | 58 ++++++++------ flask4modelcache_demo.py | 58 ++++++++------ model/text2vec-base-chinese/logs.txt | 20 ++++- modelcache/adapter/adapter.py | 8 +- modelcache/adapter/adapter_insert.py | 13 ++- modelcache/adapter/adapter_query.py | 4 +- modelcache/cache.py | 52 +++++++----- modelcache/embedding/base.py | 21 ++--- modelcache/embedding/embedding_dispatcher.py | 71 +++++++++++++++++ modelcache/embedding/huggingface.py | 4 +- requirements.txt | 5 +- websocket4modelcache.py | 83 +++++++++++--------- websocket4modelcache_demo.py | 58 ++++++++++++++ 15 files changed, 393 insertions(+), 212 deletions(-) create mode 100644 modelcache/embedding/embedding_dispatcher.py create mode 100644 websocket4modelcache_demo.py diff --git a/fastapi4modelcache.py b/fastapi4modelcache.py index d3d39bf..b260eae 100644 --- a/fastapi4modelcache.py +++ b/fastapi4modelcache.py @@ -1,61 +1,46 @@ # -*- coding: utf-8 -*- +import asyncio +from contextlib import asynccontextmanager import uvicorn import json -from fastapi import FastAPI, Request, HTTPException +from fastapi.responses import JSONResponse +from fastapi import FastAPI, Request from modelcache.cache import Cache - -#创建一个FastAPI实例 -app = FastAPI() - -cache = Cache.init("mysql", "milvus") +from modelcache.embedding import EmbeddingModel + +@asynccontextmanager +async def lifespan(app: FastAPI): + global cache + cache, _ = await Cache.init( + sql_storage="mysql", + vector_storage="milvus", + embedding_model=EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2, + embedding_workers_num=2 + ) + yield + +app = FastAPI(lifespan=lifespan) +cache: Cache = None @app.get("/welcome") async def first_fastapi(): return "hello, modelcache!" - @app.post("/modelcache") async def user_backend(request: Request): - try: - raw_body = await request.body() - # 解析字符串为JSON对象 - if isinstance(raw_body, bytes): - raw_body = raw_body.decode("utf-8") - if isinstance(raw_body, str): - try: - # 尝试将字符串解析为JSON对象 - request_data = json.loads(raw_body) - except json.JSONDecodeError as e: - # 如果无法解析，返回格式错误 - result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', - "answer": ''} - cache.save_query_info(result, model='', query='', delta_time_log=0) - raise HTTPException(status_code=101, detail="Invalid JSON format") - else: - request_data = raw_body - # 确保request_data是字典对象 - if isinstance(request_data, str): - try: - request_data = json.loads(request_data) - except json.JSONDecodeError: - raise HTTPException(status_code=101, detail="Invalid JSON format") - - return cache.handle_request(request_data) + try: + request_data = await request.json() + except Exception: + result = {"errorCode": 400, "errorDesc": "bad request", "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} + return JSONResponse(status_code=400, content=result) + try: + return await cache.handle_request(request_data) except Exception as e: - request_data = raw_body if 'raw_body' in locals() else None - result = { - "errorCode": 103, - "errorDesc": str(e), - "cacheHit": False, - "delta_time": 0, - "hit_query": '', - "answer": '', - "para_dict": request_data - } - return result + result = {"errorCode": 500, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} + cache.save_query_resp(result, model='', query='', delta_time=0) + return JSONResponse(status_code=500, content=result) -# TODO: 可以修改为在命令行中使用`uvicorn your_module_name:app --host 0.0.0.0 --port 5000 --reload`的命令启动 if __name__ == '__main__': - uvicorn.run(app, host='0.0.0.0', port=5000) \ No newline at end of file + uvicorn.run(app, host='0.0.0.0', port=5000, loop="asyncio", http="httptools") diff --git a/fastapi4modelcache_demo.py b/fastapi4modelcache_demo.py index 84058fa..c1f6a3f 100644 --- a/fastapi4modelcache_demo.py +++ b/fastapi4modelcache_demo.py @@ -1,14 +1,26 @@ # -*- coding: utf-8 -*- +import asyncio +from contextlib import asynccontextmanager import uvicorn import json -from fastapi import FastAPI, Request, HTTPException - +from fastapi.responses import JSONResponse +from fastapi import FastAPI, Request from modelcache.cache import Cache - -# 创建一个FastAPI实例 -app = FastAPI() - -cache = Cache.init("sqlite", "faiss") +from modelcache.embedding import EmbeddingModel + +@asynccontextmanager +async def lifespan(app: FastAPI): + global cache + cache, _ = await Cache.init( + sql_storage="sqlite", + vector_storage="faiss", + embedding_model=EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2, + embedding_workers_num=2 + ) + yield + +app = FastAPI(lifespan=lifespan) +cache: Cache = None @app.get("/welcome") async def first_fastapi(): @@ -16,46 +28,19 @@ async def first_fastapi(): @app.post("/modelcache") async def user_backend(request: Request): - try: - raw_body = await request.body() - # 解析字符串为JSON对象 - if isinstance(raw_body, bytes): - raw_body = raw_body.decode("utf-8") - if isinstance(raw_body, str): - try: - # 尝试将字符串解析为JSON对象 - request_data = json.loads(raw_body) - except json.JSONDecodeError as e: - # 如果无法解析，返回格式错误 - result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', - "answer": ''} - cache.save_query_info(result, model='', query='', delta_time_log=0) - raise HTTPException(status_code=101, detail="Invalid JSON format") - else: - request_data = raw_body - # 确保request_data是字典对象 - if isinstance(request_data, str): - try: - request_data = json.loads(request_data) - except json.JSONDecodeError: - raise HTTPException(status_code=101, detail="Invalid JSON format") - - return cache.handle_request(request_data) + try: + request_data = await request.json() + except Exception: + result = {"errorCode": 400, "errorDesc": "bad request", "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} + return JSONResponse(status_code=400, content=result) + try: + return await cache.handle_request(request_data) except Exception as e: - request_data = raw_body if 'raw_body' in locals() else None - result = { - "errorCode": 103, - "errorDesc": str(e), - "cacheHit": False, - "delta_time": 0, - "hit_query": '', - "answer": '', - "para_dict": request_data - } - return result + result = {"errorCode": 500, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} + cache.save_query_resp(result, model='', query='', delta_time=0) + return JSONResponse(status_code=500, content=result) -# TODO: 可以修改为在命令行中使用`uvicorn your_module_name:app --host 0.0.0.0 --port 5000 --reload`的命令启动 if __name__ == '__main__': - uvicorn.run(app, host='0.0.0.0', port=5000) \ No newline at end of file + uvicorn.run(app, host='0.0.0.0', port=5000, loop="asyncio", http="httptools") diff --git a/flask4modelcache.py b/flask4modelcache.py index 0ca949d..3d18518 100644 --- a/flask4modelcache.py +++ b/flask4modelcache.py @@ -1,34 +1,48 @@ # -*- coding: utf-8 -*- -from flask import Flask, request -import json +import asyncio + +from flask import Flask, request, jsonify from modelcache.cache import Cache +from modelcache.embedding import EmbeddingModel + -# 创建一个Flask实例 -app = Flask(__name__) +async def main(): -cache = Cache.init("mysql","milvus") + # 创建一个Flask实例 + app = Flask(__name__) -@app.route('/welcome') -def first_flask(): # 视图函数 - return 'hello, modelcache!' + cache,loop = await Cache.init( + sql_storage="mysql", + vector_storage="milvus", + embedding_model=EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2, + embedding_workers_num=2 + ) + @app.route('/welcome') + def first_flask(): # 视图函数 + return 'hello, modelcache!' -@app.route('/modelcache', methods=['GET', 'POST']) -def user_backend(): - param_dict = {} - try: - if request.method == 'POST': + + @app.post('/modelcache') + def user_backend(): + try: param_dict = request.json - elif request.method == 'GET': - param_dict = request.args + except Exception: + result = {"errorCode": 400, "errorDesc": "bad request", "cacheHit": False, "delta_time": 0, "hit_query": '',"answer": ''} + return jsonify(result), 400 + + try: + result = asyncio.run_coroutine_threadsafe( + cache.handle_request(param_dict), loop + ).result() + return jsonify(result), 200 + except Exception as e: + result = {"errorCode": 500, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '',"answer": ''} + cache.save_query_resp(result, model='', query='', delta_time=0) + return jsonify(result), 500 - return json.dumps(cache.handle_request(param_dict)) - except Exception as e: - result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', - "answer": ''} - cache.save_query_resp(result, model='', query='', delta_time=0) - return json.dumps(result) + await asyncio.to_thread(app.run, host='0.0.0.0', port=5000) if __name__ == '__main__': - app.run(host='0.0.0.0', port=5000) + asyncio.run(main()) diff --git a/flask4modelcache_demo.py b/flask4modelcache_demo.py index e0d5cfb..4251f94 100644 --- a/flask4modelcache_demo.py +++ b/flask4modelcache_demo.py @@ -1,34 +1,48 @@ # -*- coding: utf-8 -*- -from flask import Flask, request -import json +import asyncio + +from flask import Flask, request, jsonify from modelcache.cache import Cache +from modelcache.embedding import EmbeddingModel + -# 创建一个Flask实例 -app = Flask(__name__) +async def main(): -cache = Cache.init("sqlite","faiss") + # 创建一个Flask实例 + app = Flask(__name__) -@app.route('/welcome') -def first_flask(): # 视图函数 - return 'hello, modelcache!' + cache,loop = await Cache.init( + sql_storage="sqlite", + vector_storage="faiss", + embedding_model=EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2, + embedding_workers_num=2 + ) + @app.route('/welcome') + def first_flask(): # 视图函数 + return 'hello, modelcache!' -@app.route('/modelcache', methods=['GET', 'POST']) -def user_backend(): - param_dict = {} - try: - if request.method == 'POST': + + @app.post('/modelcache') + def user_backend(): + try: param_dict = request.json - elif request.method == 'GET': - param_dict = request.args + except Exception: + result = {"errorCode": 400, "errorDesc": "bad request", "cacheHit": False, "delta_time": 0, "hit_query": '',"answer": ''} + return jsonify(result), 400 + + try: + result = asyncio.run_coroutine_threadsafe( + cache.handle_request(param_dict), loop + ).result() + return jsonify(result), 200 + except Exception as e: + result = {"errorCode": 500, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '',"answer": ''} + cache.save_query_resp(result, model='', query='', delta_time=0) + return jsonify(result), 500 - return json.dumps(cache.handle_request(param_dict)) - except Exception as e: - result = {"errorCode": 101, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', - "answer": ''} - cache.save_query_resp(result, model='', query='', delta_time=0) - return json.dumps(result) + await asyncio.to_thread(app.run, host='0.0.0.0', port=5000) if __name__ == '__main__': - app.run(host='0.0.0.0', port=5000) + asyncio.run(main()) diff --git a/model/text2vec-base-chinese/logs.txt b/model/text2vec-base-chinese/logs.txt index 8b13789..1f2f0a8 100644 --- a/model/text2vec-base-chinese/logs.txt +++ b/model/text2vec-base-chinese/logs.txt @@ -1 +1,19 @@ - +Epoch:0 Valid| corr: 0.794410 +Epoch:0 Valid| corr: 0.691819 +Epoch:1 Valid| corr: 0.722749 +Epoch:2 Valid| corr: 0.735054 +Epoch:3 Valid| corr: 0.738295 +Epoch:4 Valid| corr: 0.739411 +Test | corr: 0.679971 +Epoch:0 Valid| corr: 0.817416 +Epoch:1 Valid| corr: 0.832376 +Epoch:2 Valid| corr: 0.842308 +Epoch:3 Valid| corr: 0.843520 +Epoch:4 Valid| corr: 0.841837 +Test | corr: 0.793495 +Epoch:0 Valid| corr: 0.814648 +Epoch:1 Valid| corr: 0.831609 +Epoch:2 Valid| corr: 0.841678 +Epoch:3 Valid| corr: 0.842387 +Epoch:4 Valid| corr: 0.841435 +Test | corr: 0.794840 diff --git a/modelcache/adapter/adapter.py b/modelcache/adapter/adapter.py index 204841b..d62278d 100644 --- a/modelcache/adapter/adapter.py +++ b/modelcache/adapter/adapter.py @@ -10,11 +10,11 @@ class ChatCompletion(object): """Openai ChatCompletion Wrapper""" @classmethod - def create_query(cls, *args, **kwargs): + async def create_query(cls, *args, **kwargs): def cache_data_convert(cache_data, cache_query): return construct_resp_from_cache(cache_data, cache_query) try: - return adapt_query( + return await adapt_query( cache_data_convert, *args, **kwargs @@ -24,9 +24,9 @@ def cache_data_convert(cache_data, cache_query): return str(e) @classmethod - def create_insert(cls, *args, **kwargs): + async def create_insert(cls, *args, **kwargs): try: - return adapt_insert( + return await adapt_insert( *args, **kwargs ) diff --git a/modelcache/adapter/adapter_insert.py b/modelcache/adapter/adapter_insert.py index a507ee1..b4d1f6a 100644 --- a/modelcache/adapter/adapter_insert.py +++ b/modelcache/adapter/adapter_insert.py @@ -1,9 +1,11 @@ # -*- coding: utf-8 -*- +import asyncio + from modelcache.utils.error import NotInitError from modelcache.utils.time import time_cal -def adapt_insert(*args, **kwargs): +async def adapt_insert(*args, **kwargs): chat_cache = kwargs.pop("cache_obj") model = kwargs.pop("model", None) require_object_store = kwargs.pop("require_object_store", False) @@ -13,7 +15,8 @@ def adapt_insert(*args, **kwargs): chat_info = kwargs.pop("chat_info", []) pre_embedding_data_list = [] - embedding_data_list = [] + embedding_futures_list = [] + # embedding_data_list = [] llm_data_list = [] for row in chat_info: @@ -24,13 +27,15 @@ def adapt_insert(*args, **kwargs): ) pre_embedding_data_list.append(pre_embedding_data) llm_data_list.append(row['answer']) - embedding_data = time_cal( + embedding_future = time_cal( chat_cache.embedding_func, func_name="embedding", report_func=chat_cache.report.embedding, cache_obj=chat_cache )(pre_embedding_data) - embedding_data_list.append(embedding_data) + embedding_futures_list.append(embedding_future) + + embedding_data_list = await asyncio.gather(*embedding_futures_list) chat_cache.data_manager.save( pre_embedding_data_list, diff --git a/modelcache/adapter/adapter_query.py b/modelcache/adapter/adapter_query.py index 2e56cc2..2eb8fa4 100644 --- a/modelcache/adapter/adapter_query.py +++ b/modelcache/adapter/adapter_query.py @@ -6,7 +6,7 @@ USE_RERANKER = False # 如果为 True 则启用 reranker，否则使用原有逻辑 -def adapt_query(cache_data_convert, *args, **kwargs): +async def adapt_query(cache_data_convert, *args, **kwargs): chat_cache = kwargs.pop("cache_obj") scope = kwargs.pop("scope") model = scope['model'] @@ -17,7 +17,7 @@ def adapt_query(cache_data_convert, *args, **kwargs): extra_param=context.get("pre_embedding_func", None), prompts=chat_cache.prompts, ) - embedding_data = time_cal( + embedding_data = await time_cal( chat_cache.embedding_func, func_name="embedding", report_func=chat_cache.report.embedding, diff --git a/modelcache/cache.py b/modelcache/cache.py index 8144138..16c744d 100644 --- a/modelcache/cache.py +++ b/modelcache/cache.py @@ -1,12 +1,15 @@ # -*- coding: utf-8 -*- +import asyncio import atexit import json import logging import time -from typing import Callable, Optional, List +from asyncio import AbstractEventLoop +from typing import Callable, Optional, List, Any, Coroutine from modelcache.adapter import adapter +from modelcache.embedding.embedding_dispatcher import EmbeddingDispatcher from modelcache.utils.model_filter import model_blacklist_filter -from concurrent.futures import ThreadPoolExecutor +from concurrent.futures import ThreadPoolExecutor, Future import configparser from modelcache.embedding.base import BaseEmbedding, EmbeddingModel, MetricType from modelcache.manager.scalar_data.sql_storage import SQLStorage @@ -41,10 +44,11 @@ def __init__( data_manager: DataManager, query_pre_embedding_func: Callable, insert_pre_embedding_func: Callable, - embedding_func: Callable, + embedding_func: Callable[[str], Future], report: Report, # TODO: figure out why this is needed similarity_evaluation: Optional[SimilarityEvaluation], post_process_messages_func: Callable, + similarity_threshold: float = 0.95, similarity_threshold_long: float = 0.95, prompts: Optional[List[str]] = None, @@ -82,7 +86,7 @@ def save_query_info(self,result, model, query, delta_time_log): self.data_manager.save_query_resp(result, model=model, query=json.dumps(query, ensure_ascii=False), delta_time=delta_time_log) - def handle_request(self, param_dict: dict): + async def handle_request(self, param_dict: dict): # param parsing try: request_type = param_dict.get("type") @@ -112,9 +116,9 @@ def handle_request(self, param_dict: dict): # handle request if request_type == 'query': - return self.handle_query(model, query) + return await self.handle_query(model, query) elif request_type == 'insert': - return self.handle_insert(chat_info, model) + return await self.handle_insert(chat_info, model) elif request_type == 'remove': return self.handle_remove(model, param_dict) elif request_type == 'register': @@ -151,10 +155,10 @@ def handle_remove(self, model, param_dict): result = {"errorCode": 402, "errorDesc": "", "response": response, "writeStatus": "exception"} return result - def handle_insert(self, chat_info, model): + async def handle_insert(self, chat_info, model): try: try: - response = adapter.ChatCompletion.create_insert( + response = await adapter.ChatCompletion.create_insert( model=model, chat_info=chat_info, cache_obj=self @@ -170,10 +174,10 @@ def handle_insert(self, chat_info, model): except Exception as e: return {"errorCode": 303, "errorDesc": str(e), "writeStatus": "exception"} - def handle_query(self, model, query): + async def handle_query(self, model, query): try: start_time = time.time() - response = adapter.ChatCompletion.create_query( + response = await adapter.ChatCompletion.create_query( scope={"model": model}, query=query, cache_obj=self @@ -203,7 +207,12 @@ def flush(self): self.data_manager.flush() @staticmethod - def init(sql_storage: str, vector_storage: str) -> 'Cache': + async def init( + sql_storage: str, + vector_storage: str, + embedding_model: EmbeddingModel, + embedding_workers_num: int + ) -> tuple['Cache' , AbstractEventLoop]: #================= configurations for databases ===================# sql_config = configparser.ConfigParser() @@ -234,9 +243,15 @@ def init(sql_storage: str, vector_storage: str) -> 'Cache': #=============== model-specific configuration =====================# - embedding_model = EmbeddingModel.HUGGINGFACE - model_path = "sentence-transformers/all-mpnet-base-v2" - base_embedding = BaseEmbedding.get(embedding_model, model_path=model_path) + event_loop = asyncio.get_running_loop() + model_path = embedding_model.value['model_path'] + dimension = embedding_model.value['dimension'] + + if model_path is None or dimension is None: + modelcache_log.error(f"Please set the model_path and dimension for {embedding_model} in modelcache/embedding/base.py.") + raise CacheError(f"Please set the model_path and dimension for {embedding_model} in modelcache/embedding/base.py.") + + embedding_dispatcher = EmbeddingDispatcher(embedding_model, model_path, event_loop, embedding_workers_num) #=== These will be used to initialize the cache ===# query_pre_embedding_func: Callable = None @@ -250,7 +265,7 @@ def init(sql_storage: str, vector_storage: str) -> 'Cache': #==================================================# # switching based on embedding_model - if embedding_model == EmbeddingModel.HUGGINGFACE: + if embedding_model == EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2: query_pre_embedding_func = query_with_role insert_pre_embedding_func = query_with_role post_process_messages_func = first @@ -281,7 +296,7 @@ def init(sql_storage: str, vector_storage: str) -> 'Cache': SQLStorage.get(sql_storage, config=sql_config), VectorStorage.get( name=vector_storage, - dimension=base_embedding.dimension, + dimension=dimension, config=vector_config, metric_type=similarity_metric_type, ), @@ -290,7 +305,6 @@ def init(sql_storage: str, vector_storage: str) -> 'Cache': normalize=normalize, ) - #================== Cache Initialization ====================# cache = Cache( @@ -298,7 +312,7 @@ def init(sql_storage: str, vector_storage: str) -> 'Cache': similarity_metric_type = similarity_metric_type, data_manager = data_manager, report = Report(), - embedding_func = base_embedding.to_embeddings, + embedding_func = embedding_dispatcher.embed, query_pre_embedding_func = query_pre_embedding_func, insert_pre_embedding_func = insert_pre_embedding_func, similarity_evaluation = similarity_evaluation, @@ -308,4 +322,4 @@ def init(sql_storage: str, vector_storage: str) -> 'Cache': prompts = None, log_time_func = None, ) - return cache \ No newline at end of file + return cache, event_loop \ No newline at end of file diff --git a/modelcache/embedding/base.py b/modelcache/embedding/base.py index 3cd5936..aafdc97 100644 --- a/modelcache/embedding/base.py +++ b/modelcache/embedding/base.py @@ -17,14 +17,17 @@ class EmbeddingModel(Enum): """ Enum for different embedding models. """ - HUGGINGFACE = "huggingface" - DATA2VEC_AUDIO = "data2vec_audio" - LLM_EMB2VEC_AUDIO = "llmEmb2vec_audio" - FASTTEXT = "fasttext" - PADDLE_NLP = "paddlenlp" - TIMM = "timm" - HUGGINGFACE_TEI = "huggingface_tei" - BGE_M3 = "bge_m3" + # todo: fill in the dimension and model_path for each embedding model as needed + HUGGINGFACE_ALL_MPNET_BASE_V2 = {"dimension":768, "model_path":"sentence-transformers/all-mpnet-base-v2"} + HUGGINGFACE_ALL_MINILM_L6_V2 = {"dimension":384, "model_path":"sentence-transformers/all-MiniLM-L6-v2"} + HUGGINGFACE_ALL_MINILM_L12_V2 = {"dimension":384, "model_path":"sentence-transformers/all-MiniLM-L12-v2"} + DATA2VEC_AUDIO = {"dimension":None, "model_path":"model/text2vec-base-chinese/"} + LLM_EMB2VEC_AUDIO = {"dimension":None, "model_path":None} + FASTTEXT = {"dimension":None, "model_path":None} + PADDLE_NLP = {"dimension":None, "model_path":None} + TIMM = {"dimension":None, "model_path":None} + HUGGINGFACE_TEI = {"dimension":None, "model_path":None} + BGE_M3 = {"dimension":None, "model_path":None} class MetricType(Enum): @@ -61,7 +64,7 @@ def get(model:EmbeddingModel, **kwargs): :rtype: BaseEmbedding :raises ValueError: If the specified model type is not supported. """ - if model == EmbeddingModel.HUGGINGFACE: + if model == EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2: model_path = kwargs.pop("model_path","sentence-transformers/all-mpnet-base-v2") return huggingface.Huggingface(model_path) diff --git a/modelcache/embedding/embedding_dispatcher.py b/modelcache/embedding/embedding_dispatcher.py new file mode 100644 index 0000000..358cff1 --- /dev/null +++ b/modelcache/embedding/embedding_dispatcher.py @@ -0,0 +1,71 @@ +import multiprocessing +import threading +import uuid +import asyncio +from asyncio import Future, AbstractEventLoop + +from modelcache.embedding import EmbeddingModel +from modelcache.embedding.base import BaseEmbedding + + +def worker_func(embedding_model: EmbeddingModel, model_path, task_queue, result_queue, worker_id): + base_embedding = BaseEmbedding.get(embedding_model, model_path=model_path) + print(f"Embedding worker {worker_id} started.") + while True: + job_id, data = task_queue.get() + try: + result = base_embedding.to_embeddings(data) + except Exception as e: + result = e + result_queue.put((job_id, result)) + + +class EmbeddingDispatcher: + def __init__( + self, + embedding_model: EmbeddingModel, + model_path: str, + event_loop: AbstractEventLoop, + num_workers: int + ): + if num_workers <= 0: + raise ValueError("Number of workers must be greater than 0.") + + self.task_queue = multiprocessing.Queue() + self.result_queue = multiprocessing.Queue() + self.futures: dict[str, asyncio.Future] = {} + self.event_loop = event_loop + self._start_result_collector_thread() + + # Start worker processes + self.workers = [] + for i in range(num_workers): + p = multiprocessing.Process( + target=worker_func, + args=(embedding_model, model_path, self.task_queue, self.result_queue, i) + ) + p.daemon = True + p.start() + self.workers.append(p) + + def _start_result_collector_thread(self): + def collect(): + while True: + job_id, result = self.result_queue.get() + future = self.futures.pop(job_id, None) + if future: + self.event_loop.call_soon_threadsafe( + future.set_exception if isinstance(result, Exception) else future.set_result, + result + ) + + t = threading.Thread(target=collect, daemon=True) + t.start() + + def embed(self, data: str) -> Future: + job_id = str(uuid.uuid4()) + future = asyncio.get_running_loop().create_future() + self.futures[job_id] = future + self.task_queue.put((job_id, data)) + return future + diff --git a/modelcache/embedding/huggingface.py b/modelcache/embedding/huggingface.py index d48cb9d..77ca08f 100644 --- a/modelcache/embedding/huggingface.py +++ b/modelcache/embedding/huggingface.py @@ -4,7 +4,9 @@ class Huggingface(BaseEmbedding): def __init__(self, model: str): - self.model = SentenceTransformer(model) + self.model = SentenceTransformer(model,tokenizer_kwargs={ + "clean_up_tokenization_spaces":False + }) try: self.__dimension = self.model.config.hidden_size except Exception: diff --git a/requirements.txt b/requirements.txt index 19db268..05fd6db 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,12 +12,11 @@ transformers==4.44.2 faiss-cpu==1.7.4 redis==5.0.1 modelscope==1.26.0 -fastapi==0.115.5 -uvicorn==0.32.0 +fastapi==0.115.12 +uvicorn==0.34.3 chromadb==0.5.23 elasticsearch==7.10.0 snowflake-id==1.0.2 flagembedding==1.3.4 cryptography==45.0.2 sentence-transformers==4.1.0 -websockets==15.0.1 diff --git a/websocket4modelcache.py b/websocket4modelcache.py index 4541a91..fe99cd5 100644 --- a/websocket4modelcache.py +++ b/websocket4modelcache.py @@ -1,45 +1,58 @@ # -*- coding: utf-8 -*- -import asyncio -import websockets +from contextlib import asynccontextmanager +import uvicorn import json +import asyncio +from fastapi import FastAPI, WebSocket +from starlette.websockets import WebSocketDisconnect from modelcache.cache import Cache - -# Initialize the cache -cache = Cache.init("mysql", "milvus") - - -async def handle_client(websocket): - async for message in websocket: - # Parse JSON - try: - param_dict = json.loads(message) - except json.JSONDecodeError: - await websocket.send(json.dumps({"errorCode": 400, "errorDesc": "bad request"})) - continue - - request_id = param_dict.get("requestId") - request_payload = param_dict.get("payload") - if not request_id or not request_payload: - await websocket.send(json.dumps({"errorCode": 400, "errorDesc": "bad request"})) - continue - asyncio.create_task(process_and_respond(websocket, request_id, request_payload)) +from modelcache.embedding import EmbeddingModel + +@asynccontextmanager +async def lifespan(app: FastAPI): + global cache + cache, _ = await Cache.init( + sql_storage="mysql", + vector_storage="milvus", + embedding_model=EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2, + embedding_workers_num=2 + ) + yield + +app = FastAPI(lifespan=lifespan) +cache: Cache = None + +@app.websocket("/modelcache") +async def user_backend(websocket: WebSocket): + await websocket.accept() + try: + while True: + data = await websocket.receive_text() + asyncio.create_task(handle_message(websocket, data)) + except WebSocketDisconnect as e: + print(e) -async def process_and_respond(websocket,request_id, request_payload): +async def handle_message(websocket,message): try: - result = cache.handle_request(request_payload) - await websocket.send(json.dumps({"requestId": request_id,"result": result})) + param_dict = json.loads(message) + except Exception: + await websocket.send_json({"errorCode": 400, "errorDesc": "bad request", "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''}) + return + + request_id = param_dict.get("requestId") + request_payload = param_dict.get("payload") + if not request_id or not request_payload: + await websocket.send_json({"errorCode": 400, "errorDesc": "bad request", "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''}) + return + try: + result = await cache.handle_request(request_payload) + await websocket.send_json({"requestId": request_id,"result": result}) except Exception as e: - error_result = {"errorCode": 102, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', - "answer": ''} + error_result = {"errorCode": 500, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} cache.save_query_resp(error_result, model='', query='', delta_time=0) - await websocket.send(json.dumps(error_result)) - + await websocket.send_json(error_result) -async def main(): - print("WebSocket server starting on ws://0.0.0.0:5000") - async with websockets.serve(handle_client, "0.0.0.0", 5000): - await asyncio.Future() # Run forever -if __name__ == "__main__": - asyncio.run(main()) +if __name__ == '__main__': + uvicorn.run(app, host='0.0.0.0', port=5000, loop="asyncio", http="httptools") diff --git a/websocket4modelcache_demo.py b/websocket4modelcache_demo.py new file mode 100644 index 0000000..2a20bc8 --- /dev/null +++ b/websocket4modelcache_demo.py @@ -0,0 +1,58 @@ +# -*- coding: utf-8 -*- +from contextlib import asynccontextmanager +import uvicorn +import json +import asyncio +from fastapi import FastAPI, WebSocket +from starlette.websockets import WebSocketDisconnect +from modelcache.cache import Cache +from modelcache.embedding import EmbeddingModel + +@asynccontextmanager +async def lifespan(app: FastAPI): + global cache + cache, _ = await Cache.init( + sql_storage="sqlite", + vector_storage="faiss", + embedding_model=EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2, + embedding_workers_num=2 + ) + yield + +app = FastAPI(lifespan=lifespan) +cache: Cache = None + +@app.websocket("/modelcache") +async def user_backend(websocket: WebSocket): + await websocket.accept() + try: + while True: + data = await websocket.receive_text() + asyncio.create_task(handle_message(websocket, data)) + except WebSocketDisconnect as e: + print(e) + + +async def handle_message(websocket,message): + try: + param_dict = json.loads(message) + except Exception: + await websocket.send_json({"errorCode": 400, "errorDesc": "bad request", "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''}) + return + + request_id = param_dict.get("requestId") + request_payload = param_dict.get("payload") + if not request_id or not request_payload: + await websocket.send_json({"errorCode": 400, "errorDesc": "bad request", "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''}) + return + try: + result = await cache.handle_request(request_payload) + await websocket.send_json({"requestId": request_id,"result": result}) + except Exception as e: + error_result = {"errorCode": 500, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} + cache.save_query_resp(error_result, model='', query='', delta_time=0) + await websocket.send_json(error_result) + + +if __name__ == '__main__': + uvicorn.run(app, host='0.0.0.0', port=5000, loop="asyncio", http="httptools") From 512c5b151df039208dd4b38d30181ff3e5690b89 Mon Sep 17 00:00:00 2001 From: adiaybgu Date: Thu, 12 Jun 2025 14:29:04 +0300 Subject: [PATCH 31/36] Unit Tests for wtinylfu_cache.py and arc_cache.py Co-authored-by: omerdor001 Co-authored-by: adiaybgu --- modelcache/manager/eviction/wtinylfu_cache.py | 12 +- requirements.txt | 2 + tests/__init__.py | 0 tests/test_arc_cache.py | 208 +++++++++++++++ tests/test_wtinylfu_cache.py | 237 ++++++++++++++++++ 5 files changed, 457 insertions(+), 2 deletions(-) create mode 100644 tests/__init__.py create mode 100644 tests/test_arc_cache.py create mode 100644 tests/test_wtinylfu_cache.py diff --git a/modelcache/manager/eviction/wtinylfu_cache.py b/modelcache/manager/eviction/wtinylfu_cache.py index d7f4272..f33a572 100644 --- a/modelcache/manager/eviction/wtinylfu_cache.py +++ b/modelcache/manager/eviction/wtinylfu_cache.py @@ -117,13 +117,21 @@ def _put(self, key): def _admit_to_main(self, key): if key in self.protected or key in self.probation: return + if self.probation_size == 0: + if self.on_evict: + self.on_evict(key) + self.data.pop(key, None) + return if len(self.probation) < self.probation_size: self.probation[key] = True - else: + elif self.probation: evicted = next(iter(self.probation)) self.probation.pop(evicted) self.probation[key] = True - # this eviction removes it entirely if self.on_evict: self.on_evict(evicted) self.data.pop(evicted, None) + else: + if self.on_evict: + self.on_evict(key) + self.data.pop(key, None) diff --git a/requirements.txt b/requirements.txt index 05fd6db..23ee700 100644 --- a/requirements.txt +++ b/requirements.txt @@ -20,3 +20,5 @@ snowflake-id==1.0.2 flagembedding==1.3.4 cryptography==45.0.2 sentence-transformers==4.1.0 +pytest>=8.0 + diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_arc_cache.py b/tests/test_arc_cache.py new file mode 100644 index 0000000..7958409 --- /dev/null +++ b/tests/test_arc_cache.py @@ -0,0 +1,208 @@ +import pytest +from modelcache.manager.eviction.arc_cache import ARC + +@pytest.fixture() +def empty_arc(): + return ARC(maxsize=4) + +@pytest.fixture() +def arc_with_data(): + c = ARC(maxsize=4) + c['a'] = 1 + c['b'] = 2 + return c + +def test_setitem_adds_to_arc(empty_arc): + """Test __setitem__ adds a key-value pair to ARC.""" + empty_arc['x'] = 123 + assert 'x' in empty_arc + assert empty_arc['x'] == 123 + +def test_setitem_overwrites_value(empty_arc): + """Test that __setitem__ overwrites existing value in ARC.""" + empty_arc['y'] = 1 + empty_arc['y'] = 55 + assert empty_arc['y'] == 55 + +def test_getitem_returns_value(arc_with_data): + """Test __getitem__ returns the correct value if present.""" + assert arc_with_data['a'] == 1 + +def test_getitem_raises_keyerror_on_missing(empty_arc): + """Test __getitem__ raises KeyError if key is missing.""" + with pytest.raises(KeyError): + _ = empty_arc['nope'] + +def test_contains_true_for_present(arc_with_data): + """Test __contains__ returns True for a present key.""" + assert 'b' in arc_with_data + +def test_contains_false_for_missing(arc_with_data): + """Test __contains__ returns False for missing key.""" + assert 'notfound' not in arc_with_data + +def test_len_reports_active_cache_size(arc_with_data): + """Test __len__ reports only active items (T1 + T2).""" + assert len(arc_with_data) == 2 + arc_with_data['c'] = 3 + assert len(arc_with_data) == 3 + +def test_pop_removes_key_from_one_list(arc_with_data): + """Test pop removes key from the first ARC list where it is found.""" + arc_with_data['ghost'] = 9 + arc_with_data.b1['ghost'] = 9 + arc_with_data.pop('ghost') + assert 'ghost' not in arc_with_data.t1 + assert 'ghost' in arc_with_data.b1 + +def test_clear_removes_all_keys(empty_arc): + """Test clear() empties all lists and resets p.""" + empty_arc['a'] = 1 + empty_arc['b'] = 2 + empty_arc.clear() + assert len(empty_arc.t1) == 0 + assert len(empty_arc.t2) == 0 + assert len(empty_arc.b1) == 0 + assert len(empty_arc.b2) == 0 + assert empty_arc.p == 0 + +def test_evict_internal_evicts_when_over_capacity(): + """Test _evict_internal evicts oldest when ARC is full.""" + evicted = [] + c = ARC(maxsize=2, on_evict=lambda keys: evicted.extend(keys)) + c['a'] = 1 + c['b'] = 2 + c['c'] = 3 + assert len(c) == 2 + assert len(evicted) >= 1 + for k in evicted: + assert k in ['a', 'b'] + +def test_eviction_callback_is_called(): + """Test on_evict callback is called on eviction.""" + evicted = [] + c = ARC(maxsize=2, on_evict=lambda keys: evicted.extend(keys)) + c['x'] = 1 + c['y'] = 2 + c['z'] = 3 + assert len(evicted) > 0 + assert all(isinstance(k, str) for k in evicted) + +def test_promote_from_t1_to_t2(empty_arc): + """Test that accessing key in T1 promotes it to T2.""" + empty_arc['foo'] = 10 + assert 'foo' in empty_arc.t1 + _ = empty_arc['foo'] + assert 'foo' in empty_arc.t2 + +def test_refresh_in_t2_updates_order(empty_arc): + """Test that repeated access in T2 keeps item in T2.""" + empty_arc['x'] = 1 + _ = empty_arc['x'] # promote to T2 + empty_arc['y'] = 2 + _ = empty_arc['x'] # refresh x in T2 + assert 'x' in empty_arc.t2 + assert empty_arc.t2.popitem(last=True)[0] == 'x' + +def test_hit_in_ghost_lists_promotes_to_t2(empty_arc): + """Test access in ghost list B1 promotes key to T2.""" + empty_arc['a'] = 1 + empty_arc['b'] = 2 + empty_arc['c'] = 3 # triggers eviction to ghost B1 + if empty_arc.b1: + ghost_key = next(iter(empty_arc.b1)) + empty_arc.__missing__ = lambda key: 999 + _ = empty_arc[ghost_key] + assert ghost_key in empty_arc.t2 + +def test_iter_lists_keys_in_order(arc_with_data): + """Test __iter__ yields keys from T1 and then T2.""" + arc_with_data['c'] = 3 + keys = list(iter(arc_with_data)) + expected = list(arc_with_data.t1.keys()) + list(arc_with_data.t2.keys()) + assert keys == expected + +def test_repr_outputs_status(empty_arc): + """Test __repr__ returns a string with cache stats.""" + r = repr(empty_arc) + assert r.startswith("ARC(") + assert "maxsize" in r + +# ----------- Additional/Edge case tests ----------- + +def test_pop_missing_key_returns_default(empty_arc): + """Test that pop() returns default if key not found in any list.""" + assert empty_arc.pop('missing', default='sentinel') == 'sentinel' + +def test_setitem_multiple_evictions(): + """Test multiple evictions in sequence do not corrupt the cache.""" + evicted = [] + c = ARC(maxsize=2, on_evict=lambda keys: evicted.extend(keys)) + c['a'] = 1 + c['b'] = 2 + c['c'] = 3 + c['d'] = 4 + assert len(c) == 2 + assert len(evicted) >= 2 + assert all(isinstance(k, str) for k in evicted) + +def test_access_promotes_b1_and_b2(empty_arc): + """Test accessing a key in B1 or B2 increases/decreases p appropriately.""" + empty_arc['a'] = 1 + empty_arc['b'] = 2 + empty_arc['c'] = 3 + if empty_arc.b1: + ghost_key = next(iter(empty_arc.b1)) + empty_arc.__missing__ = lambda key: 555 + p_before = empty_arc.p + _ = empty_arc[ghost_key] + assert empty_arc.p > p_before or empty_arc.p == empty_arc.maxsize + +def test_active_and_ghost_lists_dont_exceed_maxsize(): + """Test ghost lists (B1/B2) and active lists (T1/T2) don't exceed maxsize.""" + c = ARC(maxsize=3) + for k in ['a', 'b', 'c', 'd', 'e']: + c[k] = ord(k) + # B1 + B2 should never be larger than maxsize + assert len(c.b1) + len(c.b2) <= c.maxsize + assert len(c.t1) + len(c.t2) <= c.maxsize + +def test_clear_resets_all_lists_and_p(empty_arc): + """Test that clear() resets all lists and p after many ops.""" + for k in 'abcd': + empty_arc[k] = ord(k) + empty_arc.clear() + assert not any([empty_arc.t1, empty_arc.t2, empty_arc.b1, empty_arc.b2]) + assert empty_arc.p == 0 + +def test_repr_is_informative(empty_arc): + """Test that __repr__ outputs all important stats.""" + empty_arc['q'] = 9 + r = repr(empty_arc) + assert "t1_len" in r and "b1_len" in r and "p=" in r + +def test_setitem_duplicate_key_resets_position(empty_arc): + """Test that setting the same key again resets its position.""" + empty_arc['x'] = 10 + empty_arc['y'] = 11 + empty_arc['x'] = 99 + # x should be last in t1 + assert list(empty_arc.t1.keys())[-1] == 'x' + assert empty_arc['x'] == 99 + +def test_eviction_of_promoted_key(): + """Test that a key promoted to T2 can still be evicted if capacity is exceeded.""" + evicted = [] + c = ARC(maxsize=2, on_evict=lambda keys: evicted.extend(keys)) + c['a'] = 1 + c['b'] = 2 + _ = c['a'] # Promote 'a' to T2 + c['c'] = 3 # Evict one of the keys + assert len(c) == 2 + assert len(evicted) >= 1 + +def test_keyerror_on_missing_and_no_default(empty_arc): + """Test pop() raises KeyError if no default is given and key is missing.""" + with pytest.raises(KeyError): + empty_arc.pop('never-there') + diff --git a/tests/test_wtinylfu_cache.py b/tests/test_wtinylfu_cache.py new file mode 100644 index 0000000..21a25d1 --- /dev/null +++ b/tests/test_wtinylfu_cache.py @@ -0,0 +1,237 @@ +import pytest +from modelcache.manager.eviction.wtinylfu_cache import W2TinyLFU, CountMinSketch + +@pytest.fixture() +def empty_cache(): + return W2TinyLFU(maxsize=10, window_pct=50) + +@pytest.fixture() +def cache_with_data(): + c = W2TinyLFU(maxsize=10, window_pct=50) + c['a'] = 1 + c['b'] = 2 + return c + +@pytest.fixture() +def cms(): + return CountMinSketch(width=16, depth=2, decay_interval=5) + +def test_setitem_adds_to_cache(empty_cache): + """Test that __setitem__ adds a new key-value pair to the cache.""" + empty_cache['x'] = 100 + assert empty_cache.data['x'] == 100 + assert 'x' in empty_cache + +def test_setitem_overwrites_value(empty_cache): + """Test that __setitem__ overwrites existing value for the same key.""" + empty_cache['y'] = 1 + empty_cache['y'] = 999 + assert empty_cache.data['y'] == 999 + +def test_getitem_returns_value(cache_with_data): + """Test __getitem__ returns correct value for present key.""" + assert cache_with_data['a'] == 1 + +def test_getitem_raises_keyerror_on_missing(empty_cache): + """Test __getitem__ raises KeyError for missing key.""" + with pytest.raises(KeyError): + _ = empty_cache['not_found'] + +def test_get_method_returns_value(cache_with_data): + """Test get method returns value for existing key.""" + assert cache_with_data.get('b') == 2 + +def test_get_method_returns_default_on_missing(empty_cache): + """Test get method returns default for missing key.""" + assert empty_cache.get('zzz', default=123) == 123 + +def test_contains_true_for_present(cache_with_data): + """Test __contains__ returns True for present key.""" + assert 'a' in cache_with_data + +def test_contains_false_for_missing(cache_with_data): + """Test __contains__ returns False for missing key.""" + assert 'zzz' not in cache_with_data + +def test_delitem_removes_key(cache_with_data): + """Test __delitem__ removes a present key.""" + del cache_with_data['a'] + assert 'a' not in cache_with_data + +def test_delitem_safe_if_missing(empty_cache): + """Test __delitem__ does not raise if key is missing.""" + try: + del empty_cache['nope'] + except Exception: + pytest.fail("delitem should not raise when key is missing.") + +def test_duplicate_key_updates_value(empty_cache): + """Test that setting same key twice updates the value.""" + empty_cache['dup'] = 10 + empty_cache['dup'] = 20 + assert empty_cache['dup'] == 20 + +def test_window_size_clamped_at_least_one(): + """Test that window size is always at least 1.""" + c = W2TinyLFU(maxsize=3, window_pct=0) + assert c.window_size >= 1 + c['a'] = 1 + assert len(c.window) <= c.window_size + +def test_eviction_callback_at_least_one_eviction(): + """Test that eviction callback is called when capacity exceeded.""" + evicted = [] + c = W2TinyLFU(maxsize=3, window_pct=33, on_evict=evicted.append) + c['a'] = 1 + c['b'] = 2 + c['c'] = 3 + c['d'] = 4 + assert len(evicted) >= 1 + assert all(k in ('a', 'b', 'c', 'd') for k in evicted) + +def test_eviction_callback_evicted_keys_are_gone(): + """Test that evicted keys are not present in the cache.""" + evicted = [] + c = W2TinyLFU(maxsize=3, window_pct=33, on_evict=evicted.append) + keys = ['a', 'b', 'c', 'd'] + for i, k in enumerate(keys): + c[k] = i + for k in evicted: + assert k not in c + +def test_eviction_callback_not_called_when_under_capacity(): + """Test that eviction callback is not called when under capacity.""" + evicted = [] + c = W2TinyLFU(maxsize=4, window_pct=50, on_evict=evicted.append) + c['one'] = 1 + c['two'] = 2 + assert len(evicted) == 0 + +def test_eviction_callback_with_chain_eviction(): + """Test that multiple evictions can occur in chain scenarios.""" + evicted = [] + c = W2TinyLFU(maxsize=2, window_pct=50, on_evict=evicted.append) + c['x'] = 1 + c['y'] = 2 + c['z'] = 3 + c['w'] = 4 + assert set(evicted).issubset({'x', 'y', 'z', 'w'}) + assert len(evicted) >= 1 + +def test_evicted_key_not_accessible(): + """Test that accessing an evicted key raises KeyError.""" + c = W2TinyLFU(maxsize=2) + c['a'], c['b'] = 1, 2 + c['c'] = 3 + with pytest.raises(KeyError): + _ = c['a'] + +def test_eviction_until_empty_and_reusability(): + """Test cache remains usable after all items have been evicted.""" + c = W2TinyLFU(maxsize=2) + c['x'] = 1 + c['y'] = 2 + c['z'] = 3 + del c['y'] + c['w'] = 4 + assert len(c.data) <= 2 + +def test_high_frequency_key_survives_eviction(): + """Test that a frequently accessed key survives eviction pressure.""" + c = W2TinyLFU(maxsize=10, window_pct=40) + c['a'] = 1 + for new_key in ['b', 'c', 'd', 'e', 'f', 'g']: + c.get('a') + c[new_key] = ord(new_key) + assert 'a' in c + +def test_low_freq_key_evicted(): + """Test that an infrequently used key is evicted under pressure.""" + c = W2TinyLFU(maxsize=3, window_pct=33) + c['a'] = 1 + c['b'] = 2 + c['c'] = 3 + c['d'] = 4 + assert 'a' not in c + +@pytest.mark.parametrize("hits, expected_in_cache", [ + (0, False), + (5, True), + (15, True), +]) +def test_access_promotes_key(hits, expected_in_cache): + """Test that frequent access promotes key to survive eviction.""" + c = W2TinyLFU(maxsize=8, window_pct=40) + c['a'] = 1 + insert_keys = ['b', 'c', 'd', 'e', 'f', 'g', 'h'] + for i, k in enumerate(insert_keys): + if i < hits: + c.get('a') + c[k] = ord(k) + if hits == 0: + assert 'a' not in c + else: + assert 'a' in c + +def test_cms_estimate_increases_and_decays(cms): + """Test that CountMinSketch increases and decays correctly.""" + before = cms.estimate('test') + cms.add('test') + after_add = cms.estimate('test') + assert after_add >= before + cms.decay() + after_decay = cms.estimate('test') + assert after_decay <= after_add + +def test_admit_to_main_adds_to_probation(): + """Test _admit_to_main adds new key to probation segment.""" + c = W2TinyLFU(maxsize=10, window_pct=50) + key = 'k' + assert key not in c.probation + assert key not in c.protected + c._admit_to_main(key) + assert key in c.probation + +def test_admit_to_main_evicts_when_probation_full(): + """Test _admit_to_main evicts LRU key when probation is full.""" + evicted = [] + c = W2TinyLFU(maxsize=4, window_pct=25, on_evict=evicted.append) + for i in range(c.probation_size): + c._admit_to_main(f'p{i}') + c.data[f'p{i}'] = i + extra = 'extra' + c.data[extra] = 999 + c._admit_to_main(extra) + assert len(c.probation) == c.probation_size + assert extra in c.probation + assert len(evicted) >= 1 + +def test_admit_to_main_noop_if_already_present(): + """Test _admit_to_main does nothing if key already present.""" + c = W2TinyLFU(maxsize=10, window_pct=50) + key = 'present' + c.probation[key] = True + c._admit_to_main(key) + assert key in c.probation + c.protected[key] = True + c._admit_to_main(key) + assert key in c.protected + +def test_put_key_in_window_and_main(): + """Test that new keys are distributed into segments correctly.""" + c = W2TinyLFU(maxsize=10, window_pct=50) + c['a'] = 1 + for k in ['b', 'c', 'd']: + c[k] = ord(k) + c['e'] = 5 + total = len(c.window) + len(c.probation) + len(c.protected) + assert total <= c.maxsize + +def test_put_eviction_callback_called(): + """Test that the eviction callback is invoked when needed.""" + evicted = [] + c = W2TinyLFU(maxsize=2, on_evict=evicted.append) + c['a'] = 1 + c['b'] = 2 + c['c'] = 3 + assert len(evicted) >= 1 From c37875c6263ccdf7a0275eaa3f5d6ee6bc833412 Mon Sep 17 00:00:00 2001 From: adiaybgu Date: Sun, 15 Jun 2025 16:45:47 +0300 Subject: [PATCH 32/36] Adding locks to ARC and WTINYLFU caches and adding concurrency unit tests Co-authored-by: omerdor001 Co-authored-by: adiaybgu --- modelcache/cache.py | 6 +- modelcache/manager/data_manager.py | 28 +- modelcache/manager/eviction/arc_cache.py | 143 ++--- modelcache/manager/eviction/memory_cache.py | 13 +- modelcache/manager/eviction/wtinylfu_cache.py | 42 +- requirements.txt | 2 +- tests/test_arc_cache.py | 560 ++++++++++++++---- tests/test_wtinylfu_cache.py | 283 +++++++-- websocket4modelcache.py | 2 +- 9 files changed, 772 insertions(+), 307 deletions(-) diff --git a/modelcache/cache.py b/modelcache/cache.py index 16c744d..76d98fc 100644 --- a/modelcache/cache.py +++ b/modelcache/cache.py @@ -27,7 +27,7 @@ #==================== Cache class definition =========================# #=====================================================================# -executor = ThreadPoolExecutor(max_workers=6) +executor = ThreadPoolExecutor(max_workers=2) def response_text(cache_resp): return cache_resp['data'] @@ -300,8 +300,8 @@ async def init( config=vector_config, metric_type=similarity_metric_type, ), - eviction='WTINYLFU', - max_size=100000, + eviction='ARC', + max_size=10000, normalize=normalize, ) diff --git a/modelcache/manager/data_manager.py b/modelcache/manager/data_manager.py index 13d29b1..c12f414 100644 --- a/modelcache/manager/data_manager.py +++ b/modelcache/manager/data_manager.py @@ -182,8 +182,7 @@ def __init__( self.eviction_base = MemoryCacheEviction( policy=policy, maxsize=max_size, - clean_size=clean_size, - on_evict=self._evict_ids) + clean_size=clean_size) def save(self, questions: List[any], answers: List[any], embedding_datas: List[any], **kwargs): model = kwargs.pop("model", None) @@ -314,31 +313,6 @@ def truncate(self, model): 'ScalarDB': 'truncate scalar data failed, please check! e: {}'.format(e)} return {'status': 'success', 'VectorDB': 'rebuild', 'ScalarDB': 'delete_count: ' + str(delete_count)} - # added - def _evict_ids(self, ids, **kwargs): - model = kwargs.get("model") - if not ids or any(i is None for i in ids): - modelcache_log.warning("Skipping eviction for invalid IDs: %s", ids) - return - - if isinstance(ids,str): - ids = [ids] - - for _id in ids: - self.eviction_base.get_cache(model).pop(_id, None) - - try: - self.s.mark_deleted(ids) - modelcache_log.info("Evicted from scalar storage: %s", ids) - except Exception as e: - modelcache_log.error("Failed to delete from scalar storage: %s", str(e)) - - try: - self.v.delete(ids, model=model) - modelcache_log.info("Evicted from vector storage (model=%s): %s", model, ids) - except Exception as e: - modelcache_log.error("Failed to delete from vector storage (model=%s): %s", model, str(e)) - def flush(self): self.s.flush() self.v.flush() diff --git a/modelcache/manager/eviction/arc_cache.py b/modelcache/manager/eviction/arc_cache.py index 509fae0..ae3a260 100644 --- a/modelcache/manager/eviction/arc_cache.py +++ b/modelcache/manager/eviction/arc_cache.py @@ -1,27 +1,18 @@ from cachetools import Cache from collections import OrderedDict +from readerwriterlock import rwlock -class ARC(Cache): - """ - Adaptive Replacement Cache (ARC) implementation with on_evict callback. - Balances recency and frequency via two active lists (T1, T2) and two ghost lists (B1, B2). - Calls on_evict([key]) whenever an item is evicted from the active cache. - """ +_sentinel = object() - def __init__(self, maxsize, getsizeof=None, on_evict=None): - """ - Args: - maxsize (int): Maximum cache size. - getsizeof (callable, optional): Sizing function for items. - on_evict (callable, optional): Callback called as on_evict([key]) when a key is evicted. - """ +class ARC(Cache): + def __init__(self, maxsize, getsizeof=None): super().__init__(maxsize, getsizeof) self.t1 = OrderedDict() self.t2 = OrderedDict() self.b1 = OrderedDict() self.b2 = OrderedDict() - self.p = 0 # Adaptive target for T1 size. - self.on_evict = on_evict + self.p = 0 + self._rw_lock = rwlock.RWLockWrite() def __len__(self): return len(self.t1) + len(self.t2) @@ -30,96 +21,80 @@ def __contains__(self, key): return key in self.t1 or key in self.t2 def _evict_internal(self): - """ - Evicts items from T1 or T2 if cache is over capacity, and prunes ghost lists. - Calls on_evict for each evicted key. - """ - # Evict from T1 or T2 if active cache > maxsize while len(self.t1) + len(self.t2) > self.maxsize: if len(self.t1) > self.p or (len(self.t1) == 0 and len(self.t2) > 0): key, value = self.t1.popitem(last=False) self.b1[key] = value - if self.on_evict: - self.on_evict([key]) else: key, value = self.t2.popitem(last=False) self.b2[key] = value - if self.on_evict: - self.on_evict([key]) - # Prune ghost lists to their max lengths while len(self.b1) > (self.maxsize - self.p): self.b1.popitem(last=False) while len(self.b2) > self.p: self.b2.popitem(last=False) def __setitem__(self, key, value): - # Remove from all lists before re-inserting - for l in (self.t1, self.t2, self.b1, self.b2): - l.pop(key, None) - self.t1[key] = value - self.t1.move_to_end(key) - self._evict_internal() + with self._rw_lock.gen_wlock(): + for l in (self.t1, self.t2, self.b1, self.b2): + l.pop(key, None) + self.t1[key] = value + self.t1.move_to_end(key) + self._evict_internal() def __getitem__(self, key): - # Case 1: Hit in T1 → promote to T2 - if key in self.t1: - value = self.t1.pop(key) - self.t2[key] = value - self.t2.move_to_end(key) - self.p = max(0, self.p - 1) - self._evict_internal() - return value - # Case 2: Hit in T2 → refresh in T2 - if key in self.t2: - value = self.t2.pop(key) - self.t2[key] = value - self.t2.move_to_end(key) - self.p = min(self.maxsize, self.p + 1) - self._evict_internal() - return value - # Case 3: Hit in B1 (ghost) → fetch and promote to T2 - if key in self.b1: - self.b1.pop(key) - self.p = min(self.maxsize, self.p + 1) - self._evict_internal() - value = super().__missing__(key) - self.t2[key] = value - self.t2.move_to_end(key) - return value - # Case 4: Hit in B2 (ghost) → fetch and promote to T2 - if key in self.b2: - self.b2.pop(key) - self.p = max(0, self.p - 1) - self._evict_internal() - value = super().__missing__(key) - self.t2[key] = value - self.t2.move_to_end(key) - return value - # Case 5: Cold miss → handled by Cache base class (calls __setitem__ after __missing__) - return super().__getitem__(key) + with self._rw_lock.gen_wlock(): + if key in self.t1: + value = self.t1.pop(key) + self.t2[key] = value + self.t2.move_to_end(key) + self.p = max(0, self.p - 1) + self._evict_internal() + return value + if key in self.t2: + value = self.t2.pop(key) + self.t2[key] = value + self.t2.move_to_end(key) + self.p = min(self.maxsize, self.p + 1) + self._evict_internal() + return value + if key in self.b1: + self.b1.pop(key) + self.p = min(self.maxsize, self.p + 1) + self._evict_internal() + value = super().__missing__(key) + self.t2[key] = value + self.t2.move_to_end(key) + return value + if key in self.b2: + self.b2.pop(key) + self.p = max(0, self.p - 1) + self._evict_internal() + value = super().__missing__(key) + self.t2[key] = value + self.t2.move_to_end(key) + return value + return super().__getitem__(key) def __missing__(self, key): - """ - Override this in a subclass, or rely on direct assignment (cache[key] = value). - """ raise KeyError(key) - def pop(self, key, default=None): - """ - Remove key from all lists. - """ - for l in (self.t1, self.t2, self.b1, self.b2): - if key in l: - return l.pop(key) - return default + def pop(self, key, default=_sentinel): + with self._rw_lock.gen_wlock(): + for l in (self.t1, self.t2, self.b1, self.b2): + if key in l: + return l.pop(key) + if default is _sentinel: + raise KeyError(key) + return default def clear(self): - self.t1.clear() - self.t2.clear() - self.b1.clear() - self.b2.clear() - self.p = 0 - super().clear() + with self._rw_lock.gen_wlock(): + self.t1.clear() + self.t2.clear() + self.b1.clear() + self.b2.clear() + self.p = 0 + super().clear() def __iter__(self): yield from self.t1 diff --git a/modelcache/manager/eviction/memory_cache.py b/modelcache/manager/eviction/memory_cache.py index 8561075..347abf9 100644 --- a/modelcache/manager/eviction/memory_cache.py +++ b/modelcache/manager/eviction/memory_cache.py @@ -19,17 +19,14 @@ def wrapper(*args, **kwargs): class MemoryCacheEviction(EvictionBase): - def __init__(self, policy: str, maxsize: int, clean_size: int, on_evict: Callable[[List[Any]], None], **kwargs): + def __init__(self, policy: str, maxsize: int, clean_size: int, **kwargs): self._policy = policy.upper() self.model_to_cache = dict() self.maxsize = maxsize self.clean_size = clean_size - self.on_evict = on_evict self.kwargs = kwargs def create_cache(self, model: str): - - ################# Not integrated with on_evict yet ####################### if self._policy == "LRU": cache = cachetools.LRUCache(maxsize=self.maxsize, **self.kwargs) elif self._policy == "LFU": @@ -38,18 +35,14 @@ def create_cache(self, model: str): cache = cachetools.FIFOCache(maxsize=self.maxsize, **self.kwargs) elif self._policy == "RR": cache = cachetools.RRCache(maxsize=self.maxsize, **self.kwargs) - ########################################################################### - elif self._policy == "WTINYLFU": - cache = W2TinyLFU(maxsize=self.maxsize, on_evict=lambda x: self.on_evict(x,model=model)) + cache = W2TinyLFU(maxsize=self.maxsize) elif self._policy == "ARC": - cache = ARC(maxsize=self.maxsize, on_evict=lambda x: self.on_evict(x,model=model)) + cache = ARC(maxsize=self.maxsize) else: raise ValueError(f"Unknown policy {self.policy}") - cache.popitem = popitem_wrapper(cache.popitem, self.on_evict, self.clean_size) return cache - def put(self, objs: List[Tuple[Any, Any]], model: str): cache = self.get_cache(model) for key, value in objs: diff --git a/modelcache/manager/eviction/wtinylfu_cache.py b/modelcache/manager/eviction/wtinylfu_cache.py index f33a572..d5887f5 100644 --- a/modelcache/manager/eviction/wtinylfu_cache.py +++ b/modelcache/manager/eviction/wtinylfu_cache.py @@ -1,6 +1,6 @@ from cachetools import LRUCache, Cache +from readerwriterlock import rwlock import random -from typing import Any, Callable class CountMinSketch: def __init__(self, width=1024, depth=4, decay_interval=10000): @@ -16,7 +16,6 @@ def _hash(self, x, seed): def add(self, x): self.ops += 1 - # minimal increment est = self.estimate(x) for i, seed in enumerate(self.seeds): idx = self._hash(x, seed) @@ -35,9 +34,8 @@ def decay(self): for i in range(len(table)): table[i] >>= 1 - class W2TinyLFU(Cache): - def __init__(self, maxsize, window_pct=1, on_evict: Callable[[Any], None]=None): + def __init__(self, maxsize, window_pct=1): super().__init__(maxsize) self.window_size = max(1, int(maxsize * window_pct / 100)) rest = maxsize - self.window_size @@ -49,12 +47,13 @@ def __init__(self, maxsize, window_pct=1, on_evict: Callable[[Any], None]=None): self.protected = LRUCache(maxsize=self.protected_size) self.cms = CountMinSketch() - self.on_evict = on_evict self.data = {} + self._rw_lock = rwlock.RWLockWrite() def __setitem__(self, key, value): - self.data[key] = value - self._put(key) + with self._rw_lock.gen_wlock(): + self.data[key] = value + self._put(key) def __getitem__(self, key): val = self.get(key, default=None) @@ -66,10 +65,11 @@ def __contains__(self, key): return key in self.window or key in self.probation or key in self.protected def __delitem__(self, key): - self.data.pop(key, None) - self.window.pop(key, None) - self.probation.pop(key, None) - self.protected.pop(key, None) + with self._rw_lock.gen_wlock(): + self.data.pop(key, None) + self.window.pop(key, None) + self.probation.pop(key, None) + self.protected.pop(key, None) def get(self, key, default=None): if key in self.window: @@ -80,7 +80,6 @@ def get(self, key, default=None): return self.data.get(key, default) if key in self.probation: self.probation.pop(key) - # demote LRU from protected if full if len(self.protected) >= self.protected_size: demoted = next(iter(self.protected)) self.protected.pop(demoted) @@ -94,12 +93,10 @@ def _put(self, key): if key in self: return - # admission to window if len(self.window) < self.window_size: self.window[key] = True return - # window full: victim is LRU victim = next(iter(self.window)) self.window.pop(victim) @@ -107,19 +104,13 @@ def _put(self, key): self._admit_to_main(victim) self._admit_to_main(key) else: - # victim stronger or equal: victim enters main, key is dropped self._admit_to_main(victim) - # actually evicts key entirely - if self.on_evict: - self.on_evict(key) self.data.pop(key, None) def _admit_to_main(self, key): if key in self.protected or key in self.probation: return if self.probation_size == 0: - if self.on_evict: - self.on_evict(key) self.data.pop(key, None) return if len(self.probation) < self.probation_size: @@ -128,10 +119,13 @@ def _admit_to_main(self, key): evicted = next(iter(self.probation)) self.probation.pop(evicted) self.probation[key] = True - if self.on_evict: - self.on_evict(evicted) self.data.pop(evicted, None) else: - if self.on_evict: - self.on_evict(key) self.data.pop(key, None) + + def clear(self): + with self._rw_lock.gen_wlock(): + self.window.clear() + self.probation.clear() + self.protected.clear() + self.data.clear() \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 23ee700..2db7121 100644 --- a/requirements.txt +++ b/requirements.txt @@ -21,4 +21,4 @@ flagembedding==1.3.4 cryptography==45.0.2 sentence-transformers==4.1.0 pytest>=8.0 - +readerwriterlock==1.0.9 diff --git a/tests/test_arc_cache.py b/tests/test_arc_cache.py index 7958409..729cb86 100644 --- a/tests/test_arc_cache.py +++ b/tests/test_arc_cache.py @@ -1,19 +1,27 @@ import pytest +import threading +import time from modelcache.manager.eviction.arc_cache import ARC +# ----------- Fixtures ----------- + @pytest.fixture() def empty_arc(): + # Create an empty ARC cache for tests return ARC(maxsize=4) @pytest.fixture() def arc_with_data(): + # Create an ARC cache pre-filled with two items c = ARC(maxsize=4) c['a'] = 1 c['b'] = 2 return c +# ----------- Basic Functionality Tests ----------- + def test_setitem_adds_to_arc(empty_arc): - """Test __setitem__ adds a key-value pair to ARC.""" + """Test that __setitem__ adds a key-value pair to ARC.""" empty_arc['x'] = 123 assert 'x' in empty_arc assert empty_arc['x'] == 123 @@ -24,39 +32,80 @@ def test_setitem_overwrites_value(empty_arc): empty_arc['y'] = 55 assert empty_arc['y'] == 55 +def test_setitem_none_value(empty_arc): + """Test that __setitem__ can store None as a value.""" + empty_arc['foo'] = None + assert empty_arc['foo'] is None + +def test_duplicate_key_updates_value(empty_arc): + """Test that setting the same key twice updates the value.""" + empty_arc['dup'] = 10 + empty_arc['dup'] = 20 + assert empty_arc['dup'] == 20 + def test_getitem_returns_value(arc_with_data): - """Test __getitem__ returns the correct value if present.""" + """Test that __getitem__ returns the correct value if present.""" assert arc_with_data['a'] == 1 def test_getitem_raises_keyerror_on_missing(empty_arc): - """Test __getitem__ raises KeyError if key is missing.""" + """Test that __getitem__ raises KeyError if key is missing.""" with pytest.raises(KeyError): _ = empty_arc['nope'] def test_contains_true_for_present(arc_with_data): - """Test __contains__ returns True for a present key.""" + """Test that __contains__ returns True for a present key.""" assert 'b' in arc_with_data def test_contains_false_for_missing(arc_with_data): - """Test __contains__ returns False for missing key.""" + """Test that __contains__ returns False for missing key.""" assert 'notfound' not in arc_with_data def test_len_reports_active_cache_size(arc_with_data): - """Test __len__ reports only active items (T1 + T2).""" + """Test that __len__ reports only active items (T1 + T2).""" assert len(arc_with_data) == 2 arc_with_data['c'] = 3 assert len(arc_with_data) == 3 def test_pop_removes_key_from_one_list(arc_with_data): - """Test pop removes key from the first ARC list where it is found.""" + """Test that pop removes key from the first ARC list where it is found.""" arc_with_data['ghost'] = 9 - arc_with_data.b1['ghost'] = 9 + with arc_with_data._rw_lock.gen_wlock(): + arc_with_data.b1['ghost'] = 9 # Simulate ghost in B1 (protected by write lock) arc_with_data.pop('ghost') - assert 'ghost' not in arc_with_data.t1 - assert 'ghost' in arc_with_data.b1 + with arc_with_data._rw_lock.gen_rlock(): + assert 'ghost' not in arc_with_data.t1 + assert 'ghost' in arc_with_data.b1 + +def test_pop_missing_key_returns_default(empty_arc): + """Test that pop() returns default if key not found in any list.""" + assert empty_arc.pop('missing', default='sentinel') == 'sentinel' + +def test_pop_evicted_key_returns_default(empty_arc): + """Test that pop() on a recently evicted key returns default.""" + empty_arc['a'] = 1 + empty_arc['b'] = 2 + empty_arc['c'] = 3 + empty_arc['d'] = 4 + empty_arc['e'] = 5 + for key in ['a', 'b', 'c', 'd']: + try: + empty_arc.pop(key) + except KeyError: + pass + except Exception: + pytest.fail("Unexpected exception on pop after eviction.") + +def test_pop_with_none_default(empty_arc): + """Test pop returns None when default=None and key is missing.""" + assert empty_arc.pop('not_in_cache', default=None) is None + +def test_keyerror_on_missing_and_no_default(empty_arc): + """Test that pop() raises KeyError if no default is given and key is missing.""" + with pytest.raises(KeyError): + empty_arc.pop('never-there') def test_clear_removes_all_keys(empty_arc): - """Test clear() empties all lists and resets p.""" + """Test that clear() empties all lists and resets p.""" empty_arc['a'] = 1 empty_arc['b'] = 2 empty_arc.clear() @@ -66,114 +115,180 @@ def test_clear_removes_all_keys(empty_arc): assert len(empty_arc.b2) == 0 assert empty_arc.p == 0 +def test_clear_on_empty_cache(empty_arc): + """Test clear() does not raise when cache is already empty.""" + empty_arc.clear() + assert len(empty_arc) == 0 + +# ----------- Eviction and Promotion Mechanics ----------- + def test_evict_internal_evicts_when_over_capacity(): - """Test _evict_internal evicts oldest when ARC is full.""" - evicted = [] - c = ARC(maxsize=2, on_evict=lambda keys: evicted.extend(keys)) + """Test that _evict_internal evicts oldest when ARC is full.""" + c = ARC(maxsize=2) c['a'] = 1 c['b'] = 2 c['c'] = 3 assert len(c) == 2 + with c._rw_lock.gen_rlock(): + present = set(c.t1.keys()) | set(c.t2.keys()) + assert 'c' in present + assert len(present & {'a', 'b'}) == 1 + +def test_getitem_after_eviction(empty_arc): + """Test that accessing an evicted key raises KeyError.""" + empty_arc['x'] = 1 + empty_arc['y'] = 2 + empty_arc['z'] = 3 + empty_arc['w'] = 4 + empty_arc['v'] = 5 # Triggers eviction + evicted = [k for k in ['x', 'y', 'z', 'w'] if k not in empty_arc] assert len(evicted) >= 1 for k in evicted: - assert k in ['a', 'b'] - -def test_eviction_callback_is_called(): - """Test on_evict callback is called on eviction.""" - evicted = [] - c = ARC(maxsize=2, on_evict=lambda keys: evicted.extend(keys)) - c['x'] = 1 - c['y'] = 2 - c['z'] = 3 - assert len(evicted) > 0 - assert all(isinstance(k, str) for k in evicted) + with pytest.raises(KeyError): + _ = empty_arc[k] + +def test_setitem_multiple_evictions(): + """Test that multiple evictions in sequence do not corrupt the cache.""" + c = ARC(maxsize=2) + c['a'] = 1 + c['b'] = 2 + c['c'] = 3 + c['d'] = 4 + assert len(c) == 2 + with c._rw_lock.gen_rlock(): + present = set(c.t1.keys()) | set(c.t2.keys()) + assert len(present & {'a', 'b', 'c', 'd'}) == 2 + +def test_setitem_eviction_does_not_raise(empty_arc): + """Test that inserting over maxsize repeatedly never raises.""" + try: + for i in range(20): + empty_arc[f'k{i}'] = i + except Exception: + pytest.fail("Eviction during setitem should never raise.") def test_promote_from_t1_to_t2(empty_arc): """Test that accessing key in T1 promotes it to T2.""" empty_arc['foo'] = 10 - assert 'foo' in empty_arc.t1 + with empty_arc._rw_lock.gen_rlock(): + assert 'foo' in empty_arc.t1 _ = empty_arc['foo'] - assert 'foo' in empty_arc.t2 + with empty_arc._rw_lock.gen_rlock(): + assert 'foo' in empty_arc.t2 def test_refresh_in_t2_updates_order(empty_arc): - """Test that repeated access in T2 keeps item in T2.""" + """Test that repeated access in T2 keeps item in T2 and updates order.""" empty_arc['x'] = 1 - _ = empty_arc['x'] # promote to T2 + _ = empty_arc['x'] empty_arc['y'] = 2 - _ = empty_arc['x'] # refresh x in T2 - assert 'x' in empty_arc.t2 - assert empty_arc.t2.popitem(last=True)[0] == 'x' + _ = empty_arc['x'] + with empty_arc._rw_lock.gen_rlock(): + assert 'x' in empty_arc.t2 + assert empty_arc.t2.popitem(last=True)[0] == 'x' + +def test_setitem_duplicate_key_resets_position(empty_arc): + """Test that setting the same key again resets its position.""" + empty_arc['x'] = 10 + empty_arc['y'] = 11 + empty_arc['x'] = 99 + with empty_arc._rw_lock.gen_rlock(): + assert list(empty_arc.t1.keys())[-1] == 'x' + assert empty_arc['x'] == 99 def test_hit_in_ghost_lists_promotes_to_t2(empty_arc): - """Test access in ghost list B1 promotes key to T2.""" + """Test that access in ghost list B1 promotes key to T2.""" empty_arc['a'] = 1 empty_arc['b'] = 2 - empty_arc['c'] = 3 # triggers eviction to ghost B1 - if empty_arc.b1: - ghost_key = next(iter(empty_arc.b1)) + empty_arc['c'] = 3 + with empty_arc._rw_lock.gen_rlock(): + b1_keys = list(empty_arc.b1.keys()) + if b1_keys: + ghost_key = b1_keys[0] empty_arc.__missing__ = lambda key: 999 _ = empty_arc[ghost_key] - assert ghost_key in empty_arc.t2 - -def test_iter_lists_keys_in_order(arc_with_data): - """Test __iter__ yields keys from T1 and then T2.""" - arc_with_data['c'] = 3 - keys = list(iter(arc_with_data)) - expected = list(arc_with_data.t1.keys()) + list(arc_with_data.t2.keys()) - assert keys == expected - -def test_repr_outputs_status(empty_arc): - """Test __repr__ returns a string with cache stats.""" - r = repr(empty_arc) - assert r.startswith("ARC(") - assert "maxsize" in r - -# ----------- Additional/Edge case tests ----------- - -def test_pop_missing_key_returns_default(empty_arc): - """Test that pop() returns default if key not found in any list.""" - assert empty_arc.pop('missing', default='sentinel') == 'sentinel' - -def test_setitem_multiple_evictions(): - """Test multiple evictions in sequence do not corrupt the cache.""" - evicted = [] - c = ARC(maxsize=2, on_evict=lambda keys: evicted.extend(keys)) - c['a'] = 1 - c['b'] = 2 - c['c'] = 3 - c['d'] = 4 - assert len(c) == 2 - assert len(evicted) >= 2 - assert all(isinstance(k, str) for k in evicted) + with empty_arc._rw_lock.gen_rlock(): + assert ghost_key in empty_arc.t2 def test_access_promotes_b1_and_b2(empty_arc): - """Test accessing a key in B1 or B2 increases/decreases p appropriately.""" + """Test that accessing a key in B1 or B2 increases/decreases p appropriately.""" empty_arc['a'] = 1 empty_arc['b'] = 2 empty_arc['c'] = 3 - if empty_arc.b1: - ghost_key = next(iter(empty_arc.b1)) + with empty_arc._rw_lock.gen_rlock(): + b1_keys = list(empty_arc.b1.keys()) + if b1_keys: + ghost_key = b1_keys[0] empty_arc.__missing__ = lambda key: 555 - p_before = empty_arc.p + with empty_arc._rw_lock.gen_rlock(): + p_before = empty_arc.p _ = empty_arc[ghost_key] - assert empty_arc.p > p_before or empty_arc.p == empty_arc.maxsize + with empty_arc._rw_lock.gen_rlock(): + assert empty_arc.p > p_before or empty_arc.p == empty_arc.maxsize + +def test_evict_when_t1_empty_promotes_t2(empty_arc): + """ + Test that promoting an item to T2 keeps it in cache, while new items fill T1. + """ + empty_arc['a'] = 1 + _ = empty_arc['a'] # promote to T2 + empty_arc['b'] = 2 + empty_arc['c'] = 3 + empty_arc['d'] = 4 + empty_arc['e'] = 5 # should cause evictions + with empty_arc._rw_lock.gen_rlock(): + assert 'a' in empty_arc.t2 + t1_keys = list(empty_arc.t1.keys()) + t2_keys = list(empty_arc.t2.keys()) + assert len(t1_keys) + len(t2_keys) == empty_arc.maxsize + assert 'a' not in t1_keys + for k in t1_keys: + assert k in ['b', 'c', 'd', 'e'] def test_active_and_ghost_lists_dont_exceed_maxsize(): - """Test ghost lists (B1/B2) and active lists (T1/T2) don't exceed maxsize.""" + """Test that ghost lists (B1/B2) and active lists (T1/T2) don't exceed maxsize.""" c = ARC(maxsize=3) for k in ['a', 'b', 'c', 'd', 'e']: c[k] = ord(k) - # B1 + B2 should never be larger than maxsize - assert len(c.b1) + len(c.b2) <= c.maxsize - assert len(c.t1) + len(c.t2) <= c.maxsize + with c._rw_lock.gen_rlock(): + assert len(c.b1) + len(c.b2) <= c.maxsize + assert len(c.t1) + len(c.t2) <= c.maxsize def test_clear_resets_all_lists_and_p(empty_arc): """Test that clear() resets all lists and p after many ops.""" for k in 'abcd': empty_arc[k] = ord(k) empty_arc.clear() - assert not any([empty_arc.t1, empty_arc.t2, empty_arc.b1, empty_arc.b2]) - assert empty_arc.p == 0 + with empty_arc._rw_lock.gen_rlock(): + assert not any([empty_arc.t1, empty_arc.t2, empty_arc.b1, empty_arc.b2]) + assert empty_arc.p == 0 + +# ----------- Iteration and Representation ----------- + +def test_iter_lists_keys_in_order(arc_with_data): + """Test that __iter__ yields keys from T1 and then T2.""" + arc_with_data['c'] = 3 + keys = list(iter(arc_with_data)) + with arc_with_data._rw_lock.gen_rlock(): + expected = list(arc_with_data.t1.keys()) + list(arc_with_data.t2.keys()) + assert keys == expected + +def test_iter_empty_arc(empty_arc): + """Test that iterating an empty ARC yields nothing.""" + assert list(iter(empty_arc)) == [] + +def test_repr_outputs_status(empty_arc): + """Test that __repr__ returns a string with cache stats.""" + r = repr(empty_arc) + assert r.startswith("ARC(") + assert "maxsize" in r + +def test_repr_reflects_content(empty_arc): + """Test __repr__ shows correct lengths after operations.""" + empty_arc['foo'] = 123 + empty_arc['bar'] = 321 + r = repr(empty_arc) + assert f"len={len(empty_arc)}" in r + assert f"t1_len={len(empty_arc.t1)}" in r def test_repr_is_informative(empty_arc): """Test that __repr__ outputs all important stats.""" @@ -181,28 +296,271 @@ def test_repr_is_informative(empty_arc): r = repr(empty_arc) assert "t1_len" in r and "b1_len" in r and "p=" in r -def test_setitem_duplicate_key_resets_position(empty_arc): - """Test that setting the same key again resets its position.""" - empty_arc['x'] = 10 - empty_arc['y'] = 11 - empty_arc['x'] = 99 - # x should be last in t1 - assert list(empty_arc.t1.keys())[-1] == 'x' - assert empty_arc['x'] == 99 +# ----------- Concurrency and Locking ----------- -def test_eviction_of_promoted_key(): - """Test that a key promoted to T2 can still be evicted if capacity is exceeded.""" - evicted = [] - c = ARC(maxsize=2, on_evict=lambda keys: evicted.extend(keys)) - c['a'] = 1 - c['b'] = 2 - _ = c['a'] # Promote 'a' to T2 - c['c'] = 3 # Evict one of the keys - assert len(c) == 2 - assert len(evicted) >= 1 +def test_concurrent_setitem_and_getitem(): + """Test concurrent __setitem__ and __getitem__ do not corrupt cache state.""" + arc = ARC(maxsize=8) + keys = [f'k{i}' for i in range(16)] + exceptions = [] -def test_keyerror_on_missing_and_no_default(empty_arc): - """Test pop() raises KeyError if no default is given and key is missing.""" - with pytest.raises(KeyError): - empty_arc.pop('never-there') + def writer(): + for k in keys: + try: + arc[k] = ord(k[-1]) + time.sleep(0.001) + except Exception as e: + exceptions.append(e) + + def reader(): + for _ in range(20): + for k in keys: + try: + _ = arc.get(k, None) + except Exception as e: + exceptions.append(e) + time.sleep(0.001) + + threads = [threading.Thread(target=writer)] + [threading.Thread(target=reader) for _ in range(4)] + for t in threads: + t.start() + for t in threads: + t.join() + assert not exceptions, f"Exceptions in threads: {exceptions}" + +def test_concurrent_pop_and_setitem(): + """Test concurrent pop and setitem maintain cache integrity.""" + arc = ARC(maxsize=5) + for k in range(5): + arc[f'x{k}'] = k + + pop_exceptions = [] + set_exceptions = [] + + def popper(): + for _ in range(10): + try: + arc.pop(f'x{_ % 5}', default=None) + except KeyError: + pass # Acceptable in concurrency + except Exception as e: + pop_exceptions.append(e) + + def setter(): + for i in range(10, 20): + try: + arc[f'x{i % 5}'] = i + except Exception as e: + set_exceptions.append(e) + + t1 = threading.Thread(target=popper) + t2 = threading.Thread(target=setter) + t1.start() + t2.start() + t1.join() + t2.join() + assert not set_exceptions, f"Exceptions in setter: {set_exceptions}" + assert not pop_exceptions, f"Unexpected exceptions in popper: {pop_exceptions}" + +def test_concurrent_iterators_with_modifications(): + """Test that iterating keys while modifying cache is safe and doesn't throw.""" + arc = ARC(maxsize=7) + for i in range(7): + arc[f'k{i}'] = i + + iter_exceptions = [] + + def iterate(): + try: + for _ in range(10): + list(iter(arc)) + time.sleep(0.002) + except Exception as e: + iter_exceptions.append(e) + + def modifier(): + for i in range(10, 20): + arc[f'k{i%7}'] = i + time.sleep(0.001) + + t1 = threading.Thread(target=iterate) + t2 = threading.Thread(target=modifier) + t1.start() + t2.start() + t1.join() + t2.join() + assert not iter_exceptions, f"Exceptions during concurrent iteration: {iter_exceptions}" + +def test_concurrent_clear_and_get(): + """Test that clear and getitem do not deadlock or throw under concurrent access.""" + arc = ARC(maxsize=10) + for i in range(10): + arc[f'c{i}'] = i + + clear_exceptions = [] + get_exceptions = [] + + def clearer(): + for _ in range(5): + try: + arc.clear() + time.sleep(0.005) + except Exception as e: + clear_exceptions.append(e) + + def getter(): + for _ in range(15): + for i in range(10): + try: + _ = arc.get(f'c{i}', None) + except Exception as e: + get_exceptions.append(e) + time.sleep(0.001) + + t1 = threading.Thread(target=clearer) + t2 = threading.Thread(target=getter) + t1.start() + t2.start() + t1.join() + t2.join() + assert not clear_exceptions and not get_exceptions, f"Exceptions: {clear_exceptions}, {get_exceptions}" + +def test_lock_allows_multiple_readers_but_exclusive_writer(): + """Test rwlock allows multiple readers at once but only one writer at a time.""" + arc = ARC(maxsize=4) + shared_counter = 0 + read_count = [] + write_count = [] + + def reader(): + nonlocal shared_counter + with arc._rw_lock.gen_rlock(): + val = shared_counter + time.sleep(0.002) + read_count.append(val) + + def writer(): + nonlocal shared_counter + with arc._rw_lock.gen_wlock(): + current = shared_counter + shared_counter = current + 1 + time.sleep(0.003) + write_count.append(shared_counter) + + threads = [] + for _ in range(3): + threads.append(threading.Thread(target=reader)) + threads.append(threading.Thread(target=writer)) + for t in threads: + t.start() + for t in threads: + t.join() + assert any(r == read_count[0] for r in read_count[1:]), "Multiple readers did not overlap" + assert len(write_count) == 1 + +def test_concurrent_duplicate_setitem(): + """Test that concurrent setitem on the same key does not corrupt cache.""" + arc = ARC(maxsize=3) + exception_list = [] + def setter(val): + try: + for _ in range(20): + arc['dup'] = val + time.sleep(0.0005) + except Exception as e: + exception_list.append(e) + + threads = [threading.Thread(target=setter, args=(v,)) for v in range(3)] + for t in threads: + t.start() + for t in threads: + t.join() + assert 'dup' in arc + assert arc['dup'] in (0, 1, 2) + assert not exception_list, f"Exceptions: {exception_list}" + +def test_concurrent_clear_and_setitem(): + """Test that clear and setitem do not cause errors in parallel.""" + arc = ARC(maxsize=4) + set_errors = [] + clear_errors = [] + stop = threading.Event() + + def clearer(): + while not stop.is_set(): + try: + arc.clear() + time.sleep(0.001) + except Exception as e: + clear_errors.append(e) + + def setter(): + for i in range(30): + try: + arc[f'k{i%4}'] = i + time.sleep(0.0005) + except Exception as e: + set_errors.append(e) + stop.set() + + t1 = threading.Thread(target=clearer) + t2 = threading.Thread(target=setter) + t1.start() + t2.start() + t1.join() + t2.join() + assert not set_errors, f"Setitem exceptions: {set_errors}" + assert not clear_errors, f"Clear exceptions: {clear_errors}" + +def test_concurrent_pop_with_contention(): + """Test multiple threads popping the same and different keys.""" + arc = ARC(maxsize=2) + for k in ('a', 'b'): + arc[k] = k + errors = [] + def popper(key): + for _ in range(8): + try: + arc.pop(key, default=None) + time.sleep(0.0005) + except Exception as e: + errors.append(e) + t1 = threading.Thread(target=popper, args=('a',)) + t2 = threading.Thread(target=popper, args=('b',)) + t3 = threading.Thread(target=popper, args=('a',)) + t1.start() + t2.start() + t3.start() + t1.join() + t2.join() + t3.join() + assert not errors, f"Exceptions: {errors}" + +def test_concurrent_len_and_setitem(): + """Test that __len__ and __setitem__ are safe in concurrent usage.""" + arc = ARC(maxsize=10) + errors = [] + stop = threading.Event() + def setter(): + for i in range(50): + try: + arc[f'k{i%10}'] = i + time.sleep(0.0002) + except Exception as e: + errors.append(e) + stop.set() + def length_checker(): + while not stop.is_set(): + try: + _ = len(arc) + time.sleep(0.0002) + except Exception as e: + errors.append(e) + t1 = threading.Thread(target=setter) + t2 = threading.Thread(target=length_checker) + t1.start() + t2.start() + t1.join() + t2.join() + assert not errors, f"Exceptions: {errors}" diff --git a/tests/test_wtinylfu_cache.py b/tests/test_wtinylfu_cache.py index 21a25d1..3218d0a 100644 --- a/tests/test_wtinylfu_cache.py +++ b/tests/test_wtinylfu_cache.py @@ -1,12 +1,18 @@ import pytest +import threading +import time from modelcache.manager.eviction.wtinylfu_cache import W2TinyLFU, CountMinSketch +# ----------- Fixtures ----------- + @pytest.fixture() def empty_cache(): + # Returns an empty W2TinyLFU cache for tests return W2TinyLFU(maxsize=10, window_pct=50) @pytest.fixture() def cache_with_data(): + # Returns a W2TinyLFU cache with two preset items c = W2TinyLFU(maxsize=10, window_pct=50) c['a'] = 1 c['b'] = 2 @@ -14,8 +20,11 @@ def cache_with_data(): @pytest.fixture() def cms(): + # Returns a CountMinSketch for CMS tests return CountMinSketch(width=16, depth=2, decay_interval=5) +# ----------- Basic Functionality ----------- + def test_setitem_adds_to_cache(empty_cache): """Test that __setitem__ adds a new key-value pair to the cache.""" empty_cache['x'] = 100 @@ -78,53 +87,30 @@ def test_window_size_clamped_at_least_one(): c['a'] = 1 assert len(c.window) <= c.window_size -def test_eviction_callback_at_least_one_eviction(): - """Test that eviction callback is called when capacity exceeded.""" - evicted = [] - c = W2TinyLFU(maxsize=3, window_pct=33, on_evict=evicted.append) - c['a'] = 1 - c['b'] = 2 - c['c'] = 3 - c['d'] = 4 - assert len(evicted) >= 1 - assert all(k in ('a', 'b', 'c', 'd') for k in evicted) - -def test_eviction_callback_evicted_keys_are_gone(): - """Test that evicted keys are not present in the cache.""" - evicted = [] - c = W2TinyLFU(maxsize=3, window_pct=33, on_evict=evicted.append) - keys = ['a', 'b', 'c', 'd'] - for i, k in enumerate(keys): - c[k] = i - for k in evicted: - assert k not in c - -def test_eviction_callback_not_called_when_under_capacity(): - """Test that eviction callback is not called when under capacity.""" - evicted = [] - c = W2TinyLFU(maxsize=4, window_pct=50, on_evict=evicted.append) - c['one'] = 1 - c['two'] = 2 - assert len(evicted) == 0 - -def test_eviction_callback_with_chain_eviction(): - """Test that multiple evictions can occur in chain scenarios.""" - evicted = [] - c = W2TinyLFU(maxsize=2, window_pct=50, on_evict=evicted.append) - c['x'] = 1 - c['y'] = 2 - c['z'] = 3 - c['w'] = 4 - assert set(evicted).issubset({'x', 'y', 'z', 'w'}) - assert len(evicted) >= 1 +def test_clear_empties_cache(empty_cache): + """Test that clear() empties the cache.""" + empty_cache['x'] = 1 + empty_cache['y'] = 2 + empty_cache.clear() + assert len(empty_cache.data) == 0 + assert list(empty_cache.window) == [] + assert list(empty_cache.probation) == [] + assert list(empty_cache.protected) == [] + +# ----------- Eviction and Frequency ----------- def test_evicted_key_not_accessible(): """Test that accessing an evicted key raises KeyError.""" c = W2TinyLFU(maxsize=2) c['a'], c['b'] = 1, 2 c['c'] = 3 + # Only two elements can be present, so one must be gone with pytest.raises(KeyError): - _ = c['a'] + # Either 'a' or 'b' must be gone; test both if needed + try: + _ = c['a'] + except KeyError: + _ = c['b'] def test_eviction_until_empty_and_reusability(): """Test cache remains usable after all items have been evicted.""" @@ -152,7 +138,9 @@ def test_low_freq_key_evicted(): c['b'] = 2 c['c'] = 3 c['d'] = 4 - assert 'a' not in c + # At least one of the early keys must be gone (usually 'a') + evicted = {'a', 'b', 'c'} - set([k for k in c]) + assert len(evicted) >= 1 @pytest.mark.parametrize("hits, expected_in_cache", [ (0, False), @@ -168,10 +156,7 @@ def test_access_promotes_key(hits, expected_in_cache): if i < hits: c.get('a') c[k] = ord(k) - if hits == 0: - assert 'a' not in c - else: - assert 'a' in c + assert ('a' in c) == expected_in_cache def test_cms_estimate_increases_and_decays(cms): """Test that CountMinSketch increases and decays correctly.""" @@ -183,6 +168,8 @@ def test_cms_estimate_increases_and_decays(cms): after_decay = cms.estimate('test') assert after_decay <= after_add +# ----------- Segment and Admission Logic ----------- + def test_admit_to_main_adds_to_probation(): """Test _admit_to_main adds new key to probation segment.""" c = W2TinyLFU(maxsize=10, window_pct=50) @@ -194,17 +181,17 @@ def test_admit_to_main_adds_to_probation(): def test_admit_to_main_evicts_when_probation_full(): """Test _admit_to_main evicts LRU key when probation is full.""" - evicted = [] - c = W2TinyLFU(maxsize=4, window_pct=25, on_evict=evicted.append) + c = W2TinyLFU(maxsize=4, window_pct=25) for i in range(c.probation_size): c._admit_to_main(f'p{i}') c.data[f'p{i}'] = i extra = 'extra' c.data[extra] = 999 + old_probation_keys = set(c.probation.keys()) c._admit_to_main(extra) assert len(c.probation) == c.probation_size assert extra in c.probation - assert len(evicted) >= 1 + assert len(old_probation_keys - set(c.probation.keys())) >= 1 def test_admit_to_main_noop_if_already_present(): """Test _admit_to_main does nothing if key already present.""" @@ -227,11 +214,195 @@ def test_put_key_in_window_and_main(): total = len(c.window) + len(c.probation) + len(c.protected) assert total <= c.maxsize -def test_put_eviction_callback_called(): - """Test that the eviction callback is invoked when needed.""" - evicted = [] - c = W2TinyLFU(maxsize=2, on_evict=evicted.append) - c['a'] = 1 - c['b'] = 2 - c['c'] = 3 - assert len(evicted) >= 1 +# ----------- Extra Edge Cases ----------- + +def test_clear_on_empty_cache(): + """Test clear() on an already empty cache does not fail.""" + c = W2TinyLFU(maxsize=3) + c.clear() + assert len(c.data) == 0 + +def test_get_returns_default_if_not_present(): + """Test get() returns default if the key is missing.""" + c = W2TinyLFU(maxsize=3) + assert c.get('notfound', default=777) == 777 + +def test_cache_survives_rapid_inserts_and_deletes(): + """Test cache remains consistent under rapid inserts/deletes.""" + c = W2TinyLFU(maxsize=3) + for i in range(30): + c[f'k{i%3}'] = i + if i % 2 == 0: + del c[f'k{(i+1)%3}'] + assert len(c.data) <= 3 + +def test_cache_never_exceeds_maxsize(): + """Test that cache never exceeds its declared maxsize.""" + c = W2TinyLFU(maxsize=5) + for i in range(20): + c[f'x{i}'] = i + assert len(c.window) + len(c.probation) + len(c.protected) <= 5 + +# ----------- Concurrency Tests ----------- + +def test_concurrent_setitem_and_getitem(): + """Test concurrent __setitem__ and __getitem__ do not corrupt cache state.""" + cache = W2TinyLFU(maxsize=8, window_pct=50) + keys = [f'k{i}' for i in range(16)] + exceptions = [] + + def writer(): + for k in keys: + try: + cache[k] = ord(k[-1]) + time.sleep(0.001) + except Exception as e: + exceptions.append(e) + + def reader(): + for _ in range(20): + for k in keys: + try: + _ = cache.get(k, None) + except Exception as e: + exceptions.append(e) + time.sleep(0.001) + + threads = [threading.Thread(target=writer)] + [threading.Thread(target=reader) for _ in range(4)] + for t in threads: + t.start() + for t in threads: + t.join() + assert not exceptions, f"Exceptions in threads: {exceptions}" + +def test_concurrent_delitem_and_setitem(): + """Test concurrent __delitem__ and __setitem__ do not cause errors or corruption.""" + cache = W2TinyLFU(maxsize=6, window_pct=50) + for k in range(6): + cache[f'x{k}'] = k + + del_exceptions = [] + set_exceptions = [] + + def deleter(): + for _ in range(12): + try: + del cache[f'x{_ % 6}'] + except KeyError: + pass # Acceptable: may not be present + except Exception as e: + del_exceptions.append(e) + time.sleep(0.001) + + def setter(): + for i in range(12, 24): + try: + cache[f'x{i % 6}'] = i + except Exception as e: + set_exceptions.append(e) + time.sleep(0.001) + + t1 = threading.Thread(target=deleter) + t2 = threading.Thread(target=setter) + t1.start() + t2.start() + t1.join() + t2.join() + assert not del_exceptions, f"Exceptions in deleter: {del_exceptions}" + assert not set_exceptions, f"Exceptions in setter: {set_exceptions}" + +def test_concurrent_iterators_with_modifications(): + """Test that iterating over keys while modifying the cache does not throw.""" + cache = W2TinyLFU(maxsize=7, window_pct=50) + for i in range(7): + cache[f'k{i}'] = i + + iter_exceptions = [] + + def iterate(): + try: + for _ in range(10): + list(cache.data.keys()) + time.sleep(0.002) + except Exception as e: + iter_exceptions.append(e) + + def modifier(): + for i in range(10, 20): + cache[f'k{i%7}'] = i + time.sleep(0.001) + + t1 = threading.Thread(target=iterate) + t2 = threading.Thread(target=modifier) + t1.start() + t2.start() + t1.join() + t2.join() + assert not iter_exceptions, f"Exceptions during concurrent iteration: {iter_exceptions}" + +def test_concurrent_clear_and_get(): + """Test that clear and get do not deadlock or throw under concurrent access.""" + cache = W2TinyLFU(maxsize=10, window_pct=50) + for i in range(10): + cache[f'c{i}'] = i + + clear_exceptions = [] + get_exceptions = [] + + def clearer(): + for _ in range(5): + try: + cache.clear() + time.sleep(0.005) + except Exception as e: + clear_exceptions.append(e) + + def getter(): + for _ in range(15): + for i in range(10): + try: + _ = cache.get(f'c{i}', None) + except Exception as e: + get_exceptions.append(e) + time.sleep(0.001) + + t1 = threading.Thread(target=clearer) + t2 = threading.Thread(target=getter) + t1.start() + t2.start() + t1.join() + t2.join() + assert not clear_exceptions, f"Exceptions: {clear_exceptions}" + assert not get_exceptions, f"Exceptions: {get_exceptions}" + +def test_lock_allows_multiple_readers_but_exclusive_writer(): + """Test rwlock allows multiple readers at once but only one writer at a time.""" + cache = W2TinyLFU(maxsize=4, window_pct=50) + shared_counter = [0] + read_count = [] + write_count = [] + + def reader(): + with cache._rw_lock.gen_rlock(): + val = shared_counter[0] + time.sleep(0.002) + read_count.append(val) + + def writer(): + with cache._rw_lock.gen_wlock(): + current = shared_counter[0] + shared_counter[0] = current + 1 + time.sleep(0.003) + write_count.append(shared_counter[0]) + + threads = [] + for _ in range(3): + threads.append(threading.Thread(target=reader)) + threads.append(threading.Thread(target=writer)) + for t in threads: + t.start() + for t in threads: + t.join() + assert any(r == read_count[0] for r in read_count[1:]), "Multiple readers did not overlap" + assert len(write_count) == 1 + diff --git a/websocket4modelcache.py b/websocket4modelcache.py index fe99cd5..4543f03 100644 --- a/websocket4modelcache.py +++ b/websocket4modelcache.py @@ -15,7 +15,7 @@ async def lifespan(app: FastAPI): sql_storage="mysql", vector_storage="milvus", embedding_model=EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2, - embedding_workers_num=2 + embedding_workers_num=6 ) yield From d8afc32c4fa1d04df1fe39839280133f13b3113d Mon Sep 17 00:00:00 2001 From: Yuval-Roth Date: Mon, 16 Jun 2025 13:20:32 +0300 Subject: [PATCH 33/36] Updated packages in requirements.txt to support python 12 --- requirements.txt | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/requirements.txt b/requirements.txt index 2db7121..91ab0f6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,24 +1,24 @@ cachetools==5.3.1 DBUtils==1.4 -Flask==3.0.0 -numpy==1.24.4 -onnxruntime==1.16.1 +Flask==3.1.1 +numpy==2.2.6 +onnxruntime==1.22.0 openai==0.28.1 pymilvus==2.5.9 PyMySQL==1.1.1 Requests==2.32.3 torch==2.7.0 -transformers==4.44.2 -faiss-cpu==1.7.4 +transformers==4.52.4 +faiss-cpu==1.11.0 redis==5.0.1 modelscope==1.26.0 -fastapi==0.115.12 +fastapi==0.115.9 uvicorn==0.34.3 -chromadb==0.5.23 +chromadb==1.0.12 elasticsearch==7.10.0 snowflake-id==1.0.2 -flagembedding==1.3.4 +flagembedding==1.3.5 cryptography==45.0.2 sentence-transformers==4.1.0 -pytest>=8.0 +pytest==8.0 readerwriterlock==1.0.9 From 29c9e132820bc73d028226c59b491c01945a07c6 Mon Sep 17 00:00:00 2001 From: llbbl Date: Wed, 25 Jun 2025 08:24:04 -0500 Subject: [PATCH 34/36] feat: Set up comprehensive Python testing infrastructure with Poetry - Migrated from requirements.txt to Poetry package management - Added pytest, pytest-cov, and pytest-mock as dev dependencies - Configured pytest with coverage reporting (80% threshold) - Created test directory structure with unit/integration folders - Added comprehensive shared fixtures in conftest.py - Set up test markers for unit, integration, and slow tests - Created validation tests to verify infrastructure setup - Updated .gitignore with testing and Claude-related entries - Configured Poetry scripts for running tests (test/tests commands) --- .gitignore | 46 +- poetry.lock | 7703 ++++++++++++++++++++++++++++++++ pyproject.toml | 101 + tests/__init__.py | 0 tests/conftest.py | 218 + tests/integration/__init__.py | 0 tests/test_setup_validation.py | 143 + tests/unit/__init__.py | 0 8 files changed, 8210 insertions(+), 1 deletion(-) create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 tests/__init__.py create mode 100644 tests/conftest.py create mode 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configuration dictionary for testing. + + Returns: + Dict[str, Any]: Mock configuration + """ + return { + "cache_dir": "/tmp/test_cache", + "max_cache_size": 1000, + "ttl": 3600, + "embedding_model": "test-model", + "similarity_threshold": 0.8, + "vector_dimension": 768, + "batch_size": 32, + "database": { + "type": "memory", + "host": "localhost", + "port": 6379, + "password": None + } + } + + +@pytest.fixture +def mock_embedding(): + """ + Mock embedding object for testing. + + Returns: + MagicMock: Mock embedding with common methods + """ + mock = MagicMock() + mock.embed.return_value = [0.1] * 768 # Default 768-dim embedding + mock.embed_batch.return_value = [[0.1] * 768] * 10 + mock.dimension = 768 + mock.model_name = "test-embedding-model" + return mock + + +@pytest.fixture +def mock_cache_manager(): + """ + Mock cache manager for testing. + + Returns: + MagicMock: Mock cache manager with common methods + """ + mock = MagicMock() + mock.get.return_value = None + mock.set.return_value = True + mock.delete.return_value = True + mock.clear.return_value = True + mock.size.return_value = 0 + return mock + + +@pytest.fixture +def sample_vector_data(): + """ + Sample vector data for testing vector operations. + + Returns: + Dict[str, Any]: Sample vector data + """ + return { + "id": "test_vector_001", + "vector": [0.1, 0.2, 0.3, 0.4, 0.5] * 153 + [0.6, 0.7, 0.8], # 768 dimensions + "metadata": { + "source": "test", + "timestamp": 1234567890, + "model": "test-model" + } + } + + +@pytest.fixture +def mock_redis_client(): + """ + Mock Redis client for testing Redis-based operations. + + Returns: + MagicMock: Mock Redis client + """ + mock = MagicMock() + mock.get.return_value = None + mock.set.return_value = True + mock.delete.return_value = 1 + mock.exists.return_value = 0 + mock.expire.return_value = True + mock.ttl.return_value = -2 + return mock + + +@pytest.fixture +def mock_milvus_client(): + """ + Mock Milvus client for testing vector database operations. + + Returns: + MagicMock: Mock Milvus client + """ + mock = MagicMock() + mock.create_collection.return_value = True + mock.insert.return_value = MagicMock(primary_keys=[1, 2, 3]) + mock.search.return_value = [[]] + mock.query.return_value = [] + mock.delete.return_value = MagicMock(delete_count=1) + return mock + + +@pytest.fixture(autouse=True) +def reset_environment(): + """ + Reset environment variables before each test. + """ + # Store original env vars + original_env = os.environ.copy() + + # Set test environment variables + os.environ["MODELCACHE_ENV"] = "test" + os.environ["MODELCACHE_LOG_LEVEL"] = "DEBUG" + + yield + + # Restore original env vars + os.environ.clear() + os.environ.update(original_env) + + +@pytest.fixture +def sample_text_data(): + """ + Sample text data for testing text processing. + + Returns: + List[str]: List of sample texts + """ + return [ + "This is a test sentence for modelcache.", + "Machine learning models need efficient caching.", + "Vector embeddings help with semantic search.", + "Testing is important for code quality.", + "PyTest makes testing in Python easier." + ] + + +@pytest.fixture +def mock_http_response(): + """ + Mock HTTP response for testing API calls. + + Returns: + MagicMock: Mock response object + """ + mock = MagicMock() + mock.status_code = 200 + mock.json.return_value = {"status": "success", "data": {}} + mock.text = '{"status": "success", "data": {}}' + mock.headers = {"Content-Type": "application/json"} + return mock + + +# Pytest configuration hooks +def pytest_configure(config): + """ + Configure pytest with custom settings. + """ + # Add custom markers description + config.addinivalue_line( + "markers", "unit: mark test as a unit test" + ) + config.addinivalue_line( + "markers", "integration: mark test as an integration test" + ) + config.addinivalue_line( + "markers", "slow: mark test as slow running" + ) + + +def pytest_collection_modifyitems(config, items): + """ + Modify test collection to add markers based on test location. + """ + for item in items: + # Auto-mark tests based on their location + if "unit" in str(item.fspath): + item.add_marker(pytest.mark.unit) + elif "integration" in str(item.fspath): + item.add_marker(pytest.mark.integration) \ No newline at end of file diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_setup_validation.py b/tests/test_setup_validation.py new file mode 100644 index 0000000..c5ed40b --- /dev/null +++ b/tests/test_setup_validation.py @@ -0,0 +1,143 @@ +""" +Validation tests to ensure the testing infrastructure is set up correctly. +""" +import pytest +import sys +from pathlib import Path + + +class TestSetupValidation: + """Test class to validate the testing infrastructure setup.""" + + def test_pytest_installed(self): + """Verify pytest is installed and importable.""" + import pytest + assert pytest.__version__ + + def test_pytest_cov_installed(self): + """Verify pytest-cov is installed.""" + import pytest_cov + assert pytest_cov + + def test_pytest_mock_installed(self): + """Verify pytest-mock is installed.""" + import pytest_mock + assert pytest_mock + + def test_modelcache_importable(self): + """Verify the main modelcache package can be imported.""" + import modelcache + assert modelcache + + def test_project_structure(self): + """Verify the expected project structure exists.""" + project_root = Path(__file__).parent.parent + + # Check main directories + assert (project_root / "modelcache").exists() + assert (project_root / "modelcache_mm").exists() + assert (project_root / "tests").exists() + assert (project_root / "tests" / "unit").exists() + assert (project_root / "tests" / "integration").exists() + + # Check configuration files + assert (project_root / "pyproject.toml").exists() + + @pytest.mark.unit + def test_unit_marker(self): + """Test that unit marker works correctly.""" + assert True + + @pytest.mark.integration + def test_integration_marker(self): + """Test that integration marker works correctly.""" + assert True + + @pytest.mark.slow + def test_slow_marker(self): + """Test that slow marker works correctly.""" + assert True + + def test_fixtures_available(self, temp_dir, mock_config, mock_embedding): + """Test that custom fixtures are available and working.""" + # Test temp_dir fixture + assert temp_dir.exists() + assert temp_dir.is_dir() + + # Test mock_config fixture + assert isinstance(mock_config, dict) + assert "cache_dir" in mock_config + assert "embedding_model" in mock_config + + # Test mock_embedding fixture + assert hasattr(mock_embedding, "embed") + assert hasattr(mock_embedding, "dimension") + + def test_sample_data_fixtures(self, sample_vector_data, sample_text_data): + """Test that sample data fixtures provide expected data.""" + # Test vector data + assert isinstance(sample_vector_data, dict) + assert "id" in sample_vector_data + assert "vector" in sample_vector_data + assert len(sample_vector_data["vector"]) == 768 + + # Test text data + assert isinstance(sample_text_data, list) + assert len(sample_text_data) > 0 + assert all(isinstance(text, str) for text in sample_text_data) + + def test_mock_fixtures(self, mock_redis_client, mock_milvus_client, mock_cache_manager): + """Test that mock fixtures are properly configured.""" + # Test Redis mock + assert mock_redis_client.get("test") is None + assert mock_redis_client.set("test", "value") is True + + # Test Milvus mock + assert hasattr(mock_milvus_client, "search") + assert hasattr(mock_milvus_client, "insert") + + # Test cache manager mock + assert mock_cache_manager.get("test") is None + assert mock_cache_manager.set("test", "value") is True + + def test_environment_reset(self): + """Test that environment is properly set for testing.""" + import os + assert os.environ.get("MODELCACHE_ENV") == "test" + assert os.environ.get("MODELCACHE_LOG_LEVEL") == "DEBUG" + + def test_coverage_configured(self): + """Test that coverage is properly configured.""" + # This test will be meaningful when running with coverage + # For now, just ensure the test runs + assert True + + +@pytest.mark.unit +class TestUnitTestValidation: + """Validate unit test setup.""" + + def test_unit_tests_discoverable(self): + """Ensure unit tests can be discovered and run.""" + assert True + + def test_unit_test_isolation(self, temp_dir): + """Ensure unit tests have proper isolation with temp directories.""" + test_file = temp_dir / "test.txt" + test_file.write_text("test content") + assert test_file.exists() + assert test_file.read_text() == "test content" + + +@pytest.mark.integration +class TestIntegrationTestValidation: + """Validate integration test setup.""" + + def test_integration_tests_discoverable(self): + """Ensure integration tests can be discovered and run.""" + assert True + + def test_integration_mock_available(self, mock_http_response): + """Ensure integration tests have access to HTTP mocks.""" + assert mock_http_response.status_code == 200 + assert mock_http_response.json() == {"status": "success", "data": {}} \ No newline at end of file diff --git a/tests/unit/__init__.py b/tests/unit/__init__.py new file mode 100644 index 0000000..e69de29 From 15da47de3907aee43ee47244c6e16c35457f9aec Mon Sep 17 00:00:00 2001 From: Yuval-Roth Date: Mon, 16 Jun 2025 17:08:57 +0300 Subject: [PATCH 35/36] Optimizations and fixes * Updates to EmbeddingDispatcher: Added catch for exceptions in worker, set the processes to run in high priority * Offloaded some CPU-intensive and blocking code in adapt_insert and adapt_query to a background thread instead of having it run on the main asyncio event-loop * Fixed not inserting into memory cache after memory cache miss. * Fixes in WTINYLFU memory cache class. * Replaced hardcoded similarity threshold in cosine similarity with dynamic value Co-authored-by: olgaoznovich Co-authored-by: Yuval-Roth Co-authored-by: omerdor001 Co-authored-by: adiaybgu --- .gitignore | 2 +- modelcache/adapter/adapter.py | 8 ++-- modelcache/adapter/adapter_insert.py | 4 +- modelcache/adapter/adapter_query.py | 22 +++++---- modelcache/adapter/adapter_register.py | 9 +++- modelcache/adapter/adapter_remove.py | 16 +++++-- modelcache/cache.py | 48 +++++++++---------- modelcache/embedding/base.py | 18 ++++++- modelcache/embedding/embedding_dispatcher.py | 21 +++++--- modelcache/manager/data_manager.py | 21 ++++---- modelcache/manager/eviction/wtinylfu_cache.py | 15 ++++-- modelcache/manager/scalar_data/sql_storage.py | 6 +-- websocket4modelcache.py | 2 +- 13 files changed, 116 insertions(+), 76 deletions(-) diff --git a/.gitignore b/.gitignore index 0e57011..e31cca2 100644 --- a/.gitignore +++ b/.gitignore @@ -93,7 +93,7 @@ celerybeat.pid # Environments .env -.venv +.venv* env/ venv/ ENV/ diff --git a/modelcache/adapter/adapter.py b/modelcache/adapter/adapter.py index d62278d..2425539 100644 --- a/modelcache/adapter/adapter.py +++ b/modelcache/adapter/adapter.py @@ -35,9 +35,9 @@ async def create_insert(cls, *args, **kwargs): return str(e) @classmethod - def create_remove(cls, *args, **kwargs): + async def create_remove(cls, *args, **kwargs): try: - return adapt_remove( + return await adapt_remove( *args, **kwargs ) @@ -46,9 +46,9 @@ def create_remove(cls, *args, **kwargs): return str(e) @classmethod - def create_register(cls, *args, **kwargs): + async def create_register(cls, *args, **kwargs): try: - return adapt_register( + return await adapt_register( *args, **kwargs ) diff --git a/modelcache/adapter/adapter_insert.py b/modelcache/adapter/adapter_insert.py index b4d1f6a..757e0c8 100644 --- a/modelcache/adapter/adapter_insert.py +++ b/modelcache/adapter/adapter_insert.py @@ -16,7 +16,6 @@ async def adapt_insert(*args, **kwargs): pre_embedding_data_list = [] embedding_futures_list = [] - # embedding_data_list = [] llm_data_list = [] for row in chat_info: @@ -37,7 +36,8 @@ async def adapt_insert(*args, **kwargs): embedding_data_list = await asyncio.gather(*embedding_futures_list) - chat_cache.data_manager.save( + await asyncio.to_thread( + chat_cache.data_manager.save, pre_embedding_data_list, llm_data_list, embedding_data_list, diff --git a/modelcache/adapter/adapter_query.py b/modelcache/adapter/adapter_query.py index 2eb8fa4..7ce1ba0 100644 --- a/modelcache/adapter/adapter_query.py +++ b/modelcache/adapter/adapter_query.py @@ -1,4 +1,5 @@ # -*- coding: utf-8 -*- +import asyncio import logging from modelcache.embedding import MetricType from modelcache.utils.time import time_cal @@ -24,17 +25,20 @@ async def adapt_query(cache_data_convert, *args, **kwargs): cache_obj=chat_cache )(pre_embedding_data) - cache_data_list = time_cal( + search_time_cal = time_cal( chat_cache.data_manager.search, func_name="vector_search", report_func=chat_cache.report.search, cache_obj=chat_cache - )( + ) + cache_data_list = await asyncio.to_thread( + search_time_cal, embedding_data, extra_param=context.get("search_func", None), top_k=kwargs.pop("top_k", -1), model=model ) + cache_answers = [] cache_questions = [] cache_ids = [] @@ -43,7 +47,7 @@ async def adapt_query(cache_data_convert, *args, **kwargs): if chat_cache.similarity_metric_type == MetricType.COSINE: cosine_similarity = cache_data_list[0][0] # This code uses the built-in cosine similarity evaluation in milvus - if cosine_similarity < 0.9: + if cosine_similarity < chat_cache.similarity_threshold: return None elif chat_cache.similarity_metric_type == MetricType.L2: ## this is the code that uses L2 for similarity evaluation @@ -87,8 +91,9 @@ async def adapt_query(cache_data_convert, *args, **kwargs): reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=False) for cache_data in cache_data_list: primary_id = cache_data[1] - ret = chat_cache.data_manager.get_scalar_data( - cache_data, extra_param=context.get("get_scalar_data", None),model=model + ret = await asyncio.to_thread( + chat_cache.data_manager.get_scalar_data, + cache_data, extra_param=context.get("get_scalar_data", None), model=model ) if ret is None: continue @@ -133,8 +138,9 @@ async def adapt_query(cache_data_convert, *args, **kwargs): # 不使用 reranker 时，走原来的逻辑 for cache_data in cache_data_list: primary_id = cache_data[1] - ret = chat_cache.data_manager.get_scalar_data( - cache_data, extra_param=context.get("get_scalar_data", None),model=model + ret = await asyncio.to_thread( + chat_cache.data_manager.get_scalar_data, + cache_data, extra_param=context.get("get_scalar_data", None), model=model ) if ret is None: continue @@ -204,7 +210,7 @@ async def adapt_query(cache_data_convert, *args, **kwargs): ) # 更新命中次数 try: - chat_cache.data_manager.update_hit_count(return_id) + asyncio.create_task(asyncio.to_thread(chat_cache.data_manager.update_hit_count,return_id)) except Exception: logging.info('update_hit_count except, please check!') diff --git a/modelcache/adapter/adapter_register.py b/modelcache/adapter/adapter_register.py index 811bcec..c4d0317 100644 --- a/modelcache/adapter/adapter_register.py +++ b/modelcache/adapter/adapter_register.py @@ -1,11 +1,16 @@ # -*- coding: utf-8 -*- +import asyncio -def adapt_register(*args, **kwargs): +async def adapt_register(*args, **kwargs): chat_cache = kwargs.pop("cache_obj") model = kwargs.pop("model", None) if model is None or len(model) == 0: return ValueError('') - register_resp = chat_cache.data_manager.create_index(model) + register_resp = await asyncio.to_thread( + chat_cache.data_manager.create_index, + model + ) + return register_resp diff --git a/modelcache/adapter/adapter_remove.py b/modelcache/adapter/adapter_remove.py index ca00fc1..aea0284 100644 --- a/modelcache/adapter/adapter_remove.py +++ b/modelcache/adapter/adapter_remove.py @@ -1,8 +1,10 @@ # -*- coding: utf-8 -*- -from modelcache.utils.error import NotInitError, RemoveError +import asyncio +from modelcache.utils.error import RemoveError -def adapt_remove(*args, **kwargs): + +async def adapt_remove(*args, **kwargs): chat_cache = kwargs.pop("cache_obj") model = kwargs.pop("model", None) remove_type = kwargs.pop("remove_type", None) @@ -13,9 +15,15 @@ def adapt_remove(*args, **kwargs): # delete data if remove_type == 'delete_by_id': id_list = kwargs.pop("id_list", []) - resp = chat_cache.data_manager.delete(id_list, model=model) + resp = await asyncio.to_thread( + chat_cache.data_manager.delete, + id_list, model=model + ) elif remove_type == 'truncate_by_model': - resp = chat_cache.data_manager.truncate(model) + resp = await asyncio.to_thread( + chat_cache.data_manager.truncate, + model + ) else: # resp = "remove_type_error" raise RemoveError() diff --git a/modelcache/cache.py b/modelcache/cache.py index 76d98fc..9aaa224 100644 --- a/modelcache/cache.py +++ b/modelcache/cache.py @@ -27,13 +27,6 @@ #==================== Cache class definition =========================# #=====================================================================# -executor = ThreadPoolExecutor(max_workers=2) - -def response_text(cache_resp): - return cache_resp['data'] - -def response_hitquery(cache_resp): - return cache_resp['hitQuery'] # noinspection PyMethodMayBeStatic class Cache: @@ -80,11 +73,16 @@ def close(): modelcache_log.error(e) def save_query_resp(self, query_resp_dict, **kwargs): - self.data_manager.save_query_resp(query_resp_dict, **kwargs) + asyncio.create_task(asyncio.to_thread( + self.data_manager.save_query_resp, + query_resp_dict, **kwargs + )) def save_query_info(self,result, model, query, delta_time_log): - self.data_manager.save_query_resp(result, model=model, query=json.dumps(query, ensure_ascii=False), - delta_time=delta_time_log) + asyncio.create_task(asyncio.to_thread( + self.data_manager.save_query_resp, + result, model=model, query=json.dumps(query, ensure_ascii=False), delta_time=delta_time_log + )) async def handle_request(self, param_dict: dict): # param parsing @@ -103,7 +101,7 @@ async def handle_request(self, param_dict: dict): result = {"errorCode": 102, "errorDesc": "type exception, should one of ['query', 'insert', 'remove', 'register']", "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} - self.data_manager.save_query_resp(result, model=model, query='', delta_time=0) + self.save_query_resp(result, model=model, query='', delta_time=0) return result except Exception as e: return {"errorCode": 103, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', @@ -120,14 +118,14 @@ async def handle_request(self, param_dict: dict): elif request_type == 'insert': return await self.handle_insert(chat_info, model) elif request_type == 'remove': - return self.handle_remove(model, param_dict) + return await self.handle_remove(model, param_dict) elif request_type == 'register': - return self.handle_register(model) + return await self.handle_register(model) else: return {"errorCode": 400, "errorDesc": "bad request"} - def handle_register(self, model): - response = adapter.ChatCompletion.create_register( + async def handle_register(self, model): + response = await adapter.ChatCompletion.create_register( model=model, cache_obj=self ) @@ -137,10 +135,10 @@ def handle_register(self, model): result = {"errorCode": 502, "errorDesc": "", "response": response, "writeStatus": "exception"} return result - def handle_remove(self, model, param_dict): + async def handle_remove(self, model, param_dict): remove_type = param_dict.get("remove_type") id_list = param_dict.get("id_list", []) - response = adapter.ChatCompletion.create_remove( + response = await adapter.ChatCompletion.create_remove( model=model, remove_type=remove_type, id_list=id_list, @@ -191,12 +189,12 @@ async def handle_query(self, model, query): result = {"errorCode": 201, "errorDesc": response, "cacheHit": False, "delta_time": delta_time, "hit_query": '', "answer": ''} else: - answer = response_text(response) - hit_query = response_hitquery(response) + answer = response['data'] + hit_query = response['hitQuery'] result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, "hit_query": hit_query, "answer": answer} delta_time_log = round(time.time() - start_time, 2) - executor.submit(self.save_query_info, result, model, query, delta_time_log) + self.save_query_info(result, model, query, delta_time_log) except Exception as e: result = {"errorCode": 202, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} @@ -265,7 +263,9 @@ async def init( #==================================================# # switching based on embedding_model - if embedding_model == EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2: + if (embedding_model == EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2 + or embedding_model == EmbeddingModel.HUGGINGFACE_ALL_MINILM_L6_V2 + or embedding_model == EmbeddingModel.HUGGINGFACE_ALL_MINILM_L12_V2): query_pre_embedding_func = query_with_role insert_pre_embedding_func = query_with_role post_process_messages_func = first @@ -287,8 +287,8 @@ async def init( # add more configurations for other embedding models as needed else: - modelcache_log.error(f"Please add configuration for {embedding_model} in modelcache/__init__.py.") - raise CacheError(f"Please add configuration for {embedding_model} in modelcache/__init__.py.") + modelcache_log.error(f"Please add configuration for {embedding_model} in modelcache/cache.py.") + raise CacheError(f"Please add configuration for {embedding_model} in modelcache/cache.py.") # ====================== Data manager ==============================# @@ -300,7 +300,7 @@ async def init( config=vector_config, metric_type=similarity_metric_type, ), - eviction='ARC', + memory_cache_policy='ARC', max_size=10000, normalize=normalize, ) diff --git a/modelcache/embedding/base.py b/modelcache/embedding/base.py index aafdc97..70ae240 100644 --- a/modelcache/embedding/base.py +++ b/modelcache/embedding/base.py @@ -1,8 +1,12 @@ # -*- coding: utf-8 -*- from abc import abstractmethod, ABCMeta +from modelcache.utils.error import CacheError from modelcache.utils.lazy_import import LazyImport from enum import Enum + +from modelcache.utils.log import modelcache_log + huggingface = LazyImport("huggingface", globals(), "modelcache.embedding.huggingface") data2vec = LazyImport("data2vec", globals(), "modelcache.embedding.data2vec") llmEmb = LazyImport("llmEmb", globals(), "modelcache.embedding.llmEmb") @@ -21,7 +25,7 @@ class EmbeddingModel(Enum): HUGGINGFACE_ALL_MPNET_BASE_V2 = {"dimension":768, "model_path":"sentence-transformers/all-mpnet-base-v2"} HUGGINGFACE_ALL_MINILM_L6_V2 = {"dimension":384, "model_path":"sentence-transformers/all-MiniLM-L6-v2"} HUGGINGFACE_ALL_MINILM_L12_V2 = {"dimension":384, "model_path":"sentence-transformers/all-MiniLM-L12-v2"} - DATA2VEC_AUDIO = {"dimension":None, "model_path":"model/text2vec-base-chinese/"} + DATA2VEC_AUDIO = {"dimension":768, "model_path":"model/text2vec-base-chinese/"} LLM_EMB2VEC_AUDIO = {"dimension":None, "model_path":None} FASTTEXT = {"dimension":None, "model_path":None} PADDLE_NLP = {"dimension":None, "model_path":None} @@ -68,6 +72,14 @@ def get(model:EmbeddingModel, **kwargs): model_path = kwargs.pop("model_path","sentence-transformers/all-mpnet-base-v2") return huggingface.Huggingface(model_path) + elif model == EmbeddingModel.HUGGINGFACE_ALL_MINILM_L6_V2: + model_path = kwargs.pop("model_path","sentence-transformers/all-MiniLM-L6-v2") + return huggingface.Huggingface(model_path) + + elif model == EmbeddingModel.HUGGINGFACE_ALL_MINILM_L12_V2: + model_path = kwargs.pop("model_path","sentence-transformers/all-MiniLM-L12-v2") + return huggingface.Huggingface(model_path) + elif model == EmbeddingModel.DATA2VEC_AUDIO: model_path = kwargs.pop("model_path","model/text2vec-base-chinese/") return data2vec.Data2VecAudio(model_path) @@ -99,5 +111,7 @@ def get(model:EmbeddingModel, **kwargs): return bge_m3.BgeM3Embedding(model_path) else: - raise ValueError(f"Unsupported embedding model: {model}") + modelcache_log.error(f"Please add configuration for {model} in modelcache/embedding/base.py.") + raise CacheError(f"Please add configuration for {model} in modelcache/embedding/base.py.") + diff --git a/modelcache/embedding/embedding_dispatcher.py b/modelcache/embedding/embedding_dispatcher.py index 358cff1..56b5e27 100644 --- a/modelcache/embedding/embedding_dispatcher.py +++ b/modelcache/embedding/embedding_dispatcher.py @@ -2,6 +2,7 @@ import threading import uuid import asyncio +import psutil from asyncio import Future, AbstractEventLoop from modelcache.embedding import EmbeddingModel @@ -11,13 +12,18 @@ def worker_func(embedding_model: EmbeddingModel, model_path, task_queue, result_queue, worker_id): base_embedding = BaseEmbedding.get(embedding_model, model_path=model_path) print(f"Embedding worker {worker_id} started.") - while True: - job_id, data = task_queue.get() - try: - result = base_embedding.to_embeddings(data) - except Exception as e: - result = e - result_queue.put((job_id, result)) + try: + while True: + job_id, data = task_queue.get() + try: + result = base_embedding.to_embeddings(data) + except Exception as e: + result = e + result_queue.put((job_id, result)) + except KeyboardInterrupt: + print(f"Embedding worker {worker_id} stopped.") + except Exception as e: + print(f"Embedding worker {worker_id} encountered an error: {e}") class EmbeddingDispatcher: @@ -46,6 +52,7 @@ def __init__( ) p.daemon = True p.start() + psutil.Process(p.pid).nice(psutil.HIGH_PRIORITY_CLASS) self.workers.append(p) def _start_result_collector_thread(self): diff --git a/modelcache/manager/data_manager.py b/modelcache/manager/data_manager.py index c12f414..5a32c01 100644 --- a/modelcache/manager/data_manager.py +++ b/modelcache/manager/data_manager.py @@ -69,7 +69,7 @@ def get( object_base: Union[ObjectBase, str] = None, max_size: int = 3, clean_size: int = 1, - eviction: str = "ARC", + memory_cache_policy: str = "ARC", data_path: str = "data_map.txt", get_data_container: Callable = None, normalize: bool = True @@ -84,7 +84,7 @@ def get( if isinstance(object_base, str): object_base = ObjectBase.get(name=object_base) assert cache_base and vector_base - return SSDataManager(cache_base, vector_base, object_base, max_size, clean_size,normalize, eviction) + return SSDataManager(cache_base, vector_base, object_base, max_size, clean_size,normalize, memory_cache_policy) class MapDataManager(DataManager): @@ -229,22 +229,18 @@ def import_data( normalize(embedding_data) for embedding_data in embedding_datas ] - for i, embedding_data in enumerate(embedding_datas): + for embedding_data, answer, question in zip(embedding_datas,answers,questions): if self.o is not None: - ans = self._process_answer_data(answers[i]) - else: - ans = answers[i] + answer = self._process_answer_data(answer) - question = questions[i] embedding_data = embedding_data.astype("float32") - cache_datas.append([ans, question, embedding_data, model]) + cache_datas.append([answer, question, embedding_data, model]) ids = self.s.batch_insert(cache_datas) datas = [] - for i, embedding_data in enumerate(embedding_datas): - _id = ids[i] + for _id,embedding_data,cache_data in zip(ids,embedding_datas,cache_datas): datas.append(VectorData(id=_id, data=embedding_data.astype("float32"))) - self.eviction_base.put([(_id, cache_datas[i])],model=model) + self.eviction_base.put([(_id, cache_data)],model=model) self.v.mul_add(datas,model) def get_scalar_data(self, res_data, **kwargs) -> Optional[CacheData]: @@ -254,9 +250,10 @@ def get_scalar_data(self, res_data, **kwargs) -> Optional[CacheData]: cache_hit = self.eviction_base.get(_id, model=model) if cache_hit is not None: return cache_hit - cache_data = self.s.get_data_by_id(res_data[1]) + cache_data = self.s.get_data_by_id(_id) if cache_data is None: return None + self.eviction_base.put([(_id, cache_data)], model=model) return cache_data def update_hit_count(self, primary_id, **kwargs): diff --git a/modelcache/manager/eviction/wtinylfu_cache.py b/modelcache/manager/eviction/wtinylfu_cache.py index d5887f5..1f41447 100644 --- a/modelcache/manager/eviction/wtinylfu_cache.py +++ b/modelcache/manager/eviction/wtinylfu_cache.py @@ -1,4 +1,4 @@ -from cachetools import LRUCache, Cache +from cachetools import LRUCache, Cache, LFUCache from readerwriterlock import rwlock import random @@ -35,16 +35,21 @@ def decay(self): table[i] >>= 1 class W2TinyLFU(Cache): - def __init__(self, maxsize, window_pct=1): + def __init__(self, maxsize, window_pct=0.01): + """ + param maxsize: Maximum size of the cache. + + param window_pct: Percentage of the cache size to be used for the window. + """ super().__init__(maxsize) - self.window_size = max(1, int(maxsize * window_pct / 100)) + self.window_size = max(1, int(maxsize * window_pct)) rest = maxsize - self.window_size self.probation_size = rest // 2 self.protected_size = rest - self.probation_size self.window = LRUCache(maxsize=self.window_size) - self.probation = LRUCache(maxsize=self.probation_size) - self.protected = LRUCache(maxsize=self.protected_size) + self.probation = LFUCache(maxsize=self.probation_size) + self.protected = LFUCache(maxsize=self.protected_size) self.cms = CountMinSketch() self.data = {} diff --git a/modelcache/manager/scalar_data/sql_storage.py b/modelcache/manager/scalar_data/sql_storage.py index c24a024..5683f61 100644 --- a/modelcache/manager/scalar_data/sql_storage.py +++ b/modelcache/manager/scalar_data/sql_storage.py @@ -1,11 +1,9 @@ # -*- coding: utf-8 -*- -import os -import time import uuid import pymysql import json -import base64 +import numpy as np from typing import List from modelcache.manager.scalar_data.base import CacheStorage, CacheData from DBUtils.PooledDB import PooledDB @@ -147,7 +145,7 @@ def get_data_by_id(self, key: int): conn.close() if resp is not None and len(resp) == 4: - return resp + return resp[0], resp[1], np.frombuffer(resp[2], dtype=np.float32), resp[3] else: return None diff --git a/websocket4modelcache.py b/websocket4modelcache.py index 4543f03..6aaed64 100644 --- a/websocket4modelcache.py +++ b/websocket4modelcache.py @@ -15,7 +15,7 @@ async def lifespan(app: FastAPI): sql_storage="mysql", vector_storage="milvus", embedding_model=EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2, - embedding_workers_num=6 + embedding_workers_num=8 ) yield From a13ec496f49eefd2fcadba420f933c6f59621093 Mon Sep 17 00:00:00 2001 From: Yuval-Roth Date: Thu, 26 Jun 2025 18:10:16 +0300 Subject: [PATCH 36/36] Added documentation Co-authored-by: olgaoznovich Co-authored-by: Yuval-Roth Co-authored-by: omerdor001 Co-authored-by: adiaybgu --- modelcache/adapter/adapter_insert.py | 18 +++- modelcache/adapter/adapter_query.py | 32 ++++-- modelcache/cache.py | 98 ++++++++++++++++--- modelcache/embedding/embedding_dispatcher.py | 32 +++--- modelcache/manager/data_manager.py | 63 ++++++++++-- modelcache/manager/eviction/arc_cache.py | 47 +++++++-- modelcache/manager/eviction/memory_cache.py | 1 + modelcache/manager/eviction/wtinylfu_cache.py | 64 +++++++++--- modelcache/manager/scalar_data/sql_storage.py | 1 + modelcache/manager/vector_data/base.py | 1 + 10 files changed, 287 insertions(+), 70 deletions(-) diff --git a/modelcache/adapter/adapter_insert.py b/modelcache/adapter/adapter_insert.py index 757e0c8..a2f224a 100644 --- a/modelcache/adapter/adapter_insert.py +++ b/modelcache/adapter/adapter_insert.py @@ -9,23 +9,31 @@ async def adapt_insert(*args, **kwargs): chat_cache = kwargs.pop("cache_obj") model = kwargs.pop("model", None) require_object_store = kwargs.pop("require_object_store", False) + + # Validate object store availability if required if require_object_store: assert chat_cache.data_manager.o, "Object store is required for adapter." + context = kwargs.pop("cache_context", {}) chat_info = kwargs.pop("chat_info", []) - pre_embedding_data_list = [] - embedding_futures_list = [] - llm_data_list = [] + # Initialize collections for parallel processing + pre_embedding_data_list = [] # Preprocessed data ready for embedding + embedding_futures_list = [] # Async embedding generation tasks + llm_data_list = [] # Extracted LLM response data + # Process each chat entry and prepare for parallel embedding generation for row in chat_info: + # Preprocess chat data using configured preprocessing function pre_embedding_data = chat_cache.insert_pre_embedding_func( row, extra_param=context.get("pre_embedding_func", None), prompts=chat_cache.prompts, ) pre_embedding_data_list.append(pre_embedding_data) - llm_data_list.append(row['answer']) + llm_data_list.append(row['answer']) # Extract answer text for storage + + # Create async embedding generation task with performance monitoring embedding_future = time_cal( chat_cache.embedding_func, func_name="embedding", @@ -34,8 +42,10 @@ async def adapt_insert(*args, **kwargs): )(pre_embedding_data) embedding_futures_list.append(embedding_future) + # Wait for all embedding generation tasks to complete in parallel embedding_data_list = await asyncio.gather(*embedding_futures_list) + # Save all processed data to the data manager asynchronously await asyncio.to_thread( chat_cache.data_manager.save, pre_embedding_data_list, diff --git a/modelcache/adapter/adapter_query.py b/modelcache/adapter/adapter_query.py index 7ce1ba0..d98e503 100644 --- a/modelcache/adapter/adapter_query.py +++ b/modelcache/adapter/adapter_query.py @@ -8,16 +8,21 @@ USE_RERANKER = False # 如果为 True 则启用 reranker，否则使用原有逻辑 async def adapt_query(cache_data_convert, *args, **kwargs): + # Extract query parameters chat_cache = kwargs.pop("cache_obj") scope = kwargs.pop("scope") model = scope['model'] context = kwargs.pop("cache_context", {}) cache_factor = kwargs.pop("cache_factor", 1.0) + + # Preprocess query for embedding generation pre_embedding_data = chat_cache.query_pre_embedding_func( kwargs, extra_param=context.get("pre_embedding_func", None), prompts=chat_cache.prompts, ) + + # Generate embedding with performance monitoring embedding_data = await time_cal( chat_cache.embedding_func, func_name="embedding", @@ -39,24 +44,29 @@ async def adapt_query(cache_data_convert, *args, **kwargs): model=model ) + # Initialize result containers cache_answers = [] cache_questions = [] cache_ids = [] cosine_similarity = None + # Similarity evaluation based on metric type if chat_cache.similarity_metric_type == MetricType.COSINE: cosine_similarity = cache_data_list[0][0] # This code uses the built-in cosine similarity evaluation in milvus if cosine_similarity < chat_cache.similarity_threshold: - return None + return None # No suitable match found + elif chat_cache.similarity_metric_type == MetricType.L2: - ## this is the code that uses L2 for similarity evaluation + # this is the code that uses L2 for similarity evaluation similarity_threshold = chat_cache.similarity_threshold similarity_threshold_long = chat_cache.similarity_threshold_long min_rank, max_rank = chat_cache.similarity_evaluation.range() rank_threshold = (max_rank - min_rank) * similarity_threshold * cache_factor rank_threshold_long = (max_rank - min_rank) * similarity_threshold_long * cache_factor + + # Clamp thresholds to valid range rank_threshold = ( max_rank if rank_threshold > max_rank @@ -71,6 +81,8 @@ async def adapt_query(cache_data_convert, *args, **kwargs): if rank_threshold_long < min_rank else rank_threshold_long ) + + # Evaluate similarity score if cache_data_list is None or len(cache_data_list) == 0: rank_pre = -1.0 else: @@ -81,12 +93,13 @@ async def adapt_query(cache_data_convert, *args, **kwargs): extra_param=context.get("evaluation_func", None), ) if rank_pre < rank_threshold: - return None + return None # Similarity too low else: raise ValueError( f"Unsupported similarity metric type: {chat_cache.similarity_metric_type}" ) + # Process search results with optional reranking if USE_RERANKER: reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=False) for cache_data in cache_data_list: @@ -116,7 +129,6 @@ async def adapt_query(cache_data_convert, *args, **kwargs): "question": pre_embedding_data, "embedding": embedding_data, } - eval_cache_data = { "question": ret[0], "answer": ret[1], @@ -135,9 +147,10 @@ async def adapt_query(cache_data_convert, *args, **kwargs): cache_questions.append((rank, ret[1])) cache_ids.append((rank, primary_id)) else: - # 不使用 reranker 时，走原来的逻辑 + # Original logic without reranking for cache_data in cache_data_list: primary_id = cache_data[1] + # Retrieve full cache entry data ret = await asyncio.to_thread( chat_cache.data_manager.get_scalar_data, cache_data, extra_param=context.get("get_scalar_data", None), model=model @@ -150,6 +163,7 @@ async def adapt_query(cache_data_convert, *args, **kwargs): cache_answers.append((cosine_similarity, ret[0])) cache_questions.append((cosine_similarity, ret[1])) cache_ids.append((cosine_similarity, primary_id)) + elif chat_cache.similarity_metric_type == MetricType.L2: if "deps" in context and hasattr(ret.question, "deps"): eval_query_data = { @@ -167,13 +181,14 @@ async def adapt_query(cache_data_convert, *args, **kwargs): "question": pre_embedding_data, "embedding": embedding_data, } - eval_cache_data = { "question": ret[0], "answer": ret[1], "search_result": cache_data, "embedding": None } + + # Evaluate similarity for this specific result rank = chat_cache.similarity_evaluation.evaluation( eval_query_data, eval_cache_data, @@ -195,6 +210,7 @@ async def adapt_query(cache_data_convert, *args, **kwargs): f"Unsupported similarity metric type: {chat_cache.similarity_metric_type}" ) + # Sort results by similarity score (highest first) cache_answers = sorted(cache_answers, key=lambda x: x[0], reverse=True) cache_questions = sorted(cache_questions, key=lambda x: x[0], reverse=True) cache_ids = sorted(cache_ids, key=lambda x: x[0], reverse=True) @@ -208,12 +224,14 @@ async def adapt_query(cache_data_convert, *args, **kwargs): return_id = chat_cache.post_process_messages_func( [t[1] for t in cache_ids] ) - # 更新命中次数 + + # Update hit count for analytics (async to avoid blocking) try: asyncio.create_task(asyncio.to_thread(chat_cache.data_manager.update_hit_count,return_id)) except Exception: logging.info('update_hit_count except, please check!') + # Record cache hit for reporting chat_cache.report.hint_cache() return cache_data_convert(return_message, return_query) return None \ No newline at end of file diff --git a/modelcache/cache.py b/modelcache/cache.py index 9aaa224..9149a3c 100644 --- a/modelcache/cache.py +++ b/modelcache/cache.py @@ -73,30 +73,56 @@ def close(): modelcache_log.error(e) def save_query_resp(self, query_resp_dict, **kwargs): + """ + Save query response asynchronously to avoid blocking main thread. + Used for logging and analytics purposes. + """ + # Execute save operation in a separate thread to maintain async performance asyncio.create_task(asyncio.to_thread( self.data_manager.save_query_resp, query_resp_dict, **kwargs )) - def save_query_info(self,result, model, query, delta_time_log): + def save_query_info(self, result, model, query, delta_time_log): + """ + Save query information with execution timing for performance analysis. + Serializes query data to JSON for storage. + """ + # Convert query to JSON and save asynchronously asyncio.create_task(asyncio.to_thread( self.data_manager.save_query_resp, result, model=model, query=json.dumps(query, ensure_ascii=False), delta_time=delta_time_log )) async def handle_request(self, param_dict: dict): - # param parsing + """ + Main entry point for processing cache requests. + + Routes requests to appropriate handlers based on request type. + Supports: query, insert, remove, register operations. + + Args: + param_dict: Request parameters containing type, scope, query, etc. + + Returns: + dict: Response dictionary with errorCode, result data, and metadata + """ + # Parse and validate request parameters try: request_type = param_dict.get("type") + # Extract model information from scope scope = param_dict.get("scope") model = None if scope is not None: model = scope.get('model') + # Normalize model name for consistent storage (replace special chars) model = model.replace('-', '_') model = model.replace('.', '_') query = param_dict.get("query") chat_info = param_dict.get("chat_info") + + # Validate request type against supported operations if request_type is None or request_type not in ['query', 'insert', 'remove', 'register']: result = {"errorCode": 102, "errorDesc": "type exception, should one of ['query', 'insert', 'remove', 'register']", @@ -104,15 +130,16 @@ async def handle_request(self, param_dict: dict): self.save_query_resp(result, model=model, query='', delta_time=0) return result except Exception as e: + # Return error response for parameter parsing failures return {"errorCode": 103, "errorDesc": str(e), "cacheHit": False, "delta_time": 0, "hit_query": '', "answer": ''} - # model filter + # Apply model-based filtering (blacklist check) filter_resp = model_blacklist_filter(model, request_type) if isinstance(filter_resp, dict): return filter_resp - # handle request + # Route to appropriate handler based on request type if request_type == 'query': return await self.handle_query(model, query) elif request_type == 'insert': @@ -129,6 +156,7 @@ async def handle_register(self, model): model=model, cache_obj=self ) + # Process registration response and return standardized result if response in ['create_success', 'already_exists']: result = {"errorCode": 0, "errorDesc": "", "response": response, "writeStatus": "success"} else: @@ -138,12 +166,16 @@ async def handle_register(self, model): async def handle_remove(self, model, param_dict): remove_type = param_dict.get("remove_type") id_list = param_dict.get("id_list", []) + + # Execute removal operation through adapter response = await adapter.ChatCompletion.create_remove( model=model, remove_type=remove_type, id_list=id_list, cache_obj=self ) + + # Process removal response and standardize result format if not isinstance(response, dict): return {"errorCode": 401, "errorDesc": "", "response": response, "removeStatus": "exception"} state = response.get('status') @@ -156,6 +188,7 @@ async def handle_remove(self, model, param_dict): async def handle_insert(self, chat_info, model): try: try: + # Execute insertion through adapter with error handling response = await adapter.ChatCompletion.create_insert( model=model, chat_info=chat_info, @@ -164,6 +197,7 @@ async def handle_insert(self, chat_info, model): except Exception as e: return {"errorCode": 302, "errorDesc": str(e), "writeStatus": "exception"} + # Process insertion response if response == 'success': result = {"errorCode": 0, "errorDesc": "", "writeStatus": "success"} else: @@ -174,25 +208,35 @@ async def handle_insert(self, chat_info, model): async def handle_query(self, model, query): try: - start_time = time.time() + start_time = time.time() # Start performance timer + + # Execute query through adapter system response = await adapter.ChatCompletion.create_query( scope={"model": model}, query=query, cache_obj=self ) + + # Calculate query execution time delta_time = '{}s'.format(round(time.time() - start_time, 2)) + + # Process different response types if response is None: + # No cache hit found result = {"errorCode": 0, "errorDesc": '', "cacheHit": False, "delta_time": delta_time, "hit_query": '', "answer": ''} - # elif response in ['adapt_query_exception']: elif isinstance(response, str): + # Error occurred during query processing result = {"errorCode": 201, "errorDesc": response, "cacheHit": False, "delta_time": delta_time, "hit_query": '', "answer": ''} else: + # Cache hit found - extract response data answer = response['data'] hit_query = response['hitQuery'] result = {"errorCode": 0, "errorDesc": '', "cacheHit": True, "delta_time": delta_time, "hit_query": hit_query, "answer": answer} + + # Log query performance data asynchronously delta_time_log = round(time.time() - start_time, 2) self.save_query_info(result, model, query, delta_time_log) except Exception as e: @@ -202,6 +246,7 @@ async def handle_query(self, model, query): return result def flush(self): + """Flush all cached data to persistent storage backends.""" self.data_manager.flush() @staticmethod @@ -211,11 +256,35 @@ async def init( embedding_model: EmbeddingModel, embedding_workers_num: int ) -> tuple['Cache' , AbstractEventLoop]: - #================= configurations for databases ===================# + """ + Initialize a complete Cache system with all required components. + + Args: + sql_storage: SQL backend type ("mysql", "sqlite", "elasticsearch") + vector_storage: Vector backend type ("milvus", "faiss", "chromadb", "redis") + embedding_model: Embedding model enum value + embedding_workers_num: Number of parallel embedding worker processes + + Returns: + tuple: (Cache instance, event loop) ready for async operations + + Raises: + CacheError: If unsupported storage backends or model configuration issues + + Example: + cache, loop = await Cache.init( + sql_storage="mysql", + vector_storage="milvus", + embedding_model=EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2, + embedding_workers_num=4 + ) + """ + #================= Database configuration loading ===================# sql_config = configparser.ConfigParser() vector_config = configparser.ConfigParser() + # Load SQL storage configuration from INI files if sql_storage == "mysql": sql_config.read('modelcache/config/mysql_config.ini') elif sql_storage == "elasticsearch": @@ -226,6 +295,7 @@ async def init( modelcache_log.error(f"Unsupported cache storage: {sql_storage}.") raise CacheError(f"Unsupported cache storage: {sql_storage}.") + # Load vector storage configuration from INI files if vector_storage == "milvus" : vector_config.read('modelcache/config/milvus_config.ini') elif vector_storage == "chromadb" : @@ -233,22 +303,24 @@ async def init( elif vector_storage == "redis" : vector_config.read('modelcache/config/redis_config.ini') elif vector_storage == "faiss" : - vector_config = None # faiss does not require additional configuration + vector_config = None # FAISS does not require external configuration else: modelcache_log.error(f"Unsupported vector storage: {vector_storage}.") raise CacheError(f"Unsupported vector storage: {vector_storage}.") - #=============== model-specific configuration =====================# + #=============== embedding-model-specific configuration =====================# - event_loop = asyncio.get_running_loop() + event_loop = asyncio.get_running_loop() # Get current async event loop model_path = embedding_model.value['model_path'] dimension = embedding_model.value['dimension'] + # Validate that embedding model has required configuration if model_path is None or dimension is None: modelcache_log.error(f"Please set the model_path and dimension for {embedding_model} in modelcache/embedding/base.py.") raise CacheError(f"Please set the model_path and dimension for {embedding_model} in modelcache/embedding/base.py.") + # Initialize parallel embedding generation system embedding_dispatcher = EmbeddingDispatcher(embedding_model, model_path, event_loop, embedding_workers_num) #=== These will be used to initialize the cache ===# @@ -262,7 +334,7 @@ async def init( normalize: bool = None #==================================================# - # switching based on embedding_model + # Configure cache behavior based on embedding model type if (embedding_model == EmbeddingModel.HUGGINGFACE_ALL_MPNET_BASE_V2 or embedding_model == EmbeddingModel.HUGGINGFACE_ALL_MINILM_L6_V2 or embedding_model == EmbeddingModel.HUGGINGFACE_ALL_MINILM_L12_V2): @@ -285,13 +357,14 @@ async def init( similarity_threshold_long = 0.95 normalize = True - # add more configurations for other embedding models as needed + # Add configurations for additional embedding models as needed else: modelcache_log.error(f"Please add configuration for {embedding_model} in modelcache/cache.py.") raise CacheError(f"Please add configuration for {embedding_model} in modelcache/cache.py.") # ====================== Data manager ==============================# + # Create coordinated data manager with all storage backends data_manager = DataManager.get( SQLStorage.get(sql_storage, config=sql_config), VectorStorage.get( @@ -307,6 +380,7 @@ async def init( #================== Cache Initialization ====================# + # Create fully configured Cache instance cache = Cache( embedding_model = embedding_model, similarity_metric_type = similarity_metric_type, diff --git a/modelcache/embedding/embedding_dispatcher.py b/modelcache/embedding/embedding_dispatcher.py index 56b5e27..16f4beb 100644 --- a/modelcache/embedding/embedding_dispatcher.py +++ b/modelcache/embedding/embedding_dispatcher.py @@ -10,16 +10,17 @@ def worker_func(embedding_model: EmbeddingModel, model_path, task_queue, result_queue, worker_id): + """Worker function that runs in separate processes to generate embeddings.""" base_embedding = BaseEmbedding.get(embedding_model, model_path=model_path) print(f"Embedding worker {worker_id} started.") try: while True: - job_id, data = task_queue.get() + job_id, data = task_queue.get() # Get task from queue try: - result = base_embedding.to_embeddings(data) + result = base_embedding.to_embeddings(data) # Generate embedding except Exception as e: result = e - result_queue.put((job_id, result)) + result_queue.put((job_id, result)) # Send result back except KeyboardInterrupt: print(f"Embedding worker {worker_id} stopped.") except Exception as e: @@ -27,6 +28,8 @@ def worker_func(embedding_model: EmbeddingModel, model_path, task_queue, result_ class EmbeddingDispatcher: + """Manages a pool of worker processes for parallel embedding generation.""" + def __init__( self, embedding_model: EmbeddingModel, @@ -34,14 +37,15 @@ def __init__( event_loop: AbstractEventLoop, num_workers: int ): + """Initialize the dispatcher with worker processes.""" if num_workers <= 0: raise ValueError("Number of workers must be greater than 0.") - self.task_queue = multiprocessing.Queue() - self.result_queue = multiprocessing.Queue() - self.futures: dict[str, asyncio.Future] = {} + self.task_queue = multiprocessing.Queue() # Tasks to workers + self.result_queue = multiprocessing.Queue() # Results from workers + self.futures: dict[str, asyncio.Future] = {} # Pending futures self.event_loop = event_loop - self._start_result_collector_thread() + self._start_result_collector_thread() # Start result collection thread # Start worker processes self.workers = [] @@ -56,10 +60,11 @@ def __init__( self.workers.append(p) def _start_result_collector_thread(self): + """Start a thread to collect results from worker processes.""" def collect(): while True: - job_id, result = self.result_queue.get() - future = self.futures.pop(job_id, None) + job_id, result = self.result_queue.get() # Get result from queue + future = self.futures.pop(job_id, None) # Retrieve future if future: self.event_loop.call_soon_threadsafe( future.set_exception if isinstance(result, Exception) else future.set_result, @@ -70,9 +75,10 @@ def collect(): t.start() def embed(self, data: str) -> Future: - job_id = str(uuid.uuid4()) - future = asyncio.get_running_loop().create_future() - self.futures[job_id] = future - self.task_queue.put((job_id, data)) + """Submit a task for embedding generation.""" + job_id = str(uuid.uuid4()) # Generate unique job ID + future = asyncio.get_running_loop().create_future() # Create future + self.futures[job_id] = future # Store future + self.task_queue.put((job_id, data)) # Add task to queue return future diff --git a/modelcache/manager/data_manager.py b/modelcache/manager/data_manager.py index 5a32c01..7f019cd 100644 --- a/modelcache/manager/data_manager.py +++ b/modelcache/manager/data_manager.py @@ -173,23 +173,24 @@ def __init__( ): self.max_size = max_size self.clean_size = clean_size - self.s = s - self.v = v - self.o = o + self.s = s # SQL storage + self.v = v # Vector storage + self.o = o # Object storage (optional) self.normalize = normalize - # added + # Initialize memory cache with specified eviction policy self.eviction_base = MemoryCacheEviction( policy=policy, maxsize=max_size, clean_size=clean_size) def save(self, questions: List[any], answers: List[any], embedding_datas: List[any], **kwargs): + """Save multiple questions, answers, and embeddings to storage.""" model = kwargs.pop("model", None) self.import_data(questions, answers, embedding_datas, model) - def save_query_resp(self, query_resp_dict, **kwargs): + """Save query response log to SQL storage for analytics.""" save_query_start_time = time.time() self.s.insert_query_resp(query_resp_dict, **kwargs) save_query_delta_time = '{}s'.format(round(time.time() - save_query_start_time, 2)) @@ -220,10 +221,17 @@ def _process_question_data(self, question: Union[str, Question]): def import_data( self, questions: List[Any], answers: List[Answer], embedding_datas: List[Any], model: Any ): + """ + Add multiple cache entries into all storage backends. + + Coordinates data insertion across SQL, vector, and object storage, + with memory cache population and optional vector normalization. + """ if len(questions) != len(answers) or len(questions) != len(embedding_datas): raise ParamError("Make sure that all parameters have the same length") cache_datas = [] + # Normalize embedding vectors if configured if self.normalize: embedding_datas = [ normalize(embedding_data) for embedding_data in embedding_datas @@ -236,7 +244,10 @@ def import_data( embedding_data = embedding_data.astype("float32") cache_datas.append([answer, question, embedding_data, model]) + # Insert into SQL storage and get generated IDs ids = self.s.batch_insert(cache_datas) + + # Prepare vector data and populate memory cache datas = [] for _id,embedding_data,cache_data in zip(ids,embedding_datas,cache_datas): datas.append(VectorData(id=_id, data=embedding_data.astype("float32"))) @@ -244,9 +255,15 @@ def import_data( self.v.mul_add(datas,model) def get_scalar_data(self, res_data, **kwargs) -> Optional[CacheData]: + """ + Retrieve scalar data with multi-level caching strategy. + + First checks memory cache, then falls back to SQL storage. + """ model = kwargs.pop("model") - #Get Data from RAM Cache _id = res_data[1] + + # Try to get from memory cache first (fastest) cache_hit = self.eviction_base.get(_id, model=model) if cache_hit is not None: return cache_hit @@ -257,12 +274,19 @@ def get_scalar_data(self, res_data, **kwargs) -> Optional[CacheData]: return cache_data def update_hit_count(self, primary_id, **kwargs): + """Update hit count statistics in SQL storage.""" self.s.update_hit_count_by_id(primary_id) def hit_cache_callback(self, res_data, **kwargs): + """Callback executed on cache hit to update memory cache.""" self.eviction_base.get(res_data[1]) def search(self, embedding_data, **kwargs): + """ + Search for similar vectors in vector storage. + + Applies normalization if configured and delegates to vector backend. + """ model = kwargs.pop("model", None) if self.normalize: embedding_data = normalize(embedding_data) @@ -270,15 +294,24 @@ def search(self, embedding_data, **kwargs): return self.v.search(data=embedding_data, top_k=top_k, model=model) def delete(self, id_list, **kwargs): + """ + Delete cache entries from all storage backends. + + Removes from memory cache, vector storage, and marks as deleted in SQL. + Returns detailed status of deletion operations. + """ model = kwargs.pop("model") try: + # Remove from memory cache for id in id_list: - self.eviction_base.get_cache(model).pop(id, None) # Remove from in-memory LRU too + self.eviction_base.get_cache(model).pop(id, None) + # Delete from vector storage v_delete_count = self.v.delete(ids=id_list, model=model) except Exception as e: return {'status': 'failed', 'milvus': 'delete milvus data failed, please check! e: {}'.format(e), 'mysql': 'unexecuted'} try: + # Mark as deleted in SQL storage s_delete_count = self.s.mark_deleted(id_list) except Exception as e: return {'status': 'failed', 'milvus': 'success', @@ -288,13 +321,20 @@ def delete(self, id_list, **kwargs): 'mysql': 'delete_count: '+str(s_delete_count)} def create_index(self, model, **kwargs): + """Create vector index for a specific model.""" return self.v.create(model) def truncate(self, model): - # drop memory cache data + """ + Truncate all data for a specific model across all storage backends. + + Clears memory cache, rebuilds vector storage, and deletes SQL data. + Returns detailed status of truncation operations. + """ + # Clear memory cache data self.eviction_base.clear(model) - # drop vector base data + # Rebuild vector storage (drops and recreates collection) try: vector_resp = self.v.rebuild_col(model) except Exception as e: @@ -302,7 +342,8 @@ def truncate(self, model): 'ScalarDB': 'unexecuted'} if vector_resp: return {'status': 'failed', 'VectorDB': vector_resp, 'ScalarDB': 'unexecuted'} - # drop scalar base data + + # Delete scalar data from SQL storage try: delete_count = self.s.model_deleted(model) except Exception as e: @@ -311,10 +352,12 @@ def truncate(self, model): return {'status': 'success', 'VectorDB': 'rebuild', 'ScalarDB': 'delete_count: ' + str(delete_count)} def flush(self): + """Flush all storage backends to ensure data persistence.""" self.s.flush() self.v.flush() def close(self): + """Close all storage connections and release resources.""" self.s.close() self.v.close() diff --git a/modelcache/manager/eviction/arc_cache.py b/modelcache/manager/eviction/arc_cache.py index ae3a260..4f97231 100644 --- a/modelcache/manager/eviction/arc_cache.py +++ b/modelcache/manager/eviction/arc_cache.py @@ -5,22 +5,34 @@ _sentinel = object() class ARC(Cache): + """ + Adaptive Replacement Cache (ARC) implementation. + + ARC maintains four lists (T1, T2, B1, B2) to adaptively balance + between LRU and LFU eviction strategies based on access patterns. + """ + def __init__(self, maxsize, getsizeof=None): + """Initialize ARC cache with maximum size.""" super().__init__(maxsize, getsizeof) - self.t1 = OrderedDict() - self.t2 = OrderedDict() - self.b1 = OrderedDict() - self.b2 = OrderedDict() - self.p = 0 - self._rw_lock = rwlock.RWLockWrite() + self.t1 = OrderedDict() # Recent items + self.t2 = OrderedDict() # Frequent items + self.b1 = OrderedDict() # Ghost entries for T1 + self.b2 = OrderedDict() # Ghost entries for T2 + self.p = 0 # Adaptive parameter + self._rw_lock = rwlock.RWLockWrite() # Thread safety def __len__(self): + """Return total number of cached items.""" return len(self.t1) + len(self.t2) def __contains__(self, key): + """Check if key exists in cache.""" return key in self.t1 or key in self.t2 def _evict_internal(self): + """Internal method to evict items when cache is full.""" + # Evict from cache lists to ghost lists while len(self.t1) + len(self.t2) > self.maxsize: if len(self.t1) > self.p or (len(self.t1) == 0 and len(self.t2) > 0): key, value = self.t1.popitem(last=False) @@ -28,46 +40,56 @@ def _evict_internal(self): else: key, value = self.t2.popitem(last=False) self.b2[key] = value + + # Maintain ghost list sizes while len(self.b1) > (self.maxsize - self.p): self.b1.popitem(last=False) while len(self.b2) > self.p: self.b2.popitem(last=False) def __setitem__(self, key, value): + """Insert or update a cache entry.""" with self._rw_lock.gen_wlock(): + # Remove key from all lists first for l in (self.t1, self.t2, self.b1, self.b2): l.pop(key, None) + # Add to recent list (T1) self.t1[key] = value self.t1.move_to_end(key) self._evict_internal() def __getitem__(self, key): + """Retrieve a cache entry and update access pattern.""" with self._rw_lock.gen_wlock(): if key in self.t1: + # Move from recent to frequent list value = self.t1.pop(key) self.t2[key] = value self.t2.move_to_end(key) - self.p = max(0, self.p - 1) + self.p = max(0, self.p - 1) # Adjust adaptive parameter self._evict_internal() return value if key in self.t2: + # Access frequent list value = self.t2.pop(key) self.t2[key] = value self.t2.move_to_end(key) - self.p = min(self.maxsize, self.p + 1) + self.p = min(self.maxsize, self.p + 1) # Adjust adaptive parameter self._evict_internal() return value if key in self.b1: + # Promote from ghost list B1 to frequent list T2 self.b1.pop(key) - self.p = min(self.maxsize, self.p + 1) + self.p = min(self.maxsize, self.p + 1) # Adjust adaptive parameter self._evict_internal() value = super().__missing__(key) self.t2[key] = value self.t2.move_to_end(key) return value if key in self.b2: + # Promote from ghost list B2 to frequent list T2 self.b2.pop(key) - self.p = max(0, self.p - 1) + self.p = max(0, self.p - 1) # Adjust adaptive parameter self._evict_internal() value = super().__missing__(key) self.t2[key] = value @@ -76,9 +98,11 @@ def __getitem__(self, key): return super().__getitem__(key) def __missing__(self, key): + """Handle missing keys.""" raise KeyError(key) def pop(self, key, default=_sentinel): + """Remove a cache entry.""" with self._rw_lock.gen_wlock(): for l in (self.t1, self.t2, self.b1, self.b2): if key in l: @@ -88,6 +112,7 @@ def pop(self, key, default=_sentinel): return default def clear(self): + """Clear all cache entries.""" with self._rw_lock.gen_wlock(): self.t1.clear() self.t2.clear() @@ -97,10 +122,12 @@ def clear(self): super().clear() def __iter__(self): + """Iterate over cache keys.""" yield from self.t1 yield from self.t2 def __repr__(self): + """Return string representation of the cache.""" return (f"ARC(maxsize={self.maxsize}, p={self.p}, len={len(self)}, " f"t1_len={len(self.t1)}, t2_len={len(self.t2)}, " f"b1_len={len(self.b1)}, b2_len={len(self.b2)})") diff --git a/modelcache/manager/eviction/memory_cache.py b/modelcache/manager/eviction/memory_cache.py index 347abf9..838e8cd 100644 --- a/modelcache/manager/eviction/memory_cache.py +++ b/modelcache/manager/eviction/memory_cache.py @@ -67,3 +67,4 @@ def get_cache(self, model: str): @property def policy(self) -> str: return self._policy + diff --git a/modelcache/manager/eviction/wtinylfu_cache.py b/modelcache/manager/eviction/wtinylfu_cache.py index 1f41447..378b1ed 100644 --- a/modelcache/manager/eviction/wtinylfu_cache.py +++ b/modelcache/manager/eviction/wtinylfu_cache.py @@ -4,42 +4,60 @@ class CountMinSketch: def __init__(self, width=1024, depth=4, decay_interval=10000): + """Initialize Count-Min Sketch with specified dimensions.""" self.width = width self.depth = depth - self.tables = [[0]*width for _ in range(depth)] - self.seeds = [random.randrange(1<<30) for _ in range(depth)] - self.ops = 0 + self.tables = [[0]*width for _ in range(depth)] # Hash tables + self.seeds = [random.randrange(1<<30) for _ in range(depth)] # Hash seeds + self.ops = 0 # Operation counter for decay trigger self.decay_interval = decay_interval def _hash(self, x, seed): + """Hash function for mapping items to table positions.""" return hash((x, seed)) % self.width def add(self, x): + """Add an item and increment its frequency estimate.""" self.ops += 1 - est = self.estimate(x) + est = self.estimate(x) # Get current estimate + # Update all hash tables for i, seed in enumerate(self.seeds): idx = self._hash(x, seed) if self.tables[i][idx] <= est: self.tables[i][idx] += 1 + + # Periodic decay to handle changing patterns if self.ops >= self.decay_interval: self.decay() self.ops = 0 def estimate(self, x): + """Estimate frequency of an item (minimum across all tables).""" return min(self.tables[i][self._hash(x, seed)] for i, seed in enumerate(self.seeds)) def decay(self): + """Decay all frequency counts by half.""" for table in self.tables: for i in range(len(table)): - table[i] >>= 1 + table[i] >>= 1 # Right shift (divide by 2) class W2TinyLFU(Cache): + """ + Window Tiny LFU cache implementation. + + Combines a small LRU window cache with a main cache divided into + probation and protected segments, using frequency estimation for + admission control. + """ + def __init__(self, maxsize, window_pct=0.01): """ - param maxsize: Maximum size of the cache. + Initialize W-TinyLFU cache. - param window_pct: Percentage of the cache size to be used for the window. + Args: + maxsize: Maximum size of the cache + window_pct: Percentage of cache size for the window (default 1%) """ super().__init__(maxsize) self.window_size = max(1, int(maxsize * window_pct)) @@ -47,29 +65,34 @@ def __init__(self, maxsize, window_pct=0.01): self.probation_size = rest // 2 self.protected_size = rest - self.probation_size - self.window = LRUCache(maxsize=self.window_size) - self.probation = LFUCache(maxsize=self.probation_size) - self.protected = LFUCache(maxsize=self.protected_size) + # Three cache segments + self.window = LRUCache(maxsize=self.window_size) # Recent items + self.probation = LFUCache(maxsize=self.probation_size) # New main cache items + self.protected = LFUCache(maxsize=self.protected_size) # Frequently accessed items - self.cms = CountMinSketch() - self.data = {} - self._rw_lock = rwlock.RWLockWrite() + self.cms = CountMinSketch() # Frequency estimator + self.data = {} # Cache data storage + self._rw_lock = rwlock.RWLockWrite() # Read-write lock for thread safety def __setitem__(self, key, value): + """Add or update an item in the cache.""" with self._rw_lock.gen_wlock(): self.data[key] = value self._put(key) def __getitem__(self, key): + """Retrieve an item from the cache.""" val = self.get(key, default=None) if val is None: raise KeyError(key) return val def __contains__(self, key): + """Check if an item exists in the cache.""" return key in self.window or key in self.probation or key in self.protected def __delitem__(self, key): + """Remove an item from the cache.""" with self._rw_lock.gen_wlock(): self.data.pop(key, None) self.window.pop(key, None) @@ -77,6 +100,10 @@ def __delitem__(self, key): self.protected.pop(key, None) def get(self, key, default=None): + """ + Retrieve an item from the cache, updating its position + in the cache hierarchy if necessary. + """ if key in self.window: self.window[key] = True return self.data.get(key, default) @@ -94,6 +121,10 @@ def get(self, key, default=None): return default def _put(self, key): + """ + Add an item to the cache, using frequency-based admission + control and eviction policies. + """ self.cms.add(key) if key in self: return @@ -113,6 +144,9 @@ def _put(self, key): self.data.pop(key, None) def _admit_to_main(self, key): + """ + Admit an item to the main cache (probation or protected segment). + """ if key in self.protected or key in self.probation: return if self.probation_size == 0: @@ -129,8 +163,10 @@ def _admit_to_main(self, key): self.data.pop(key, None) def clear(self): + """Clear all items from the cache.""" with self._rw_lock.gen_wlock(): self.window.clear() self.probation.clear() self.protected.clear() - self.data.clear() \ No newline at end of file + self.data.clear() + diff --git a/modelcache/manager/scalar_data/sql_storage.py b/modelcache/manager/scalar_data/sql_storage.py index 5683f61..20eba9b 100644 --- a/modelcache/manager/scalar_data/sql_storage.py +++ b/modelcache/manager/scalar_data/sql_storage.py @@ -145,6 +145,7 @@ def get_data_by_id(self, key: int): conn.close() if resp is not None and len(resp) == 4: + # parse the numpy array from bytes and return the data return resp[0], resp[1], np.frombuffer(resp[2], dtype=np.float32), resp[3] else: return None diff --git a/modelcache/manager/vector_data/base.py b/modelcache/manager/vector_data/base.py index 6742c94..689f833 100644 --- a/modelcache/manager/vector_data/base.py +++ b/modelcache/manager/vector_data/base.py @@ -150,3 +150,4 @@ def check_dimension(dimension): if dimension <= 0: raise ParamError(f"the dimension should be greater than zero, current value: {dimension}.") +