chores: refactor examples

LaansDole · LaansDole · commit ab552321aee9 · 2025-10-04T21:27:41.000+07:00
diff --git a/examples/insert_content_list_example.py b/examples/insert_content_list_example.py
@@ -245,13 +245,16 @@ def vision_model_func(
             else:
                 return llm_model_func(prompt, system_prompt, history_messages, **kwargs)
 
-        # Define embedding function
+        # Define embedding function - using environment variables for configuration
+        embedding_dim = int(os.getenv("EMBEDDING_DIM", "3072"))
+        embedding_model = os.getenv("EMBEDDING_MODEL", "text-embedding-3-large")
+
         embedding_func = EmbeddingFunc(
-            embedding_dim=3072,
+            embedding_dim=embedding_dim,
             max_token_size=8192,
             func=lambda texts: openai_embed(
                 texts,
-                model="text-embedding-3-large",
+                model=embedding_model,
                 api_key=api_key,
                 base_url=base_url,
             ),
diff --git a/examples/modalprocessors_example.py b/examples/modalprocessors_example.py
@@ -164,14 +164,20 @@ async def process_equation_example(lightrag: LightRAG, llm_model_func):
 
 
 async def initialize_rag(api_key: str, base_url: str = None):
+    # Use environment variables for embedding configuration
+    import os
+
+    embedding_dim = int(os.getenv("EMBEDDING_DIM", "3072"))
+    embedding_model = os.getenv("EMBEDDING_MODEL", "text-embedding-3-large")
+
     rag = LightRAG(
         working_dir=WORKING_DIR,
         embedding_func=EmbeddingFunc(
-            embedding_dim=3072,
+            embedding_dim=embedding_dim,
             max_token_size=8192,
             func=lambda texts: openai_embed(
                 texts,
-                model="text-embedding-3-large",
+                model=embedding_model,
                 api_key=api_key,
                 base_url=base_url,
             ),
diff --git a/examples/raganything_example.py b/examples/raganything_example.py
@@ -182,13 +182,16 @@ def vision_model_func(
             else:
                 return llm_model_func(prompt, system_prompt, history_messages, **kwargs)
 
-        # Define embedding function
+        # Define embedding function - using environment variables for configuration
+        embedding_dim = int(os.getenv("EMBEDDING_DIM", "3072"))
+        embedding_model = os.getenv("EMBEDDING_MODEL", "text-embedding-3-large")
+
         embedding_func = EmbeddingFunc(
-            embedding_dim=3072,
+            embedding_dim=embedding_dim,
             max_token_size=8192,
             func=lambda texts: openai_embed(
                 texts,
-                model="text-embedding-3-large",
+                model=embedding_model,
                 api_key=api_key,
                 base_url=base_url,
             ),
