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[E-2] 11-Reranker / 01-CrossEncoderReranker #183

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[E-2] 11-Reranker / 01-CrossEncoderReranker
ThePurpleCollar Jan 6, 2025
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[E-2] 11-Reranker / 01-CrossEncoderReranker
ThePurpleCollar Jan 9, 2025
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[E-2] 11-Reranker / 01-CrossEncoderReranker
ThePurpleCollar Jan 9, 2025
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "T3l-XPuhit0_"
},
"source": [
"# Cross Encoder Reranker\n",
"\n",
"- Author: [Jeongho Shin](https://github.com/ThePurpleCollar)\n",
"- Design:\n",
"- Peer Review:\n",
"- This is a part of [LangChain Open Tutorial](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial)\n",
"\n",
"[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/01-Basic/05-Using-OpenAIAPI-MultiModal.ipynb) [![Open in GitHub](https://img.shields.io/badge/Open%20in%20GitHub-181717?style=flat-square&logo=github&logoColor=white)](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/01-Basic/05-Using-OpenAIAPI-MultiModal.ipynb)\n",
"\n",
"## Overview\n",
"\n",
"The **Cross Encoder Reranker** is a technique designed to enhance the performance of Retrieval-Augmented Generation (RAG) systems. This guide explains how to implement a reranker using Hugging Face's cross-encoder model to refine the ranking of retrieved documents, promoting those most relevant to a query.\n",
"\n",
"### Table of Contents\n",
"\n",
"- [Overview](#overview)\n",
"- [Key Features and Mechanism](#key-features-and-mechanism)\n",
"- [Practical Applications](#practical-applications)\n",
"- [Implementation](#implementation)\n",
"- [Key Advantages of Reranker](#key-advantages-of-reranker)\n",
"- [Document Count Settings for Reranker](#document-count-settings-for-reranker)\n",
"- [Trade-offs When Using a Reranker](#trade-offs-when-using-a-reranker)\n",
"\n",
"### References\n",
"\n",
"[Hugging Face cross encoder models ](https://huggingface.co/cross-encoder)\n",
"\n",
"----"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ydu-Mu2tizbd"
},
"source": [
"## Key Features and Mechanism\n",
"\n",
"### Purpose\n",
"- Re-rank retrieved documents to refine their ranking, prioritizing the most relevant results for the query.\n",
"\n",
"### Structure\n",
"- Accepts both the `query` and `document` as a single input pair, enabling joint processing.\n",
"\n",
"### Mechanism\n",
"- **Single Input Pair**: \n",
" Processes the `query` and `document` as a combined input to output a relevance score directly.\n",
"- **Self-Attention Mechanism**: \n",
" Uses self-attention to jointly analyze the `query` and `document`, effectively capturing their semantic relationship.\n",
"\n",
"### Advantages\n",
"- **Higher Accuracy**: \n",
" Provides more precise similarity scores.\n",
"- **Deep Contextual Analysis**: \n",
" Explores semantic nuances between `query` and `document`.\n",
"\n",
"### Limitations\n",
"- **High Computational Costs**: \n",
" Processing can be time-intensive.\n",
"- **Scalability Issues**: \n",
" Not suitable for large-scale document collections without optimization.\n",
"\n",
"---\n",
"\n",
"## Practical Applications\n",
"- A **Bi-Encoder** quickly retrieves candidate `documents` by computing lightweight similarity scores. \n",
"- A **Cross Encoder** refines these results by deeply analyzing the semantic relationship between the `query` and the retrieved `documents`.\n",
"\n",
"---\n",
"\n",
"## Implementation\n",
"- Use Hugging Face cross encoder models, such as `BAAI/bge-reranker`.\n",
"- Easily integrate with frameworks like `LangChain` through the `CrossEncoderReranker` component.\n",
"\n",
"---\n",
"\n",
"## Key Advantages of Reranker\n",
"- **Precise Similarity Scoring**: \n",
" Delivers highly accurate measurements of relevance between the `query` and `documents`.\n",
"- **Semantic Depth**: \n",
" Analyzes deeper semantic relationships, uncovering nuances in `query-document` interactions.\n",
"- **Refined Search Quality**: \n",
" Improves the relevance and quality of the retrieved `documents`.\n",
"- **RAG System Boost**: \n",
" Enhances the performance of `Retrieval-Augmented Generation (RAG)` systems by refining input relevance.\n",
"- **Seamless Integration**: \n",
" Easily adaptable to various workflows and compatible with multiple frameworks.\n",
"- **Model Versatility**: \n",
" Offers flexibility with a wide range of pre-trained models for tailored use cases.\n",
"\n",
"---\n",
"\n",
"## Document Count Settings for Reranker\n",
"- Reranking is generally performed on the top `5–10` `documents` retrieved during the initial search.\n",
"- The ideal number of `documents` for reranking should be determined through experimentation and evaluation, as it depends on the dataset characteristics and computational resources available.\n",
"\n",
"---\n",
"\n",
"## Trade-offs When Using a Reranker\n",
"- **Accuracy vs Processing Time**: \n",
" Striking a balance between achieving higher accuracy and minimizing processing time.\n",
"- **Performance Improvement vs Computational Cost**: \n",
" Weighing the benefits of improved performance against the additional computational resources required.\n",
"- **Search Speed vs Relevance Accuracy**: \n",
" Managing the trade-off between faster retrieval and maintaining high relevance in results.\n",
"- **System Requirements**: \n",
" Ensuring the system meets the necessary hardware and software requirements to support reranking.\n",
"- **Dataset Characteristics**: \n",
" Considering the scale, diversity, and specific attributes of the `dataset` to optimize reranker performance.\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tk9Sg0AKi76R"
},
"source": [
"Explaining the Implementation of Cross Encoder Reranker with a Simple Example"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "_DlGOaxxozWp"
},
"outputs": [],
"source": [
"# Helper function to format and print document content\n",
"def pretty_print_docs(docs):\n",
" # Print each document in the list with a separator between them\n",
" print(\n",
" f\"\\n{'-' * 100}\\n\".join( # Separator line for better readability\n",
" [f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)] # Format: Document number + content\n",
" )\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "obj9TBnHjFBt",
"outputId": "f3a0ecd4-e9ba-4bcf-a509-ef38631be18a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Document 1:\n",
"\n",
"Word2Vec\n",
"Definition: Word2Vec is a technique in NLP that maps words to a vector space, representing their semantic relationships based on context.\n",
"Example: In a Word2Vec model, \"king\" and \"queen\" are represented by vectors located close to each other.\n",
"Related Keywords: Natural Language Processing (NLP), Embedding, Semantic Similarity\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 2:\n",
"\n",
"Token\n",
"Definition: A token refers to a smaller unit of text obtained by splitting a larger piece of text. It can be a word, phrase, or sentence.\n",
"Example: The sentence \"I go to school\" can be tokenized into \"I,\" \"go,\" \"to,\" and \"school.\"\n",
"Related Keywords: Tokenization, Natural Language Processing (NLP), Syntax Analysis\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 3:\n",
"\n",
"Example: A customer information table in a relational database is an example of structured data.\n",
"Related Keywords: Database, Data Analysis, Data Modeling\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 4:\n",
"\n",
"Schema\n",
"Definition: A schema defines the structure of a database or file, detailing how data is organized and stored.\n",
"Example: A relational database schema specifies column names, data types, and key constraints.\n",
"Related Keywords: Database, Data Modeling, Data Management\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 5:\n",
"\n",
"Keyword Search\n",
"Definition: Keyword search involves finding information based on user-inputted keywords, commonly used in search engines and database systems.\n",
"Example: Searching \n",
"When a user searches for \"coffee shops in Seoul,\" the system returns a list of relevant coffee shops.\n",
"Related Keywords: Search Engine, Data Search, Information Retrieval\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 6:\n",
"\n",
"TF-IDF (Term Frequency-Inverse Document Frequency)\n",
"Definition: TF-IDF is a statistical measure used to evaluate the importance of a word within a document by considering its frequency and rarity across a corpus.\n",
"Example: Words with high TF-IDF values are often unique and critical for understanding the document.\n",
"Related Keywords: Natural Language Processing (NLP), Information Retrieval, Data Mining\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 7:\n",
"\n",
"SQL\n",
"Definition: SQL (Structured Query Language) is a programming language for managing data in databases. \n",
"It allows you to perform various operations such as querying, updating, inserting, and deleting data.\n",
"Example: SELECT * FROM users WHERE age > 18; retrieves information about users aged above 18.\n",
"Related Keywords: Database, Query, Data Management\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 8:\n",
"\n",
"Open Source\n",
"Definition: Open source software allows its source code to be freely used, modified, and distributed, fostering collaboration and innovation.\n",
"Example: The Linux operating system is a well-known open source project.\n",
"Related Keywords: Software Development, Community, Technical Collaboration\n",
"Structured Data\n",
"Definition: Structured data is organized according to a specific format or schema, making it easy to search and analyze.\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 9:\n",
"\n",
"Semantic Search\n",
"Definition: Semantic search is a search technique that understands the meaning of a user's query beyond simple keyword matching, returning results that are contextually relevant.\n",
"Example: If a user searches for \"planets in the solar system,\" the system provides information about planets like Jupiter and Mars.\n",
"Related Keywords: Natural Language Processing (NLP), Search Algorithms, Data Mining\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 10:\n",
"\n",
"GPT (Generative Pretrained Transformer)\n",
"Definition: GPT is a generative language model pre-trained on vast datasets, capable of performing various text-based tasks. It generates natural and coherent text based on input.\n",
"Example: A chatbot generating detailed answers to user queries is powered by GPT models.\n",
"Related Keywords: Natural Language Processing (NLP), Text Generation, Deep Learning\n"
]
}
],
"source": [
"from langchain_community.document_loaders import TextLoader\n",
"from langchain_community.vectorstores import FAISS\n",
"from langchain_huggingface import HuggingFaceEmbeddings\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"# Load documents\n",
"documents = TextLoader(\"./data/appendix-keywords.txt\").load()\n",
"\n",
"# Configure text splitter\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
"\n",
"# Split documents into chunks\n",
"texts = text_splitter.split_documents(documents)\n",
"\n",
"# Set up the embedding model\n",
"embeddings_model = HuggingFaceEmbeddings(\n",
" model_name=\"sentence-transformers/msmarco-distilbert-dot-v5\"\n",
")\n",
"\n",
"# Create FAISS index from documents and set up retriever\n",
"retriever = FAISS.from_documents(texts, embeddings_model).as_retriever(\n",
" search_kwargs={\"k\": 10}\n",
")\n",
"\n",
"# Define the query\n",
"query = \"Can you tell me about Word2Vec?\"\n",
"\n",
"# Execute the query and retrieve results\n",
"docs = retriever.invoke(query)\n",
"\n",
"# Display the retrieved documents\n",
"pretty_print_docs(docs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T_6qFjxQpUc3"
},
"source": [
"Now, let's wrap the base retriever with a `ContextualCompressionRetriever`. The `CrossEncoderReranker` leverages `HuggingFaceCrossEncoder` to re-rank the retrieved results.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_D3uj-DOpwUt"
},
"source": [
"Multilingual Support BGE Reranker: [`bge-reranker-v2-m3`](https://huggingface.co/BAAI/bge-reranker-v2-m3)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "f5yrJ-FTjWJB",
"outputId": "ddfebce9-e45a-4057-cadc-2767d7c26152"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Document 1:\n",
"\n",
"Word2Vec\n",
"Definition: Word2Vec is a technique in NLP that maps words to a vector space, representing their semantic relationships based on context.\n",
"Example: In a Word2Vec model, \"king\" and \"queen\" are represented by vectors located close to each other.\n",
"Related Keywords: Natural Language Processing (NLP), Embedding, Semantic Similarity\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 2:\n",
"\n",
"Open Source\n",
"Definition: Open source software allows its source code to be freely used, modified, and distributed, fostering collaboration and innovation.\n",
"Example: The Linux operating system is a well-known open source project.\n",
"Related Keywords: Software Development, Community, Technical Collaboration\n",
"Structured Data\n",
"Definition: Structured data is organized according to a specific format or schema, making it easy to search and analyze.\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 3:\n",
"\n",
"TF-IDF (Term Frequency-Inverse Document Frequency)\n",
"Definition: TF-IDF is a statistical measure used to evaluate the importance of a word within a document by considering its frequency and rarity across a corpus.\n",
"Example: Words with high TF-IDF values are often unique and critical for understanding the document.\n",
"Related Keywords: Natural Language Processing (NLP), Information Retrieval, Data Mining\n"
]
}
],
"source": [
"from langchain.retrievers import ContextualCompressionRetriever\n",
"from langchain.retrievers.document_compressors import CrossEncoderReranker\n",
"from langchain_community.cross_encoders import HuggingFaceCrossEncoder\n",
"\n",
"# Initialize the model\n",
"model = HuggingFaceCrossEncoder(model_name=\"BAAI/bge-reranker-v2-m3\")\n",
"\n",
"# Select the top 3 documents\n",
"compressor = CrossEncoderReranker(model=model, top_n=3)\n",
"\n",
"# Initialize the contextual compression retriever\n",
"compression_retriever = ContextualCompressionRetriever(\n",
" base_compressor=compressor, base_retriever=retriever\n",
")\n",
"\n",
"# Retrieve compressed documents\n",
"compressed_docs = compression_retriever.invoke(\"Can you tell me about Word2Vec?\")\n",
"\n",
"# Display the documents\n",
"pretty_print_docs(compressed_docs)"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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