Building Contextual RAG Systems with Hybrid Search & Reranking

This guide details building a Contextual Retrieval Augmented Generation (RAG) system, enhancing standard RAG approaches by incorporating contextual information and hybrid search techniques. Standard RAG systems, while efficient for answering questions on custom data, often suffer from context loss due to document chunking. This improved system addresses this limitation.

Table of Contents:

• Naive RAG Architecture
• Naive RAG Limitations
• Hybrid RAG Workflow
• Contextual Retrieval Explained
• Implementing Contextual Retrieval
• Contextual Retrieval Pre-processing
• Contextual RAG with Hybrid Search & Reranking Architecture
• Hands-on Implementation:
  • Dependency Installation
  • OpenAI API Key Input
  • Environment Variable Setup
  • Dataset Acquisition
  • JSON Wikipedia Document Processing
  • PDF Research Paper Processing with Contextual Information
  • Vector Database Indexing & Semantic Retrieval
  • BM25 Indexing & Keyword Retrieval
  • Hybrid Search with Ensemble Retrieval
  • Retriever Enhancement with Reranker
• Frequently Asked Questions

Naive RAG Architecture:

A basic RAG system involves:

1. Data Processing & Indexing: Documents are loaded, chunked, embedded, and stored in a vector database.

2. Retrieval & Response Generation: User queries are processed, similar chunks are retrieved, and an LLM generates a response using the retrieved context.

Naive RAG Limitations:

• Contextual information loss due to isolated chunks.
• Suboptimal retrieval performance.
• Reliance on semantic similarity alone.

Hybrid RAG Workflow:

This approach combines semantic and keyword search:

BM25, a refinement of TF-IDF, is used for keyword search, factoring in document length. Results from both methods are combined using Reciprocal Rank Fusion (RRF).

Contextual Retrieval:

This technique enhances chunk quality by prepending context information generated by a large language model (LLM). Anthropic's research highlights the benefits of this approach.

Contextual Retrieval Pre-processing Architecture:

This pipeline processes documents, chunks them, generates contextual information using an LLM, and prepends this context to each chunk. Cost optimization strategies for LLM usage are discussed.

Contextual RAG with Hybrid Search & Reranking Architecture:

This architecture integrates contextual pre-processing, hybrid search (semantic and keyword retrieval), ensemble retrieval (RRF), and reranking (using a cross-encoder model like BAAI/bge-reranker-v2-m3) for improved retrieval accuracy.

Hands-on Implementation: This section provides a detailed, step-by-step guide with code examples using Langchain, PyMuPDF, ChromaDB, BM25, and OpenAI's embeddings and LLMs. The code covers data loading, processing, indexing, retrieval, and response generation. The example uses Wikipedia articles and research papers. Testing the pipeline with sample queries demonstrates the system's effectiveness.

Conclusion: The guide successfully demonstrates the construction of a Contextual RAG system with hybrid search and reranking, showcasing improved retrieval accuracy and response quality compared to naive RAG systems.

Frequently Asked Questions: This section answers common questions about RAG systems, their limitations, and the techniques used in this improved architecture.

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