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# Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is a technique that combines information retrieval with language model generation to produce more accurate and grounded responses.

## How RAG Works The RAG pipeline consists of three main stages: 1. **Indexing**: Documents are split into chunks, embedded into vectors, and stored in a vector database like Qdrant, Pinecone, or Weaviate. 2. **Retrieval**: When a user query arrives, it is embedded using the same model, and the most similar document chunks are retrieved via approximate nearest neighbor (ANN) search. 3. **Generation**: The retrieved chunks are passed as context to a large language model (LLM) like GPT-4 or Claude, which generates a response grounded in the provided information.

## Key Benefits - **Reduced hallucination**: By grounding responses in retrieved documents, RAG significantly reduces the tendency of LLMs to generate incorrect information. - **Up-to-date knowledge**: Unlike fine-tuning, RAG allows models to access the latest information without retraining. - **Source attribution**: Each response can be traced back to specific source documents, enabling citation and verification. - **Domain specificity**: Organizations can build RAG systems over their proprietary knowledge bases.

## Chunking Strategies Effective chunking is critical for RAG performance: - **Fixed-size chunking**: Split text into chunks of N tokens with overlap. Simple but may break semantic boundaries. - **Semantic chunking**: Split by paragraphs, sections, or sentences, respecting document structure. - **Recursive chunking**: Start with large chunks, recursively split those exceeding the size limit. - **Agentic chunking**: Use an LLM to determine optimal split points based on content. The optimal chunk size depends on the embedding model and use case, typically ranging from 256 to 1024 tokens.

## Vector Databases Vector databases are purpose-built for storing and searching high-dimensional embeddings: - **Qdrant**: Open-source, supports filtering and payload storage, written in Rust. - **Pinecone**: Managed cloud service with simple API, scales automatically. - **Weaviate**: Open-source with built-in vectorization modules and GraphQL API. - **Milvus**: Open-source, designed for billion-scale vector search. - **ChromaDB**: Lightweight, designed for AI application development.

## Advanced RAG Techniques

### Hybrid Search Combining dense vector search with sparse keyword search (BM25) improves recall by capturing both semantic similarity and exact term matches.

### Re-ranking After initial retrieval, a cross-encoder model re-ranks the results for better precision. Models like Cohere Rerank or BGE Reranker are commonly used.

### Query Expansion Generating multiple reformulations of the user query helps retrieve a broader set of relevant documents.

### Knowledge Graphs Integrating knowledge graphs with RAG adds structured relationships between entities, enabling multi-hop reasoning and better context understanding.