RAG

Retrieval-Augmented Generation (RAG)

RAG is a technique that combines the generative capabilities of LLMs with external data retrieval to provide accurate, context-aware, and up-to-date responses.

Pipeline Architecture

1. Ingestion: Loading documents, chunking text, and generating embeddings.
2. Storage: Storing embeddings in a Vector Database (e.g., Pinecone, Chroma, Milvus).
3. Retrieval: Finding the most relevant chunks based on a user query.
4. Generation: Feeding the retrieved context along with the query to the LLM.

Advanced Techniques

• Hybrid Search: Combining keyword search (BM25) with semantic search.
• Reranking: Using a cross-encoder to refine the relevance of retrieved documents.
• GraphRAG: Utilizing Knowledge Graphs (e.g., Neo4j) to understand complex relationships.
• Asynchronous RAG: Using queues (Redis/Valkey) to handle high-concurrency document processing.

References

• Full Stack Generative and Agentic AI with Python
• Agent Memory Systems

AI RAG VectorDB NLP