RAG - Hybrid search and RAG pipeline using FAISS DB

Ramya Perumal Posted on May 31 RAG - Hybrid search and RAG pipeline using FAISS DB # ai # rag # beginners # nlp Hybrid Search Hybrid search is a combination of dense embeddings and sparse embeddings. Dense embeddings focus on semantic meaning, while sparse embeddings focus on exact keyword matching. By combining both approaches, hybrid search improves retrieval accuracy and relevance. OpenSearch is commonly used as a search engine for: Log analysis Observability and monitoring One of the key features of OpenSearch is hybrid search, which combines: Vector search (dense retrieval) BM25-based search (sparse retrieval) BM25 internally uses concepts such as: TF (Term Frequency) IDF (Inverse Document Frequency) This allows OpenSearch to retrieve documents based on both semantic meaning and exact keyword matches. RAG Cycle A Retrieval-Augmented Generation (RAG) system consists of the following stages: 1. Document Ingestion Documents are split into chunks using a chunking strategy. 2. Embedding Generation Each chunk is converted into an embedding vector using an embedding model. 3. Storage The generated vectors are stored in a vector database. 4. Retrieval When a user submits a query: The query is converted into an embedding vector Similar documents are retrieved from the vector database 5. Augmentation The Augmentor combines: User query Retrieved documents/chunks Prompt instructions This combined context is then sent to the LLM. Generation The LLM processes the augmented context and generates a human-readable response. RAG Flow Documents ↓ Chunking ↓ Embeddings ↓ Vector Database ↓ User Query ↓ Retrieval ↓ Augmentation (Query + Retrieved Documents + Instructions) ↓ LLM ↓ Human Readable Response FAISS FAISS (Facebook AI Similarity Search) is an open-source library used for efficient vector similarity search. FAISS is commonly used to: Store vector indexes locally Perform similarity search efficiently Build small to medium-scale RAG applications Advantages Fast similarity sea