rag-systems

Build a RAG pipeline by implementing indexing and querying phases, combining term-based BM25 and embedding-based vector search for hybrid retrieval. This approach enhances LLM responses by injecting relevant external context retrieved from your knowledge base.

Document chunking strategies include fixed-size and semantic chunking for optimal document segmentation. Choosing the right chunking strategy improves retrieval accuracy by ensuring segments are contextually meaningful before generating embeddings for your knowledge base.

Query transformation improves retrieval-augmented generation accuracy through query expansion and HyDE. These techniques modify the original query to better match indexed documents, significantly increasing the relevance of retrieved context before passing it to the LLM.

Yes, you can use FAISS with sentence transformers to build a retrieval system. The implementation supports embedding generation and vector search using faiss-cpu and sentence_transformers, enabling efficient similarity matching across large document sets.

Use reranking in a RAG system after the initial retrieval phase to reorder search results based on relevance. Applying a reranking step refines the retrieved context from hybrid retrieval, ensuring the most accurate information is injected into the LLM response.

Evaluating RAG system performance requires defining specific metrics to assess retrieval accuracy and context relevance. The system provides evaluation capabilities to measure how effectively the pipeline retrieves and augments information for natural language processing tasks.