rag_implementation

To build a RAG system, you need to load and split your documents, generate embeddings, and store them in a vector database. This allows the LLM to retrieve relevant context and provide factual, accurate answers from your knowledge base.

Yes, you can use Pinecone, Weaviate, or Qdrant as the vector database for your RAG application. These integrations allow you to efficiently store document embeddings and perform semantic searches to retrieve relevant information for the LLM.

The best way to reduce hallucinations and improve retrieval relevance is by implementing reranking strategies alongside hybrid search. This ensures the LLM receives only the most highly relevant context from your vector database before generating a response.

Yes, LangChain is a required dependency for implementing this RAG system. It provides the necessary framework to orchestrate document loading, text splitting, embedding generation, and the LLM chains needed for knowledge-grounded AI.

Semantic search works by converting text into numerical embeddings using models like sentence-transformers. The system then compares these embeddings in a vector store to find contextually similar documents, passing them to the LLM to ground its responses.

Dense search alone may miss exact keyword matches in document Q&A systems. To overcome this limitation, you should implement hybrid retrieval strategies that combine dense and sparse search methods, followed by reranking to maximize relevance.