rag-implementation

Build LLM applications with external knowledge via vector databases and semantic search.

• Retrieval-Augmented Generation systems for LLMs
• Use external knowledge sources to ground AI responses
• Enable LLMs to access domain-specific knowledge

• Store and retrieve document embeddings efficiently
• Convert text to numerical vectors for semantic search
• Combine dense and sparse retrieval for better results
• Improve retrieval quality with reranking
• Support hybrid, multi-query, and contextual compression
• Reduce hallucinations with source-cited, grounded responses

1. 1.Load and split documents into chunks
2. 2.Create embeddings and store in vector database
3. 3.Build retrieval chain with QA pipeline
4. 4.Use hybrid or multi-query strategies for better results
5. 5.Apply contextual compression to filter relevant context