fullstack-rag-pro

Building a production-grade RAG pipeline involves document ingestion, chunking, embedding generation, vector database querying, and LLM synthesis. This approach formats retrieved context for system prompts to enable AI chat applications with custom knowledge bases.

Hybrid search in semantic search combines cosine similarity search with metadata filtering to retrieve relevant documents. This mechanism optimizes vector database querying by narrowing results based on specific document attributes.

To generate embeddings for a given text during document ingestion, the pipeline processes various document formats and applies supported embedding models. This creates efficient vector storage for subsequent similarity search and retrieval operations.

Yes, you can use custom knowledge bases for AI chat applications by ingesting documents, generating embeddings, and querying a vector database. The retrieved context is formatted for LLM synthesis to answer user queries accurately.

Yes, the RAG pipeline supports metadata filtering for vector database querying by implementing cosine similarity search alongside it. This allows precise retrieval of relevant documents based on semantic similarity and specific metadata attributes.