Build a self-contained Python application combining a local LLM with vector embeddings and semantic search capabilities. Implement RAG (Retrieval-Augmented Generation) pipeline with document ingestion, embedding generation, similarity search, and context-aware response generation. Include UI for managing document collections and performing semantic queries.
Build a self-contained Python application combining a local LLM with vector embeddings and semantic search capabilities. Implement RAG (Retrieval-Augmented Generation) pipeline with document ingestion, embedding generation, similarity search, and context-aware response generation. Include UI for managing document collections and performing semantic queries.