RAG Pipeline Framework

RAG Pipeline Framework Retrieval-Augmented Generation is the most practical way to give LLMs access to your private data — but getting it right is harder than the tutorials suggest. This framework provides the complete pipeline: document ingestion, intelligent chunking, embedding generation, vector store integration, retrieval with reranking, and answer generation with source citations. Plus evaluation tools to measure whether your RAG system actually returns correct answers. Key Features Document Ingestion — Load PDFs, Markdown, HTML, Word docs, and plain text with automatic format detection and metadata extraction Chunking Strategies — Fixed-size, semantic, recursive, and document-structure-aware chunking with configurable overlap Embedding Generation — Support for OpenAI, Cohere, and local embedding models with automatic batching and rate limiting Vector Store Integration — Pluggable backends for ChromaDB, Pinecone, Weaviate, pgvector, and FAISS with unified query interface Hybrid S