RAG Pipeline Builder

A production-grade Retrieval-Augmented Generation pipeline that handles document ingestion, intelligent chunking, embedding via OpenAI, and vector search via Pinecone.

Architecture

1. Document Ingestion: PDF, Markdown, and plain text support
2. Chunking Strategy: Recursive character splitting with overlap
3. Embedding: OpenAI text-embedding-3-small
4. Vector Store: Pinecone serverless with metadata filtering
5. Retrieval: Hybrid search combining dense and sparse vectors
6. Generation: Claude 3.5 Sonnet with retrieved context

Results

• 92% retrieval accuracy on benchmark dataset
• <200ms average query latency
• 10K+ documents indexed and searchable

Tags: RAG, LLM, Pipeline, Python