I will custom rag pipeline for your documents

I build custom RAG (Retrieval-Augmented Generation) pipelines that turn your documents into an intelligent Q&A assistant. You provide the files (PDFs, Word docs, websites, markdown, etc.) and I design an end-to-end solution: chunking, embeddings, vector store, retrieval, and a clean chat interface grounded in your own knowledge base.

What I will deliver:

• Custom RAG pipeline architecture for your documents
• Vector database setup (Pinecone, Qdrant, Chroma, Weaviate, etc.)
• Document ingestion and chunking strategy tuned to your content
• Retrieval and reranking optimized for your use case
• Source-cited answers to prevent hallucination
• Clean API or chat UI integration

I work with your preferred stack (Python, LangChain/LlamaIndex, OpenAI or any other LLM provider) and deliver tested, production-ready code with clear documentation.