I will build a custom rag chatbot using langchain and vector db for your document

Tired of manually searching through hundreds of pages to find answers? I'll build a production-ready RAG (Retrieval-Augmented Generation) chatbot that lets you chat with your own documents PDFs, Word files, knowledge bases, or any text data.

What you get:

• Document ingestion pipeline with chunking and embedding
• Vector database setup (Pinecone, Weaviate, or Chroma)
• LLM integration (OpenAI, Claude, or AWS Bedrock)
• FastAPI backend with clean REST endpoints
• Conversational memory for follow-up questions

Why work with me: I'm a Senior GenAI Engineer with 5+ years of Python experience. I've built RAG systems that improved retrieval accuracy by 40% on enterprise-scale datasets. Every delivery is production-grade not just a notebook demo.

My stack: LangChain · LlamaIndex · FastAPI · PostgreSQL · AWS · Docker

Packages at a glance:

• Basic ($75) Single-doc RAG with API endpoint, up to 50 pages
• Standard ($150) Multi-doc RAG + memory + Streamlit UI
• Premium ($300) Full system with RBAC, semantic cache, AWS deployment

Message me before ordering if you have a specific use case happy to confirm it fits before you buy.