I will build a rag ai chatbot for your documents with langchain and openai

Most companies have great documentation that nobody can search. I build RAG (retrieval-augmented generation) chatbots that answer questions from YOUR documents, with citations back to the source section so users can verify every answer.

I don't just wire up an API, I measure retrieval quality. In my own published case study, naive fixed-size chunking retrieved the right section for only 34% of test questions. After switching to section-aware 300-character chunks with overlap, retrieval hit 100% on a 32-question eval set. That tuning step is what separates a demo from something you can trust.

What you get:

• Full pipeline: document parsing, smart chunking, embeddings, vector search
• Grounded prompting so answers stay faithful to your content
• An evaluation harness you can rerun with your own questions
• A working interface (Gradio/Streamlit) your team can try immediately

I work in Python with LangChain, OpenAI, and open-source embedding models (your data never has to leave your infrastructure if privacy matters).

Message me with your document type and use case before ordering. I'll tell you honestly whether RAG is the right fit.