I will build a rag ai chatbot trained on your documents with vector search


About this gig
I build custom AI chatbots trained on your own documents (RAG) using OpenAI/ChatGPT or Claude, so your customers or team get instant, accurate answers with sources they can check.
I build Retrieval-Augmented Generation (RAG) chatbots that search your content using vector embeddings and answer with OpenAI or Claude models, grounded in your data instead of guessing.
What you get:
- Ingestion of PDFs, DOCX, web pages, Notion/Docs exports or database content
- Smart chunking, embeddings and similarity search (pgvector, Pinecone or similar)
- Answers with citations and an 'I don't know' fallback to reduce hallucinations
- Clean chat UI in Next.js/React, or an API you can plug into your product
- Optional auth, usage logs and feedback buttons
- Deployment and a clear README
Why me:
7+ years full stack (React, Next.js, Node.js, Python, AWS) with hands-on experience in NLP pipelines, embeddings and similarity search. I care about retrieval quality, not just a pretty chat box.
Send me a sample of your documents and your use case before ordering.
Get to know Aman
Professional software engineer
- FromIndia
- Member sinceAug 2013
- Avg. response time1 hour
- Last delivery3 years
Languages
English
FAQ
Will it make things up?
RAG greatly reduces this. I ground answers in retrieved text, show sources and configure it to say it does not know when the answer is not in your data.
Is my data private?
Your documents stay in your own accounts (vector DB, cloud, API key). I can delete my working copies after delivery.
Which vector database?
pgvector (Postgres), Pinecone, or others you already use. I will recommend the cheapest option that fits.
Can I add it to my website?
Yes. Standard and Premium include an embeddable chat widget or API.
