I will build a rag ai chatbot trained on your documents with source citations


Informazioni su questo servizio
Need a chatbot that answers from YOUR documents, with citations, and no made-up answers? I build RAG (Retrieval-Augmented Generation) chatbots that read your PDFs, docs or website and reply only from your content, citing the source and page so your team and customers can verify every answer.
WHAT YOU GET: document ingestion (PDF, DOCX, TXT, web pages), embeddings and vector search (Pinecone, Chroma or pgvector), grounded answers with citations, a chat UI, widget, Slack or FastAPI endpoint, tested accuracy and a setup guide.
TECH: Python, LangChain, OpenAI / Gemini / open-source LLMs.
WHY ME: B.Tech in AI & ML (MIT-WPU, Pune) and AI intern at AssetCues, building RAG pipelines and agentic chatbots day to day.
PROCESS: free scoping chat, sample-data demo, milestone updates, handoff with source code and a Loom walkthrough.
Message me with your documents and goals before ordering.
Scopri di più su Nachiket J
AI Agent and n8n Automation Developer
- DaIndia
- Membro daott 2026
- Tempo di risposta medio1 ora
Lingue
Inglese
FAQ
What document types can the chatbot use?
PDF, DOCX, TXT, Markdown, web pages and Notion/Google Docs exports. Scanned files can use OCR as an extra.
How do citations work?
Each answer shows the source file and page or section it came from, so you can verify it instantly.
Will it make things up?
I constrain answers to retrieved content and add a fallback like 'I could not find this in your documents'.
Who pays for LLM and vector DB usage?
You, through your own API keys. I pick cost-efficient models and can use free options like Chroma.
Can it be embedded on my site or Slack?
Yes. Every package includes an API; widget, Slack or WhatsApp delivery depends on the package or extras.

