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AI & Automation ToolsMicro Business#228

RAG Chatbot on Private Documents

A private RAG chatbot that answers from your own PDFs and documents — with citations — running on a self-hostable LLM so nothing leaves your control.

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Project snapshot

Starting at
₹1,20,000$1,650
Delivery
40 days
Status
Draft
Tech stack
Next.js + LangChain + Qdrant + LLaMA 3

Cited

Answers

Private

LLM

Vector

Search

40 days

Delivery

Overview

RAG Chatbot on Private Documents lets your team ask questions in natural language against your own knowledge base of PDFs and documents. Answers cite the exact source passages, and the system can run on a private LLM so sensitive content never goes to a third party.

It is built for organisations sitting on large internal document sets — policies, contracts, research, manuals — who want fast, trustworthy answers with the privacy that a public AI service cannot offer.

The problem

Answers are buried across hundreds of documents, and pasting confidential files into a public AI tool is a non-starter. This gives grounded, cited answers from your own documents on infrastructure you control.

Who it's for

  • Organisations with large internal document sets to query.
  • Teams in regulated or sensitive fields that cannot use public AI tools.
  • Knowledge, legal, and research teams needing cited, verifiable answers.

Features

Chat with your documents

Ask questions in plain language against your uploaded PDFs and documents and get direct answers.

Citations in answers

Every answer points to the exact source passages, so responses are verifiable and trustworthy.

Private LLM option

The system can run on a self-hostable model, so confidential content never leaves your environment.

Vector search

Documents are embedded and searched semantically, so relevant passages are found even without exact keywords.

Document management

Upload, organise, and refresh the knowledge base as documents change.

User stories

  • As a knowledge worker, I want to ask questions across our documents so that I find answers fast.
  • As a compliance lead, I want a private LLM so that confidential content stays in-house.
  • As a user, I want citations so that I can verify each answer.
  • As an admin, I want to manage the document set so that answers stay current.

User flow

  1. 1

    Upload documents

    An admin adds PDFs and documents, which are embedded into the knowledge base.

  2. 2

    Ask a question

    A user asks in natural language through the chat interface.

  3. 3

    Retrieve & answer

    Relevant passages are retrieved and the model answers grounded in them.

  4. 4

    Verify via citations

    The user checks the cited sources to confirm the answer.

Architecture

A Next.js front end with a LangChain retrieval pipeline, a Qdrant vector store, and a LLaMA 3 model that can be self-hosted. Documents are chunked and embedded so questions retrieve the most relevant passages before the model answers.

Answers are generated only from retrieved context and returned with citations, which keeps responses grounded and verifiable rather than hallucinated.

Because the model and vector store can run in your own environment, no document content is sent to an external AI provider.

Tech stack

FrameworkNext.js
OrchestrationLangChain (RAG pipeline)
Vector storeQdrant
ModelLLaMA 3 (self-hostable)
HostingYour cloud / on-prem

Data model

EntityFields
Documentid, title, source, uploadedAt
Chunkid, documentId, text, embedding
Queryid, question, retrievedChunks, answer, createdAt

Build plan

  1. Weeks 1–2 — Ingestion & vectors

    ~14 days

    Document ingestion, chunking, embeddings, and Qdrant setup.

  2. Weeks 3–4 — RAG & citations

    ~14 days

    Retrieval pipeline, grounded answering with citations, chat UI.

  3. Weeks 5–6 — Private LLM & launch

    ~12 days

    Self-hosted model wiring, document management, evaluation, and deployment.

Deliverables

  • Deployed private RAG chatbot.
  • Vector-indexed knowledge base from your documents.
  • Cited answers and document management.
  • Full source code and deployment documentation.

Integrations

  • Qdrant vector database.
  • Self-hostable LLM (LLaMA 3) or a provider of your choice.
  • Optional: SSO for internal access.
  • Optional: connectors to existing document stores.

FAQ

Does my document content leave my environment?

It doesn't have to. The model and vector store can run in your own cloud or on-prem, keeping content private.

How do I know an answer is correct?

Every answer cites the exact source passages so you can verify it.

What document types are supported?

PDFs and common document formats; the set is uploaded and refreshed by an admin.

Can it use a different model?

Yes. It defaults to a self-hostable LLaMA 3, but can point at another model or provider.

Want RAG Chatbot on Private Documents built for you?

Share your requirements and CodersArts Build will scope it into a fixed timeline and price, starting at ₹1,20,000.

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