ChatDoc

A unified retrieval-augmented generation (RAG) document API and web interface, powered by FastAPI, React, Vite, Milvus, and MistralAI.

Table of Contents

1. Overview
2. Architecture
3. Features
4. Tech Stack
5. Getting Started
   • Prerequisites
   • Backend Setup
   • Frontend Setup
   • Docker (Optional)
6. API Reference
7. Architecture Diagram
8. Future Goals
9. Contributing
10. License

Overview

ChatDoc is a web application enabling users to upload documents, extract and chunk text, store embeddings in Milvus, and query with state-of-the-art LLMs. It provides both a REST API and a web-based interface for seamless integration.

Preview

Landing Page
Dashboard

Complete Working Video: chatdoc.mkv

Architecture

flowchart TB
  subgraph Frontend
    UI[React & Vite] -->|REST API| API(FastAPI)
  end
  subgraph Backend
    API --> Extract[Text Extraction]
    Extract --> Chunk[Text Chunking]
    Chunk --> Embed[MistralAI Embedding]
    Embed --> Store[Milvus Vector Store]
    API --> Retrieve[Retrieval]
    Retrieve --> LLM[ChatOpenAI]
    LLM --> Store
  end
  Store -.->|Query Results| API

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Features

• Upload PDF, TXT, CSV, XLSX, PPTX, DOCX files via API or web form
• Automatic text extraction and chunking (500 tokens, 50 overlap)
• Embedding with MistralAI Embeddings & storage in Milvus (Zilliz)
• Retrieval and response generation via OpenAI-compatible LLM
• Real-time, responsive React UI with upload, history, and settings
• Per-request overrides for API keys, endpoints, and collections
• Admin endpoints for deleting uploads or clearing the vector store

Tech Stack

• Backend: FastAPI, Python, PyPDF2, python-pptx, python-docx, Pandas, Milvus
• Frontend: React, Vite, TypeScript, Tailwind CSS
• Embeddings: MistralAI
• Vector Database: Milvus (Zilliz Cloud)
• LLM: OpenAI-compatible ChatOpenAI via LangChain

Getting Started

Prerequisites

• Node.js >= 16 and npm/yarn
• Python >= 3.9
• Docker (optional)
• Milvus or Zilliz Cloud credentials
• MistralAI & OpenAI API keys

Backend Setup

cd Backend
copy .env.example .env
# Edit .env and set:
# MISTRAL_API_KEY, ZILLIZ_URI, ZILLIZ_TOKEN, HF_TOKEN (optional), COLLECTION_NAME
pip install -r requirements.txt
uvicorn main:app --reload

Frontend Setup

cd Frontend
npm install
npm run dev

Docker (Optional)

# Build and run backend container
docker build -t chatdocapi-backend .
docker run --rm -p 8080:8080 \
  -e MISTRAL_API_KEY=$env:MISTRAL_API_KEY \
  -e ZILLIZ_URI=$env:ZILLIZ_URI \
  -e ZILLIZ_TOKEN=$env:ZILLIZ_TOKEN \
  -e ZILLIZ_COLLECTION_NAME=$env:ZILLIZ_COLLECTION_NAME \
  chatdocapi-backend

API Reference

1) POST /upload

• Description: Upload document and store embeddings.
• Content-Type: multipart/form-data
• Fields:
  • file (required)
  • mistral_api_key, zilliz_uri, zilliz_token, collection_name (optional)
• Responses:
  • 200: { "upload_id": "<uuid>" }
  • 400: errors (no file, extraction failure)
  • 413: file too large

2) POST /query

• Description: Retrieve and answer based on stored chunks.
• Content-Type: application/json
• Body:

  {
    "question": "string",
    "upload_id": "string",
    ...overrides
  }

• Responses:
  • 200: { "answer": "<generated answer>" }
  • 400: invalid body
  • 500: generation error

3) DELETE /delete/{upload_id}

• Description: Remove all vectors for a given upload.
• Params: upload_id path, overrides as query params
• Response: { "status": "deleted" }

4) GET /deleteall

• Description: Clear entire vector store.
• Query: password (native admin) or per-request overrides
• Response: { "status": "all_deleted" }

Future Goals

• Streaming responses from the model to improve perceived latency and UX.
• Better OCR and robust file parsing for scanned PDFs and more file formats.
• Pluggable support for multiple vector stores (Milvus, FAISS, Pinecone, etc.).
• Increase upload and context limits (larger files, fewer artificial word/chunk restrictions).
• Personalization with login/signup, per-user profiles, metadata, and tags.
• Expand supported AI models/providers and allow per-request model selection.

Contributing

Contributions and suggestions welcome — if you'd like to see something prioritized, open an issue or a discussion.

Contributions are welcome! Please fork the repository, create a feature branch, and submit a pull request.

1. Fork it
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Open a Pull Request

For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License. See LICENSE for details.