https://github.com/0xarchit/chatdoc
Upload any document and start intelligent conversations. Get instant answers, summaries, and insights from your files using advanced RAG technology.
https://github.com/0xarchit/chatdoc
chatdoc chatdocument rag rag-chatbot
Last synced: 2 months ago
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Upload any document and start intelligent conversations. Get instant answers, summaries, and insights from your files using advanced RAG technology.
- Host: GitHub
- URL: https://github.com/0xarchit/chatdoc
- Owner: 0xarchit
- License: other
- Created: 2025-08-22T06:58:46.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-09-21T14:05:35.000Z (9 months ago)
- Last Synced: 2025-09-21T16:10:24.812Z (9 months ago)
- Topics: chatdoc, chatdocument, rag, rag-chatbot
- Language: TypeScript
- Homepage: https://chatdoc.0xarchit.is-a.dev
- Size: 7.66 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: .github/SECURITY.md
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README
# ChatDoc
[](https://github.com/0xarchit/chatdoc/stargazers)
[](https://github.com/0xarchit/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](#overview)
2. [Architecture](#architecture)
3. [Features](#features)
4. [Tech Stack](#tech-stack)
5. [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Backend Setup](#backend-setup)
- [Frontend Setup](#frontend-setup)
- [Docker (Optional)](#docker-optional)
6. [API Reference](#api-reference)
7. [Architecture Diagram](#architecture-diagram)
8. [Future Goals](#future-goals)
9. [Contributing](#contributing)
10. [License](#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](assets/chatdoc.mkv)
## Architecture
```mermaid
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
```
## 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
```powershell
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
```powershell
cd Frontend
npm install
npm run dev
```
### Docker (Optional)
```powershell
# 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": "" }`
- `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**:
```json
{
"question": "string",
"upload_id": "string",
...overrides
}
```
- **Responses**:
- `200`: `{ "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](LICENSE) for details.