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RAG Projects: ChatDoc + AI/ML/GenAI Mentor Chatbot

Two RAG projects: ChatDoc (FastAPI + React + Milvus + Mistral) and an AI/ML/GenAI Mentor Chatbot (Gradio + LangChain + ChromaDB + Gemini). Unified page with separate sections.

FastAPI React Milvus License
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GitHub: 0xarchit/ChatDoc
GitHub: 0xarchit/DocumentChat-Simple-Rag-VectorDB-Project

Page Contents

  • ChatDoc
    • Overview
    • Preview
    • Architecture
    • Features
    • Tech Stack
    • Getting Started
    • API Reference
    • Future Goals
  • AI/ML/GenAI Mentor Chatbot
    • Running in Google Colab
    • Preview
    • Basic Working
    • Usage
    • Clearing Data

ChatDoc

GitHub: 0xarchit/ChatDoc
Live Demo: https://chatdoc.0xarchit.is-a.dev

License GitHub stars GitHub issues

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

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
Landing Page

Dashboard
Dashboard

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

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/

  • 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.

AI/ML/GenAI Mentor Chatbot

GitHub: 0xarchit/DocumentChat-Simple-Rag-VectorDB-Project

This vibe coded project provides an AI/ML/GenAI Mentor Chatbot built with Gradio, LangChain, ChromaDB, and Google Gemini. You can run the notebook in Google Colab to interact with the chatbot.

Running in Google Colab

  1. Open the notebook:

    • Import the notebook into your Google Drive or GitHub repository.
    • Open Google Colab and select File > Open notebook.
    • Under the Google Drive, GitHub, or Upload tab, locate your notebook (main.ipynb) and open it.

  2. Install dependencies:

    • Colab will automatically install required packages when running the first cell.

  3. Run all cells:

    • In Google Colab, select Runtime > Run all to execute every cell sequentially.
    • This will set up the environment, process documents, initialize the chatbot, and launch the Gradio interface.

Alternatively, you can use the public Colab link:

Open in Google Colab

Preview

collab
collab
working image 1
working image 1
working image 2
working image 2

Basic Working

This project combines several modern AI tools to create an interactive mentor chatbot:

  • Gradio UI: Provides a simple, user-friendly web interface for uploading documents and chatting with the AI mentor.
  • ChromaDB (Vector Database): Stores document chunks as vector embeddings, enabling fast and relevant retrieval of information based on your questions.
  • all-MiniLM-L6-v2 Embedding Model: Converts text from your documents and queries into numerical vectors, allowing semantic search and matching.
  • Google Gemini (Generative AI): Generates detailed, structured answers using both retrieved document context and its own AI/ML/GenAI knowledge.

How it works:

  1. Upload your PDF, PPT, or TXT files using the Gradio interface.
  2. The files are split into chunks and embedded using the all-MiniLM-L6-v2 model.
  3. ChromaDB stores these embeddings for efficient retrieval.
  4. When you ask a question, the system finds the most relevant document chunks and sends them, along with your question, to Gemini.
  5. Gemini generates a comprehensive answer, referencing your materials and providing real-world examples.

Usage

  • Upload your PDF, PPT, or TXT files using the notebook’s upload section.
  • After processing, ask questions about AI, ML, or GenAI topics.
  • The chatbot will provide structured, detailed responses with real-world examples based on provided material only.

Clearing Data

  • To clear all uploaded documents and chat histor��������������������������������������������������������