Skip to content

README.md

Latest commit

 

History

History
162 lines (121 loc) · 4.96 KB

README.md

File metadata and controls

162 lines (121 loc) · 4.96 KB

PDF ChatBot - Chat with Any PDF using AI

Python Streamlit Llama Pinecone License

An AI-powered PDF ChatBot with a dark chat UI. Upload any PDF, ask questions in a conversational interface, and get grounded answers powered by Llama 3.3 70B via OpenRouter.

Built as a portfolio project - fast, lightweight, zero local GPU required.

Features

  • Upload & parse any PDF with PyMuPDF
  • Smart text chunking via LangChain
  • Semantic embeddings using all-MiniLM-L6-v2
  • Vector storage & retrieval with Pinecone (serverless)
  • Answers from Llama 3.3 70B via OpenRouter (free tier)
  • Persistent chat history with full conversation UI
  • Dark GitHub-inspired theme
  • Auto-retry on rate limits, batch upsert for large PDFs

Architecture

PDF Upload
    |
    v
PyMuPDF -- extract raw text
    |
    v
LangChain RecursiveCharacterTextSplitter -- chunk (500 tokens, 50 overlap)
    |
    v
SentenceTransformers all-MiniLM-L6-v2 -- embed chunks -> 384-dim vectors
    |
    v
Pinecone Serverless -- store & query vectors (top-5 retrieval)
    |
    v
OpenRouter -> Llama 3.3 70B Instruct -- generate grounded answer
    |
    v
Streamlit Dark Chat UI -- display in conversation thread

Tech Stack

Layer Technology
Frontend Streamlit with custom dark CSS
PDF Parsing PyMuPDF
Text Splitting LangChain RecursiveCharacterTextSplitter
Embeddings SentenceTransformers all-MiniLM-L6-v2
Vector DB Pinecone Serverless (free tier)
LLM Llama 3.3 70B via OpenRouter (free)
API Client OpenAI Python SDK

Project Structure

app/
├── app.py                  # Streamlit UI entry point
├── config.py               # All constants (model names, chunk sizes, etc.)
├── pdf_utils.py            # PDF extraction and text chunking
├── embedder.py             # SentenceTransformer loading and vector encoding
├── vector_store.py         # Pinecone connect, upsert, and query
├── llm.py                  # OpenRouter API call with retry logic
├── .streamlit/
│   └── config.toml         # Dark theme + server config for Streamlit Cloud
├── .env.example            # Template -- copy to .env and fill in keys
├── .gitignore              # Excludes .env, .venv, temp.pdf, __pycache__
├── requirements.txt        # Minimal direct dependencies (7 packages)
└── README.md               # This file

Local Setup

1. Clone the repo

git clone https://github.com/RohitDSonawane/PDFChatBot.git
cd PDFChatBot

2. Create and activate a virtual environment

python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Set up environment variables

cp .env.example .env

Then edit .env and add your keys:

PINECONE_API_KEY=your-pinecone-api-key
OPENROUTER_API_KEY=your-openrouter-api-key

5. Run the app

streamlit run app.py

Deploy on Streamlit Community Cloud (Free)

  1. Push this repo to GitHub (public)
  2. Go to share.streamlit.io -> sign in with GitHub
  3. Click New app -> select this repo -> set app.py as entry point
  4. Go to Advanced settings -> Secrets and add:
PINECONE_API_KEY = "your-pinecone-api-key"
OPENROUTER_API_KEY = "your-openrouter-api-key"
  1. Click Deploy -- live in ~2 minutes

Author

Rohit Sonawane

Star this repo if you found it useful! Feedback, issues, and PRs are welcome.