Chat_with_pdf_or_webpage

📚 chat-pdf-web

Your AI-powered chatbot for PDFs and web pages – built with Streamlit, LangChain, and Gemini 2.5 Pro.

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Live Link 👉 https://chat-with-pdf-or-webpage.onrender.com/

✨ Features

Feature	Description
🗂️ PDF Chat	Drag-and-drop any PDF and start chatting instantly.
🌐 Website Chat	Paste any URL to browse & chat with its content.
🧠 Gemini 2.5 Pro	Powered by Google’s newest reasoning model.
🔍 Vector Store	Fast & persistent embeddings with Chroma.
🖼️ Streamlit UI	Responsive sidebar and chat interface.
🔁 State-ful	Retains chat history and vectorstore across sessions.

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🚀 Quick Start

1. Clone

git clone https://github.com/<your-org>/chat-pdf-web.git
cd chat-pdf-web

2. Install

pip install -r requirements.txt
# or
pip install .

Python ≥ 3.10 recommended.

3. Launch

streamlit run main.py

A browser tab will open at http://localhost:8501.

4. Configure

In the sidebar:

1. Enter your Gemini API key.
2. Upload a PDF or enter a website URL.
3. Click Ingest and start chatting!

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📖 Usage

Step	UI/CLI
1. Provide key	Sidebar → “Enter your Gemini API key…”
2. Upload PDF	“Upload PDF” file picker.
3. Add website	“Enter website URL” input.
4. Ingest	Click Ingest (downloads, chunk, embed).
5. Chat	Type questions in the chat box.
6. Clear Everything	Click “End Chat” to reset.

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🛠 Tech Stack

Layer	Stack
UI	Streamlit, streamlit-extras
LLM	Google gemini-2.5-pro, google-generativeai
Embeddings	gemini-embedding-001
Orchestration	LangChain
Vector DB	ChromaDB (persisted)
PDF parsing	PyPDFLoader via pypdf
Web scraping	WebBaseLoader, BeautifulSoup4
Config	pyproject.toml (modern PEP621)

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📁 Data Flow

graph LR
A[User inputs PDF/URL] --> B{Ingestion}
B -->|PDF| C[PyPDFLoader]
B -->|URL| D[WebBaseLoader]
C & D --> E[LangChain chunks]
E --> F[Embed with Gemini]
F --> G[Chroma Vector Store]
H[User question] --> I[RAGChatBot.retrieve]
I --> G
G --> J[LLM.answer]
J --> K[Show response in UI]

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⚙️ Environment Variables (optional)

Var	Default	Purpose
GOOGLE_API_KEY	—	Falls back to sidebar input.
CHROMA_PERSIST_DIR	./chroma_db	Vectorstore path.
CHUNK_SIZE	1000	Text-split parameter.
CHUNK_OVERLAP	200	Text-split parameter.

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🤝 Contributing

We love community contributions!
Please see CONTRIBUTING.md for guidelines.

TL;DR

1. Fork.
2. pre-commit install.
3. Commit & push on a feature branch.
4. Create a PR 🚀

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🔗 References & Credits

• Streamlit – docs.streamlit.io
• LangChain – langchain.dev
• Google AI – ai.google.dev

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📄 License

MIT © 2024 Build Fast with AI and contributors.