PDF_Explainer_Agent

The PDF Explainer Agent is an AI-powered tool that reads and answers questions from PDF documents using LLMs. It's built using LangChain, OpenRouter (e.g., DeepSeek, Mixtral, etc.), and HuggingFace embeddings for semantic search.

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🚀 Features

• Load and process PDF files
• Embed text chunks using HuggingFace embeddings
• Search relevant content using FAISS vector store
• Answer user queries using an LLM via OpenRouter
• Interactive local UI using streamlit

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📁 Folder Structure

pdf_explainer_agent/
│
├── main.py                   # Entry point of the application
├── data.pdf                  # Your PDF file (replace as needed)
├── requirements.txt          # Python dependencies
├── .env                      # API keys and config (OpenRouter, HuggingFace)
└── README.md                 # This file

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🔧 Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/pdf_explainer_agent.git
cd pdf_explainer_agent

2. Create & Activate Virtual Environment

# Using Anaconda
conda create -n pdf-agent python=3.11
conda activate pdf-agent

3. Install Requirements

pip install -r requirements.txt

4. Set Up .env File

Create a .env file in the root directory:

OPENROUTER_API_KEY=your_openrouter_key_here
HUGGINGFACEHUB_API_TOKEN=your_huggingface_token_here

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▶️ Run the Application

streamlit run main.py

Then, open your browser at http://localhost:8501

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📦 Dependencies

Key Python packages used:

• langchain
• openai (for OpenRouter)
• streamlit
• PyPDF2 or pdfminer.six
• faiss-cpu
• sentence-transformers
• python-dotenv

Install them all using:

pip install -r requirements.txt

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✍️ Example Usage

1. Upload or hardcode a PDF (data.pdf)
2. Ask questions like:
   • “What is the purpose of the report?”
   • “Summarize section 3”
3. The agent will retrieve relevant chunks and generate an answer.

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🔐 Security Notes

• Do not commit your .env file.
• Never expose API keys publicly.
• Use only trusted models in OpenRouter.

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📚 References

• LangChain Docs
• OpenRouter
• HuggingFace Embeddings

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🙋‍♂️ Author

Built by Shreyas Mulay.
Need help? Open an issue or reach out directly.

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📝 License

MIT License – do whatever you want but give credit.