💊 Pharma Chatbot

An intelligent, context-aware chatbot built to answer questions about pharmacy-related topics using documents, vector search (FAISS), and LLMs from HuggingFace. Built with LangChain, Streamlit, and Mistral 7B.

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🚀 Features

• 💬 Conversational interface with memory and retrieval
• 🔍 FAISS vector search over custom PDF documents
• 🧠 Mistral 7B LLM via Hugging Face Inference Endpoint
• 📄 Custom prompts to keep answers grounded in context
• ⚡ Built-in caching for performance

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🛠️ Tech Stack

• LangChain
• FAISS
• Hugging Face Transformers
• Streamlit
• PyPDF

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📁 Project Structure

├── data/                            # PDF documents
├── vectorstore/db_faiss/           # FAISS vector store (auto-generated)
├── create_memory_for_llm.py        # Loads PDFs and creates FAISS index
├── connect_memory_with_llm.py      # Retrieval + QA chain logic
├── medibot.py                      # Streamlit UI and main app
├── requirements.txt                # Python dependencies
└── .env                            # Environment variables (HF_TOKEN)

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📦 Setup & Installation

1. Clone the Repository

git clone https://github.com/yourusername/pharma-chatbot.git
cd pharma-chatbot

2. Set up Environment Variables

Create a .env file and add your Hugging Face API key:

HF_TOKEN=your_huggingface_token_here

3. Install Dependencies

pip install -r requirements.txt

4. Load and Index Your PDFs

python create_memory_for_llm.py

5. Launch the Chatbot

streamlit run medibot.py

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💡 Example Prompt

"What is the recommended dosage for Paracetamol according to the document?"

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⚠️ Security Warning

This project uses allow_dangerous_deserialization=True when loading the FAISS index. Only use this with trusted files as it may execute arbitrary code during deserialization.

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📌 To-Do

• Add PDF upload support from UI
• Enable feedback on chatbot answers
• Store chat history in a database
• Dockerize the app

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🧠 Credits

Built using:

• LangChain
• FAISS
• Hugging Face
• Streamlit

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📜 License

MIT License. See LICENSE file for details.