Medibot is a context-aware medical chatbot that leverages large language models (LLMs) with FAISS-based vector memory to provide relevant, data-driven responses. It allows persistent memory and conversational context management, designed for efficiency and adaptability in healthcare-oriented environments.

• Integrates LLMs with vector memory for enhanced contextual conversation.
• Utilizes FAISS for efficient semantic search on embedded medical documents.
• Modular design for memory creation, connection, and inference.

data/                        - Data directory for medical documents
vectorstore/db_faiss/       - FAISS vector database files
connect_memory_with_llm.py  - Loads memory and connects with LLM for inference
create_memory_for_llm.py    - Embeds documents and creates FAISS vector store
medibot.py                  - Main execution script for chatbot
requirements.txt            - Required Python packages
Pipfile / Pipfile.lock      - Pipenv environment configuration
LICENSE                     - Project license (MIT)
README.md                   - Project overview and setup instructions

1. Clone the Repository

git clone https://github.com/biswajeetyadavv/medibot.git
cd medibot

2. Install Dependencies

pip install -r requirements.txt

or using pipenv:

pipenv install
pipenv shell

3. Prepare the Data

Place your medical documents (PDF or text files) inside the data/ folder

4. Create Vector Memory Run the following script to embed the documents and generate the FAISS vector store:

python create_memory_for_llm.py

5. Launch the Chatbot Start the chatbot interface that uses the embedded memory and connects to the LLM:

python connect_memory_with_llm.py

• Python 3.8+
• FAISS – for efficient vector similarity search
• OpenAI / compatible LLMs – for natural language understanding
• LangChain (if used) – for chaining memory and language tools
• Streamlit or Flask (optional frontend integration)

##Notes

• Ensure API keys or access tokens (e.g., OpenAI keys) are managed securely and excluded via .gitignore.
• The project supports easy extension for custom documents, additional tools, and deployment options.

##License This project is licensed under the MIT License. See the LICENSE file for more details.

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