README.md

metadata

title: Multi-Agent Chatbot
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.25.0
app_file: app.py
pinned: false

🤖 Multi-Agent Knowledge Retrieval Chatbot

An AI-powered chatbot using OpenAI GPT-3.5 and Pinecone for intelligent knowledge retrieval.
This chatbot efficiently handles user queries by orchestrating multiple AI agents, retrieving relevant documents, and generating context-aware responses.

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Project Overview

In this project, I built a chatbot that:

• Retrieves relevant documents using Pinecone vector search
• Generates intelligent responses with OpenAI GPT-3.5
• Uses a multi-agent architecture to ensure modular and scalable processing

The system consists of multiple agents:

1. Query Agent - Converts user queries into embeddings and retrieves relevant documents.
2. Relevance Agent - Filters out unrelated search results.
3. Answering Agent - Generates intelligent, context-aware responses using OpenAI LLM.
4. Obnoxious Agent - Ensures safety by moderating inappropriate queries.
5. Head Agent - Coordinates the workflow between all agents, ensuring efficiency.

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Key Challenges & Solutions

1. Handling Irrelevant Questions

• If no relevant documents are found, the bot politely informs the user instead of generating misleading responses.

2. Filtering Inappropriate Queries

• The chatbot detects offensive or inappropriate queries and responds with a predefined safe message.

3. Managing General Conversations

• The chatbot handles general interactions, such as greetings, ensuring a user-friendly experience.

4. Multi-Turn Conversation Handling

• Users can ask follow-up questions, and the chatbot maintains context to provide deeper insights.

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Project Architecture

├── agents.py # Implements the multi-agent system ├── app.py # Streamlit-based UI for chatbot ├── requirements.txt # Dependencies required for the project ├── README.md # Project documentation └── .gitignore # Ensures sensitive files aren't committed

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Installation & Setup

1. Clone the Repository

git clone https://github.com/YujieHan-Rachel/multi-agent-chatbot.git
cd multi-agent-chatbot

2. Create virtual environment (optional but recommended)

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

# Install required dependencies
pip install -r requirements.txt

3. Set Up API Keys (Security Best Practice)

Ensure your OpenAI API Key and Pinecone API Key are stored as environment variables:

export OPENAI_API_KEY="your-openai-api-key"
export PINECONE_API_KEY="your-pinecone-api-key"

(For Windows, use set instead of export.)

Run the Chatbot

streamlit run app.py

Open your browser and go to http://localhost:8501 to interact with the chatbot.

Live Demo

🔗 Hugging Face Space: [Insert Link Here] 📂 GitHub Repository: https://github.com/YujieHan-Rachel/multi-agent-chatbot

Customization & Enhancements

If you want to modify the chatbot behavior:

Optimize Retrieval: Modify Query_Agent in agents.py for better search results. Enhance Response Quality: Experiment with prompt engineering in Answering_Agent. Improve Document Filtering: Adjust ranking logic in Relevance_Agent.

Contributing

Pull requests are welcome! If you find a bug or want to suggest improvements:

Fork the repo Create a new branch (git checkout -b feature-new-feature) Commit your changes (git commit -m "Added new feature") Push to your branch (git push origin feature-new-feature) Open a Pull Request

License

This project is licensed under the MIT License. Feel free to use and modify!

Contact

For questions or collaboration, reach out at:

Email: [yujierachel@gmail.com] LinkedIn: [LinkedIn Profile]

Final Notes

• This project showcases a scalable, modular AI chatbot with efficient knowledge retrieval.
• Ideal for AI-driven information retrieval, Q&A systems, and interactive chatbots.
• Can be expanded to support domain-specific datasets or company knowledge bases.
• Enjoy building AI-powered conversational systems! 🚀