Wikipedia AI agent research assistant.

AI agent research assistant that uses Wikipedia's vast knowledge base to deliver comprehensive, well-researched answers to your questions.

Featuring a ReAct (Reasoning and Action) agent architecture that uses Wikipedia, or RAG (retrieval augmented generation), semantic chunking, embeddings, GraphRAG. Offers flexible access through both terminal and web interfaces.

Built on a modern stack including LangChain's LangGraph's ReAct agent, Wikipedia API, FAISS vector storage, Microsoft's GraphRAG, support for leading LLM providers (OpenAI, Anthropic, Google), and Streamlit frontend.

Features

• 🤖 Multiple LLM Support from OpenAI, Anthropic, and Google, such as:
  • OpenAI: gpt-4o, chatgpt-4o-latest, o1-2024-12-17, gpt-4-turbo
  • Anthropic: claude-3-5-sonnet-latest
  • Google: gemini-exp-1206, gemini-1.5-pro, gemini-2.0-flash-exp, gemini-2.0-flash-thinking-exp-1219
• 🔍 Wikipedia search capabilities
• 📚 RAG (Retrieval Augmented Generation) support, soon finished GraphRAG
• 💾 Conversation memory and thread management
• 🖥️ Both terminal and web interfaces
• 🔄 Streaming responses for real-time interaction
• 📝 Logging system

Architecture

The project uses a modular architecture with:

• LangGraph's ReAct agent for agent orchestration
• LangChain for LLM interactions and tool integration
• Wikipedia API as agent's tool
• FAISS for vector storage (when using RAG), or soon finished GraphRAG
• Streamlit for the web interface

Setup

1. Clone the repository:

git clone https://github.com/yourusername/wikipedia-agent.git
cd wikipedia-agent

2. Install dependencies:

pip install -r requirements.txt

3. Configure by setting up environment variables in .env:

OPENAI_API_KEY="your_key_here" # if you want to use OpenAI
ANTHROPIC_API_KEY="your_key_here" # if you want to use Anthropic
GOOGLE_API_KEY="your_key_here" # if you want to use Google
LLM="claude-3-5-sonnet-latest"  # or other supported models
USE_RAG="false"  # set to true to enable RAG, is false by default
SYSTEM_PROMPT="your_prompt_here" # if you want to change the default prompt

Usage

Web Interface

python -m streamlit run app.py 

or

streamlit run app.py

Then visit http://localhost:8501 in your browser.

Terminal Interface

python app_terminal.py

RAG Setup

To use RAG functionality instead of Wikipedia search:

1. Place wikipedia pages text files in the rag_data/ directory (by default includes only artificial_intelligence.txt, physics.txt)
2. Set USE_RAG=true in your .env file

GraphRAG will work soon too.

Contributing

Contributions are welcome! Please feel free to submit a pull request.

TODO

• Finish GraphRAG support
• Implement automatic Wikipedia scraping for RAG data collection
• Integrate text-to-speech capabilities
• Add support for more LLM providers (e.g., Mistral, Cohere)
• Create a Docker container for easy deployment
• Implement rate limiting and API usage tracking
• Add support for custom knowledge bases beyond Wikipedia
• Create a web API endpoint for programmatic access
• Implement caching to reduce API calls
• Add support for image and diagram generation
• Add benchmarking tools for different LLM models
• Add support for concurrent user sessions
• Create a configuration UI for easy setup

License

This project is licensed under the MIT License - see the LICENSE file for details.