https://github.com/burnycoder/wikipedia-ai-agent
Wikipedia AI agent research assistant. LangChain's LangGraph's ReAct agent architecture, LLMs (OpenAI, Anthropic, Google), Wikipedia API, RAG with FAISS vector db, semantic chunking, GraphRAG, Streamlit frontend, terminal and web interfaces
https://github.com/burnycoder/wikipedia-ai-agent
agent agents ai artificial-intelligence claude deep-learning faiss gemini gpt-4 langchain langgraph large-language-model large-language-models llm llms machine-learning openai rag retrieval-augmented-generation vector-database
Last synced: 3 months ago
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Wikipedia AI agent research assistant. LangChain's LangGraph's ReAct agent architecture, LLMs (OpenAI, Anthropic, Google), Wikipedia API, RAG with FAISS vector db, semantic chunking, GraphRAG, Streamlit frontend, terminal and web interfaces
- Host: GitHub
- URL: https://github.com/burnycoder/wikipedia-ai-agent
- Owner: BurnyCoder
- License: mit
- Created: 2025-01-15T20:15:03.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-16T21:52:08.000Z (over 1 year ago)
- Last Synced: 2026-03-21T05:52:28.407Z (3 months ago)
- Topics: agent, agents, ai, artificial-intelligence, claude, deep-learning, faiss, gemini, gpt-4, langchain, langgraph, large-language-model, large-language-models, llm, llms, machine-learning, openai, rag, retrieval-augmented-generation, vector-database
- Language: Python
- Homepage:
- Size: 142 KB
- Stars: 1
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
[](https://opensource.org/licenses/MIT)
## 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:
```bash
git clone https://github.com/yourusername/wikipedia-agent.git
cd wikipedia-agent
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Configure by setting up environment variables in `.env`:
```bash
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
```bash
python -m streamlit run app.py
```
or
```bash
streamlit run app.py
```
Then visit `http://localhost:8501` in your browser.
### Terminal Interface
```bash
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](LICENSE) file for details.