A powerful Wikipedia Question Answering system with LLM integration. WikiQA allows you to extract information from Wikipedia articles using natural language questions and various extraction methods.
- 🤖 Direct question answering about Wikipedia content
- 🔍 Entity extraction from articles
- 📝 Article summarization with customizable length and focus
- ⏳ Timeline extraction from articles
- 🔌 Support for multiple LLM providers (OpenAI, Anthropic, Together)
pip install wikiqa
python -m spacy download enfrom wikiqa import WikiQA
# Initialize with your preferred LLM provider
qa = WikiQA(
llm_provider="together", # or "openai" or "anthropic"
api_key="your_api_key",
model="your_model_name" # optional, defaults to provider's best model
)
# Ask a question about a Wikipedia article
answer, page_url, revision_id = qa.ask(
"What is the capital of France?",
article="France"
)
print(f"Answer: {answer}")
print(f"Source: {page_url}")
# Extract specific information
birth_date, page_url, revision_id = qa.extract_entity(
"Albert Einstein",
"date of birth"
)
print(f"Birth date: {birth_date}")
# Get a summary
summary, page_url, revision_id = qa.summarize(
article="Python (programming language)",
length="paragraph"
)
print(f"Summary: {summary}")
# Extract a timeline
timeline, page_url, revision_id = qa.extract_timeline("World War II")
print(f"Timeline: {timeline.events}")- 🎨 OpenAI (GPT models)
- 🧠 Anthropic (Claude models)
- 🌟 Together (various open-source models)
- 🐍 Python 3.8 or higher
- 🌐 Internet connection for Wikipedia access
- 🔑 API key for your chosen LLM provider
MIT License
Contributions are welcome! Please feel free to submit a Pull Request.