Metadata-Version: 2.4
Name: wikiqa
Version: 0.1.1
Summary: A powerful Wikipedia QA system with LLM integration
Home-page: https://github.com/pawan2411/WikiQA
Author: Pawan Kumar Rajpoot
Author-email: pawan.rajpoot2411@gmail.com
License: MIT
Project-URL: Bug Reports, https://github.com/pawan2411/WikiQA/issues
Project-URL: Source, https://github.com/pawan2411/WikiQA
Keywords: wikipedia,qa,llm,nlp,artificial intelligence
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Text Processing :: General
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: wikipedia>=1.4.0
Requires-Dist: openai>=1.0.0
Requires-Dist: anthropic>=0.5.0
Requires-Dist: together>=0.1.0
Requires-Dist: spacy>=3.0.0
Requires-Dist: pandas>=2.0.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: python-dateutil>=2.8.2
Requires-Dist: tqdm>=4.65.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: license-file
Dynamic: project-url
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# 🎯 WikiQA

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.

## ✨ Features

- 🤖 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)

## 🚀 Installation

```bash
pip install wikiqa
```

## 🎮 Quick Start

```python
from 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}")
```

## 🤝 Supported LLM Providers

- 🎨 OpenAI (GPT models)
- 🧠 Anthropic (Claude models)
- 🌟 Together (various open-source models)

## 📋 Requirements

- 🐍 Python 3.8 or higher
- 🌐 Internet connection for Wikipedia access
- 🔑 API key for your chosen LLM provider

## 📄 License

MIT License

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## ⭐ Star History

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