qa-store 0.1.3

Like a key-value store, but for questions and answers.

Question-Answer Store

Like key-value, but with questions and answers.

A Python package for managing and querying a Question-Answer Knowledge Base using vector embeddings.

Features

• Store and retrieve question-answer pairs
• Generate question rewordings for improved retrieval
• Metadata support for filtering and additional information
• Vector-based similarity search for efficient querying
• Persistent storage using ChromaDB

Installation

You can install qa-store using pip:

pip install qa-store

Usage

Here's a quick example of how to use qa-store:

from qa_store import QuestionAnswerKB

# Initialize the Knowledge Base
kb = QuestionAnswerKB()

# Add a question-answer pair
kb.add_qa("What is the capital of France?", "Paris", metadata={"source": "Geography 101"})

# Query the Knowledge Base
results = kb.query("What's the capital city of France?")
print(results[0]["answer"])  # Output: Paris

# Update an answer
kb.update_answer("What is the capital of France?", "Paris (City of Light)")

# Query with metadata filter
results = kb.query("capital of France", metadata_filter={"source": "Geography 101"})

Advanced Usage Example

This example showcases the use of question rewordings for both adding QA pairs and querying the knowledge base:

from qa_store import QuestionAnswerKB

# Initialize the Knowledge Base
kb = QuestionAnswerKB()

# Add a question-answer pair with rewordings
original_question = "What is the best way to learn programming?"
answer = "The best way to learn programming is through consistent practice, working on real projects, and continuous learning."

added_questions = kb.add_qa(
    question=original_question,
    answer=answer,
    metadata={"topic": "education", "field": "computer science"},
    num_rewordings=3
)

print("Added questions:")
for q in added_questions:
    print(f"- {q}")

# Now let's query the Knowledge Base with a different phrasing
query_question = "How can I become proficient in coding?"

results = kb.query(
    question=query_question,
    n_results=2,
    metadata_filter={"topic": "education"},
    num_rewordings=2
)

print("\nQuery results:")
for result in results:
    print(f"Question: {result['question']}")
    print(f"Answer: {result['answer']}")
    print(f"Similarity: {result['similarity']:.2f}")
    print(f"Metadata: {result['metadata']}")
    print()

For more detailed usage instructions, please refer to the documentation.

Requirements

• Python 3.7+
• Dependencies listed in requirements.txt

Contributing

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

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

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

Contact

Donald Thompson - @dt_public - [e-mail]

Project Link: https://github.com/witt3rd/qa-store