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 and tree structures.

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
• Tree structure for organizing questions hierarchically
• Automatic generation of question-answer pairs from text
• Priority-based question suggestion system
• Visualization of the question tree

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 QuestionAnswerSystem

# Initialize the Question Answer System
qas = QuestionAnswerSystem("qa_system.db", "qa_collection")

# Add a question to the tree
question_id = qas.add_question("What is the capital of France?")

# Answer the question
qas.answer_question(question_id, "Paris")

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

# Get unanswered questions
unanswered = qas.get_unanswered_questions()

# Get the next suggested question
next_question = qas.suggest_next_question()

Advanced Usage Example

This example showcases the use of the QuestionAnswerSystem, including adding questions, answering them, and querying the knowledge base:

from qa_store import QuestionAnswerSystem

# Initialize the Question Answer System
qas = QuestionAnswerSystem("qa_system.db", "qa_collection")

# Add a root question
root_id = qas.add_question("What are the main topics in computer science?")

# Add child questions
qas.add_question("What is machine learning?", parent_id=root_id)
qas.add_question("What are data structures?", parent_id=root_id)

# Answer some questions
qas.answer_question(root_id, "The main topics in computer science include algorithms, data structures, artificial intelligence, and software engineering.")

# Query the Knowledge Base
results = qas.query("What are the fundamental areas of computer science?", 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()

# Get the next suggested question
next_question = qas.suggest_next_question()
if next_question:
    print(f"Suggested next question: {next_question['question']}")

Advanced Features

Generating QA Pairs from Text

You can automatically generate question-answer pairs from a given text:

from qa_store import QuestionAnswerKB

kb = QuestionAnswerKB()

text = """
Machine learning is a subset of artificial intelligence that focuses on
the development of algorithms and statistical models that enable
computer systems to improve their performance on a specific task through
experience.
"""

qa_pairs = kb.generate_qa_pairs(text)

for pair in qa_pairs:
    kb.add_qa(pair['q'], pair['a'])

Visualizing the Question Tree

You can visualize the question tree structure:

from qa_store import QuestionAnswerTree

tree = QuestionAnswerTree("qa_tree.db")
tree.visualize("question_tree")

This will generate a PNG image of the question tree.

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 - witt3rd@witt3rd.com

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