ask-pydantic 0.1.4

CLI tool to ask questions about Pydantic AI and Logfire documentation

ask-pydantic

A python library and CLI tool to ask questions about Pydantic AI and Logfire documentation using AI-powered search.

# run with uvx
uvx ask-pydantic
uvx ask-pydantic "How do I create an agent?"

# or use in your code
uv add ask-pydantic

from ask_pydantic import agent

result = agent.run_sync("How do I create an agent?")
print(result.output)

https://github.com/user-attachments/assets/f2ab1dfc-f90c-4311-a04c-c41b82eb3052

Installation

# install globally
uv tool install ask-pydantic

Requirements

You need an API key from at least one supported LLM provider:

• Anthropic (Claude) - Recommended

  export ANTHROPIC_API_KEY="your-key-here"
  

• OpenAI (GPT)

  export OPENAI_API_KEY="your-key-here"
  

• Google (Gemini)

  export GOOGLE_API_KEY="your-key-here"
  

• Mistral

  export MISTRAL_API_KEY="your-key-here"
  

• Groq

  export GROQ_API_KEY="your-key-here"
  

Tip: Add your API key to a .env file and source it:

echo 'export ANTHROPIC_API_KEY="your-key-here"' > .env
source .env && ask-pydantic "your question"

Usage

CLI Usage

Ask questions directly from the command line:

ask-pydantic "How do I create a Pydantic AI agent? Format your response nicely for a CLI."
ask-pydantic "What is Logfire?"
ask-pydantic "How do I use tools with agents?"

On first run, the tool will ask permission to download documentation (~20MB) from the Pydantic AI and Logfire repositories.

Programmatic Usage

Use the agent in your Python code:

from ask_pydantic import agent

result = agent.run_sync("How do I create an agent?")
print(result.output)

Or run inline with uv:

uv run --with ask-pydantic python - <<'EOF'
from ask_pydantic import agent

result = agent.run_sync("How do I use tools with Pydantic AI?")
print(result.output)
EOF

How It Works

1. Documentation Download: On first run, the tool downloads documentation from the Pydantic AI and Logfire repositories to ~/.ask-pydantic/docs/
2. Vector Database: Creates a searchable vector database using LanceDB and sentence transformers
3. Hybrid Search: Uses both semantic and keyword search to find relevant documentation
4. AI-Powered Answers: Uses your preferred LLM to generate comprehensive answers with links to source documentation

Examples

# Get started with Pydantic AI
ask-pydantic "How do I get started with Pydantic AI?"

# Learn about tools
ask-pydantic "What are tools and how do I use them?"

# Logfire queries
ask-pydantic "How do I set up Logfire for my application?"

# Specific features
ask-pydantic "How do I stream responses from an agent?"

Configuration

• Documentation: ~/.ask-pydantic/docs/
• Vector Database: /tmp/lancedb-pydantic-ai-chat

Development

You may use this as a tempalte for distributing your own Pydantic AI agents as packages so your users can import them in their code. Just expose your own agent object and adjust the ask-pydantic/cli.py to your needs.

# Clone the repository
git clone https://github.com/dsfaccini/ask-pydantic.git
cd ask-pydantic

# Install dependencies
uv sync

# Run locally
uv run ask-pydantic "your question"

Links

• Pydantic AI Documentation
• Logfire Documentation
• Pydantic

License

MIT