PydanticAI Howto
PydanticAI is a Python Agent Framework that streamlines the development of production-grade AI applications by combining Pydantic's powerful data validation with LLM integration, offering type-safe dependency injection and model-agnostic support.
View MoreHow to Use PydanticAI
Install PydanticAI: Install using pip: 'pip install pydantic-ai' or for minimal installation use 'pip install pydantic-ai-slim'
Import Required Components: Import the basic components: 'from pydantic_ai import Agent, RunContext' and any other needed Pydantic components
Create an Agent: Initialize an Agent with a model (e.g., 'agent = Agent("openai:gpt-4o")' or 'agent = Agent("gemini-1.5-flash")')
Define Data Models: Create Pydantic models to define the structure of your inputs and outputs using class definitions with type hints
Set Up Dependencies: Define dependencies using @dataclass if your agent needs access to external resources or data during execution
Configure System Prompts: Add system prompts either statically through the agent constructor or dynamically using the @agent.system_prompt decorator
Add Tools: Register tools using @agent.tool decorator to give your agent additional capabilities and functions it can call
Implement Result Validation: Set up result validation using Pydantic models and the result_type parameter in your Agent configuration
Run the Agent: Execute the agent using either run_sync() for synchronous operations or run() for async operations, passing necessary dependencies
Optional: Add Monitoring: Integrate with Pydantic Logfire for monitoring by installing the logfire optional group and configuring logging
PydanticAI FAQs
PydanticAI is a Python Agent Framework designed to build production-grade applications with Generative AI. It's built by the team behind Pydantic and is currently in early beta. It aims to make it less painful to develop AI applications while providing type safety and structured response validation.
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