Introduction

This repository contains examples and explanations of how to use PydanticAI - a Python Agent Framework designed to make it easier to build production-grade applications with Generative AI.

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Introduction to PydanticAI

PydanticAI is a Python Agent Framework created by the team behind Pydantic, designed to streamline the development of production-grade applications with Generative AI. Building on the success and widespread adoption of Pydantic in the Python AI ecosystem, PydanticAI offers a type-safe, model-agnostic approach that seamlessly integrates with popular LLM providers like OpenAI, Gemini, and Groq. The framework emphasizes developer ergonomics by combining structured response validation, streamed responses, and a dependency injection system, all while allowing developers to leverage standard Python development practices for control flow and agent composition.

Pydantic AI Core Concepts

1. Agents: The primary interface for interacting with LLMs, allowing you to define system prompts and manage interactions.
2. Dependencies: A type-safe system for injecting runtime context and accessing external services, making testing and integration easier.
3. Results: Agents can return plain text, structured data, or streamed responses, all validated by Pydantic models.
4. Messages and Chat History: Provides access to complete message history and tools for analyzing agent behavior and continuing conversations.
5. Testing and Evals: Supports unit tests and evaluations to assess model performance and ensure application reliability.
6. Debugging and Monitoring: Integrates with Pydantic Logfire for real-time debugging, performance monitoring, and querying of agent runs.

Getting Started

To begin using PydanticAI, follow these steps:

1. Python: Ensure you have Python installed on your system. PydanticAI requires Python 3.9 or later.

2. Install Requirements: Navigate to the root directory of the repository and install the necessary dependencies by running:

   pip install -r requirements.txt
   

3. Set Up Environment Variables: Copy the provided .env.example file to a new file named .env. Open the .env file and add your OpenAI API key:

   OPENAI_API_KEY=your_openai_api_key_here
   

   Make sure to replace your_openai_api_key_here with your actual OpenAI API key.

4. Run the Introduction Script: To get a feel for how PydanticAI works, execute the introduction.py script:

This script will guide you through the basic functionalities of PydanticAI, demonstrating how to interact with language models using the framework.

By following these steps, you'll be set up to explore and build applications with PydanticAI. For more detailed examples and documentation, refer to the PydanticAI documentation.

Problems I've Encountered

PydanticAI is in early beta, and the API is still subject to change. There is still a lot more to do.

• Model Parameters: Currently, I couldn't find a way to adjust model parameters like temperature.

• Message History with Tools: There is a problem with message history when using tools. The following error occurs:

  BadRequestError: Error code: 400 - {'error': {'message': "An assistant message with 'tool_calls' must be followed by tool messages responding to each 'tool_call_id'. The following tool_call_ids did not have response messages: call_KMMn5Bo6wPN3aZosdstleZO2", 'type': 'invalid_request_error', 'param': 'messages.[6].role', 'code': None}}