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Pydantic-AI: Reliable Structured Output from LLMs with Pydantic
Enhance the reliability and structure of Large Language Model (LLM) outputs by seamlessly integrating Pydantic for data validation and parsing. This project provides tools and examples to reliably obtain structured data from LLMs.
Python
Added on 2025年5月13日9,409
Stars829
ForksPython
LanguageProject Introduction
Summary
This project is an agent framework or shim designed to bridge the gap between Large Language Models and structured data needs, leveraging the power of Pydantic to parse and validate LLM outputs into predictable data structures.
Problem Solved
LLMs often produce unstructured, inconsistent, or invalid text output, making it difficult to reliably extract specific data points or integrate outputs into downstream systems requiring structured formats (like JSON).
Core Features
Pydantic Model Definition
Define expected LLM output structure using standard Pydantic models for clear schema definition.
Automated Parsing & Validation
Automatically parse and validate raw text output from LLMs against the defined Pydantic schemas.
Robust Error Handling
Handle cases where LLM output does not conform to the schema, providing structured error feedback.
Output Guidance Tools
Includes utilities to guide LLMs towards generating valid JSON or other structured formats compatible with Pydantic.
Tech Stack
Python
Pydantic
LLMs (various via integrations/wrappers)
JSON
Use Cases
This project is essential for any application where structured and validated data is needed directly from LLM responses. Common use cases include:
Structured Data Extraction
Details
Extract specific information (e.g., names, dates, entities) from unstructured text generated by an LLM into a predictable Pydantic object.
User Value
Ensures that extracted data is correctly typed and formatted, simplifying downstream processing and storage.
Generating Reliable Structured Responses
Details
Define the expected format for API responses, configuration objects, or commands generated by an LLM, using Pydantic models.
User Value
Makes LLM output usable directly as input for other functions or system components.
Function Calling & Agent Definition
Details
Define complex inputs or outputs for AI agents or functions using Pydantic, and use this tool to parse LLM calls/responses.
User Value
Provides a robust mechanism for agents to exchange structured information.
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