e-lab/SyntaxShaper
Powering Agent Chains by Constraining LLM Outputs
This tool helps AI developers create more reliable and precise responses from large language models (LLMs), especially when using local models or building complex AI agents. It takes your desired data structure (like a Pydantic model) and a prompt, then ensures the LLM generates output that strictly adheres to that structure. This results in accurately formatted data ready for further processing in your AI applications.
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Use this if you are building AI agents or applications with local LLMs and need their outputs to consistently follow a specific, complex data format without parsing errors.
Not ideal if you are using commercial LLM APIs like GPT-4, which typically produce reliable structured outputs, or if your application only requires simple, unstructured text responses.
Stars
9
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Language
Python
License
MIT
Category
Last pushed
May 15, 2024
Commits (30d)
0
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