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SaaSHub
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> ery few open-source LLMs explicitly claim they intentionally support structured data, but they’re smart enough and they have logically seen enough examples of JSON Schema that with enough system prompt tweaking they should behave.
Open source models are actually _better_ at structured outputs because you can adapt them using tools like JSONFormer et al (https://www.reddit.com/r/LocalLLaMA/comments/17a4zlf/reliabl...). The structured outputs can be arbitrary grammars, for example, not just JSON (https://github.com/outlines-dev/outlines#using-context-free-...).
FWIW, I've seen stronger performance from gpt-4-1106-preview when I use "response_type: JSON" (and providing a typescript schema in context), rather than using the "tools" API.
More flexible, and (evaluating non-scientifically!) qualitatively better answers & instruction following.
Example from a hack week project earlier this month: https://github.com/microsoft-healthcare-madison/hackweek-202...
FWIW, I've seen stronger performance from gpt-4-1106-preview when I use "response_type: JSON" (and providing a typescript schema in context), rather than using the "tools" API.
More flexible, and (evaluating non-scientifically!) qualitatively better answers & instruction following.
Example from a hack week project earlier this month: https://github.com/microsoft-healthcare-madison/hackweek-202...
* Gorilla [https://github.com/ShishirPatil/gorilla]
Could be interesting to try some of these exercises with these models.
I've been using the instructor[1] library recently and have found the abstractions simple and extremely helpful for getting great structured outputs from LLMs with pydantic.
1 https://github.com/jxnl/instructor/tree/main
we (like simpleaichat from OP) leverage Pydantic to specify the desired structured output, and under the hood Langroid translates it to either the OpenAI function-calling params or (for LLMs that don’t natively support fn-calling), auto-insert appropriate instructions into tje system-prompt. We call this mechanism a ToolMessage:
https://github.com/langroid/langroid/blob/main/langroid/agen...
We take this idea much further — you can define a method in a ChatAgent to “handle” the tool and attach the tool to the agent. For stateless tools you can define a “handle” method in the tool itself and it gets patched into the ChatAgent as the handler for the tool.