convert_to_openai_function not generating properly with nested BaseModels

[mimichelow]

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Example Code

from langchain_core.utils.function_calling import convert_to_openai_function
from enum import Enum
from pydantic import BaseModel, Field
from typing import List

class Tool(Enum):
    WEB_SEARCH = "web-search"
    RSS_FEED_SCRAPER = "rss-feed-scraper"
    USER_INPUT = "user-input"

class Task(BaseModel):
    """Tasks based on the objective"""
    id: int = Field(description="Create an ID and make sure all task IDs are in chronological order")
    task: str = Field(description="Task description should be detailed. TASK Example: 'Look up AI news from today (May 27, 2023) and write a poem'")
    tool: Tool = Field(description="Current tool options are [text-completion] [web-search] [rss-feed-scraper]")

class TaskList(BaseModel):
    """List of tasks"""
    task_list: List[Task] = Field(description="List of tasks")

extraction_functions = [convert_to_openai_function(TaskList)]

Description

I'm trying to migrate from the deprecated convert_pydantic_to_openai_function with the previous Pydantic class, but the new function is not going beyond the first level. The produced output is

[
  {
    "name": "TaskList",
    "description": "List of tasks",
    "parameters": {
      "type": "object",
      "properties": {},
      "required": ["task_list"]
    }
  }
]

Is this a known bug, do i have to manually produce the full schema to pass as a dictionary now? I can't find anything in how to generate the full openai function using the new function.

Thanks

System Info

System Information

OS: Windows
OS Version: 10.0.22621
Python Version: 3.11.6 (tags/v3.11.6:8b6ee5b, Oct 2 2023, 14:57:12) [MSC v.1935 64 bit (AMD64)]
Package Information

---

langchain_core: 0.1.22
langchain: 0.1.6
langchain_community: 0.0.19
langsmith: 0.0.87
langchain_openai: 0.0.5
Packages not installed (Not Necessarily a Problem)

---

The following packages were not found:

langgraph
langserve

[dosubot[bot]]

🤖

Based on the context provided, it appears that the convert_to_openai_function is not designed to handle nested Pydantic models. It only converts the top-level fields of the Pydantic model to the OpenAI function schema and does not recursively convert nested Pydantic models.

To address this issue, you would need to modify the convert_to_openai_function function to handle nested Pydantic models. This could be achieved by checking if a field is a Pydantic model and if so, recursively calling convert_to_openai_function on that field.

However, without the actual implementation of convert_to_openai_function, it's challenging to provide a concrete solution. The information provided in the context is based on the unit tests found in the LangChain repository, specifically libs/langchain/tests/unit_tests/utils/test_openai_functions.py.

Unfortunately, I couldn't find the current implementation of the convert_to_openai_function in the LangChain codebase. It would be helpful if you could provide more information about the implementation of this function or any related code snippets. This would allow me to give a more accurate and helpful response.

Please note that there might be a better answer that I wasn't able to find. If you have any additional information or context, please provide it.

Sources

• libs/langchain/tests/unit_tests/utils/test_openai_functions.py

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[mimichelow]

I want to add that it was absolutelly working with the old convert_pydantic_to_openai_function as intended.

[shmoradi-ai]

+1

[SoulEvill]

same here. this is confusing as it was recommend to switch from convert_pydantic_to_openai_function but it doesnt work.

[eyurtsev]

cc @baskaryan / @ccurme

[eyurtsev]

Created an issue here: #21270