# Convert\_pydantic\_to\_openai\_function is deprecated

**URL:** <https://community.deeplearning.ai/t/convert-pydantic-to-openai-function-is-deprecated/587445>\
**Category:** Functions, Tools and Agents with LangChain\
**Created:** [March 9, 2024, 11:46pm UTC](https://community.deeplearning.ai/t/convert-pydantic-to-openai-function-is-deprecated/587445 "2024-03-09T23:46:32Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![jperedo](https://avatars.discourse-cdn.com/v4/letter/j/aca169/32.png) [@jperedo](https://community.deeplearning.ai/u/jperedo)\
**Post date:** [March 9, 2024, 11:46pm UTC](https://community.deeplearning.ai/t/convert-pydantic-to-openai-function-is-deprecated/587445/1 "2024-03-09T23:46:32Z")

</div>

In the chapter about tagging, when I try to run the examples locally with the latest version of Langchain, I get a deprecation warning that convert\_pydantic\_to\_openai\_function is going away. The message says to use convert\_to\_openai\_function. I was able to convert the extraction example to use that function and the @tool decorator. However, It only seems to pull a single name, age from the text indepently of what I put in pass to the model. Any ideas would be appreciated. Here is the code:

```python
from langchain.tools import tool
from langchain_core.prompts import ChatPromptTemplate
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
from langchain_core.utils.function_calling import convert_to_openai_function
from pprint import pprint

from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0)

@tool
def info(name: str, age: int = None):
    """Information to extract."""
    return [{"name": name, "age": age}]

def with_decorator():
    print(info.name)
    print(info.description)
    print(info.args)

    tools = [info]
    extraction_functions = [convert_to_openai_function(t) for t in tools]
    pprint(extraction_functions)

    extraction_model = model.bind(functions=extraction_functions, function_call={"name": "info"})

    # print(info.run({"name": "Tom", "age": 20}))
    # print(extraction_model.invoke("Joe is 30, his mom is Martha"))

    prompt = ChatPromptTemplate.from_messages([
        ("system", "Extract the relevant information, if not explicitly provided do not guess. Extract partial info"),
        ("human", "{input}")
    ])

    extraction_chain = prompt | extraction_model | JsonOutputFunctionsParser()
    # print(extraction_chain.invoke({"input": "Joe is 30, his mom is Martha"}))
    print(extraction_chain.invoke({"input": "Tom is 20. Joe is 30"}))

```