bind_functions convenience method #12518
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xieqihui
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Nov 21, 2023
I always take 20-30 seconds to re-discover where the `convert_to_openai_function` wrapper lives in our codebase. Chat langchain [has no clue](https://smith.langchain.com/public/3989d687-18c7-4108-958e-96e88803da86/r) what to do either. There's the older `create_openai_fn_chain` , but we haven't been recommending it in LCEL. The example we show in the [cookbook](https://python.langchain.com/docs/expression_language/how_to/binding#attaching-openai-functions) is really verbose. General function calling should be as simple as possible to do, so this seems a bit more ergonomic to me (feel free to disagree). Another option would be to directly coerce directly in the class's init (or when calling invoke), if provided. I'm not 100% set against that. That approach may be too easy but not simple. This PR feels like a decent compromise between simple and easy. ``` from enum import Enum from typing import Optional from pydantic import BaseModel, Field class Category(str, Enum): """The category of the issue.""" bug = "bug" nit = "nit" improvement = "improvement" other = "other" class IssueClassification(BaseModel): """Classify an issue.""" category: Category other_description: Optional[str] = Field( description="If classified as 'other', the suggested other category" ) from langchain.chat_models import ChatOpenAI llm = ChatOpenAI().bind_functions([IssueClassification]) llm.invoke("This PR adds a convenience wrapper to the bind argument") # AIMessage(content='', additional_kwargs={'function_call': {'name': 'IssueClassification', 'arguments': '{\n "category": "improvement"\n}'}}) ```
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Feb 2, 2024
I always take 20-30 seconds to re-discover where the `convert_to_openai_function` wrapper lives in our codebase. Chat langchain [has no clue](https://smith.langchain.com/public/3989d687-18c7-4108-958e-96e88803da86/r) what to do either. There's the older `create_openai_fn_chain` , but we haven't been recommending it in LCEL. The example we show in the [cookbook](https://python.langchain.com/docs/expression_language/how_to/binding#attaching-openai-functions) is really verbose. General function calling should be as simple as possible to do, so this seems a bit more ergonomic to me (feel free to disagree). Another option would be to directly coerce directly in the class's init (or when calling invoke), if provided. I'm not 100% set against that. That approach may be too easy but not simple. This PR feels like a decent compromise between simple and easy. ``` from enum import Enum from typing import Optional from pydantic import BaseModel, Field class Category(str, Enum): """The category of the issue.""" bug = "bug" nit = "nit" improvement = "improvement" other = "other" class IssueClassification(BaseModel): """Classify an issue.""" category: Category other_description: Optional[str] = Field( description="If classified as 'other', the suggested other category" ) from langchain.chat_models import ChatOpenAI llm = ChatOpenAI().bind_functions([IssueClassification]) llm.invoke("This PR adds a convenience wrapper to the bind argument") # AIMessage(content='', additional_kwargs={'function_call': {'name': 'IssueClassification', 'arguments': '{\n "category": "improvement"\n}'}}) ```
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I always take 20-30 seconds to re-discover where the
convert_to_openai_functionwrapper lives in our codebase. Chat langchain has no clue what to do either. There's the oldercreate_openai_fn_chain, but we haven't been recommending it in LCEL. The example we show in the cookbook is really verbose.General function calling should be as simple as possible to do, so this seems a bit more ergonomic to me (feel free to disagree). Another option would be to directly coerce directly in the class's init (or when calling invoke), if provided. I'm not 100% set against that. That approach may be too easy but not simple. This PR feels like a decent compromise between simple and easy.