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openai[patch]: refactor with_structured_output (#18052)
- make schema Optional with default val None, since in json_mode you don't need it if not parsing to pydantic - change return_type -> include_raw - expand docstring examples
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Bagatur
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@ -754,10 +754,10 @@ class ChatOpenAI(BaseChatModel):
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@overload
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def with_structured_output(
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self,
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schema: _DictOrPydanticClass,
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schema: Optional[_DictOrPydanticClass] = None,
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*,
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method: Literal["function_calling", "json_mode"] = "function_calling",
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return_type: Literal["all"] = "all",
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include_raw: Literal[True] = True,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, _AllReturnType]:
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...
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@ -765,10 +765,10 @@ class ChatOpenAI(BaseChatModel):
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@overload
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def with_structured_output(
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self,
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schema: _DictOrPydanticClass,
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schema: Optional[_DictOrPydanticClass] = None,
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*,
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method: Literal["function_calling", "json_mode"] = "function_calling",
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return_type: Literal["parsed"] = "parsed",
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include_raw: Literal[False] = False,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, _DictOrPydantic]:
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...
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@ -776,10 +776,10 @@ class ChatOpenAI(BaseChatModel):
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@beta()
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def with_structured_output(
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self,
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schema: _DictOrPydanticClass,
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schema: Optional[_DictOrPydanticClass] = None,
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*,
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method: Literal["function_calling", "json_mode"] = "function_calling",
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return_type: Literal["parsed", "all"] = "parsed",
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include_raw: bool = False,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, _DictOrPydantic]:
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"""Model wrapper that returns outputs formatted to match the given schema.
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@ -795,10 +795,10 @@ class ChatOpenAI(BaseChatModel):
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or "json_mode". If "function_calling" then the schema will be converted
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to an OpenAI function and the returned model will make use of the
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function-calling API. If "json_mode" then OpenAI's JSON mode will be
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used.
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return_type: The wrapped model's return type, either "parsed" or "all". If
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"parsed" then only the parsed structured output is returned. If an
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error occurs during model output parsing it will be raised. If "all"
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used. Note that if using "json_mode" then you must include instructions
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for formatting the output into the desired schema into the model call.
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include_raw: If False then only the parsed structured output is returned. If
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an error occurs during model output parsing it will be raised. If True
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then both the raw model response (a BaseMessage) and the parsed model
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response will be returned. If an error occurs during output parsing it
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will be caught and returned as well. The final output is always a dict
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@ -807,12 +807,12 @@ class ChatOpenAI(BaseChatModel):
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Returns:
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A Runnable that takes any ChatModel input and returns as output:
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If return_type == "all" then a dict with keys:
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If include_raw is True then a dict with keys:
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raw: BaseMessage
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parsed: Optional[_DictOrPydantic]
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parsing_error: Optional[BaseException]
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If return_type == "parsed" then just _DictOrPydantic is returned,
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If include_raw is False then just _DictOrPydantic is returned,
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where _DictOrPydantic depends on the schema:
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If schema is a Pydantic class then _DictOrPydantic is the Pydantic
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@ -820,7 +820,7 @@ class ChatOpenAI(BaseChatModel):
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If schema is a dict then _DictOrPydantic is a dict.
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Example: Function-calling, Pydantic schema (method="function_calling", return_type="parsed"):
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Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
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.. code-block:: python
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from langchain_openai import ChatOpenAI
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@ -841,7 +841,7 @@ class ChatOpenAI(BaseChatModel):
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# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
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# )
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Example: Function-calling, Pydantic schema (method="function_calling", return_type="all"):
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Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
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.. code-block:: python
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from langchain_openai import ChatOpenAI
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@ -853,7 +853,7 @@ class ChatOpenAI(BaseChatModel):
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justification: str
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llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
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structured_llm = llm.with_structured_output(AnswerWithJustification, return_type="all")
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structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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# -> {
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@ -862,7 +862,7 @@ class ChatOpenAI(BaseChatModel):
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# 'parsing_error': None
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# }
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Example: Function-calling, dict schema (method="function_calling", return_type="parsed"):
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Example: Function-calling, dict schema (method="function_calling", include_raw=False):
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.. code-block:: python
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from langchain_openai import ChatOpenAI
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@ -884,11 +884,66 @@ class ChatOpenAI(BaseChatModel):
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# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
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# }
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Example: JSON mode, Pydantic schema (method="json_mode", include_raw=True):
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.. code-block::
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from langchain_openai import ChatOpenAI
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from langchain_core.pydantic_v1 import BaseModel
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class AnswerWithJustification(BaseModel):
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answer: str
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justification: str
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llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
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structured_llm = llm.with_structured_output(
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AnswerWithJustification,
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method="json_mode",
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include_raw=True
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)
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structured_llm.invoke(
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"Answer the following question. "
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"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
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"What's heavier a pound of bricks or a pound of feathers?"
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)
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# -> {
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# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
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# 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
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# 'parsing_error': None
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# }
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Example: JSON mode, no schema (schema=None, method="json_mode", include_raw=True):
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.. code-block::
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from langchain_openai import ChatOpenAI
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structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
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structured_llm.invoke(
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"Answer the following question. "
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"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
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"What's heavier a pound of bricks or a pound of feathers?"
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)
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# -> {
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# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
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# 'parsed': {
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# 'answer': 'They are both the same weight.',
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# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
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# },
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# 'parsing_error': None
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# }
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""" # noqa: E501
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if kwargs:
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raise ValueError(f"Received unsupported arguments {kwargs}")
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is_pydantic_schema = _is_pydantic_class(schema)
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if method == "function_calling":
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if schema is None:
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raise ValueError(
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"schema must be specified when method is 'function_calling'. "
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"Received None."
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)
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llm = self.bind_tools([schema], tool_choice=True)
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if is_pydantic_schema:
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output_parser: OutputParserLike = PydanticToolsParser(
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@ -912,9 +967,7 @@ class ChatOpenAI(BaseChatModel):
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f"'json_format'. Received: '{method}'"
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)
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if return_type == "parsed":
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return llm | output_parser
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elif return_type == "all":
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if include_raw:
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parser_assign = RunnablePassthrough.assign(
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parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
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)
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@ -924,10 +977,7 @@ class ChatOpenAI(BaseChatModel):
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)
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return RunnableMap(raw=llm) | parser_with_fallback
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else:
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raise ValueError(
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f"Unrecognized return_type argument. Expected one of 'parsed' or "
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f"'all'. Received: '{return_type}'"
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)
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return llm | output_parser
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def _is_pydantic_class(obj: Any) -> bool:
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@ -1,6 +1,6 @@
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[tool.poetry]
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name = "langchain-openai"
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version = "0.0.7"
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version = "0.0.8-rc.1"
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description = "An integration package connecting OpenAI and LangChain"
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authors = []
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readme = "README.md"
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