mirror of https://github.com/hwchase17/langchain
langchain[minor], core[minor]: update json, pydantic parser. add openai-json structured output runnable (#16914)
Bagatur
4 months ago
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GitHub
parent
e22c4d4eb0
commit
852973d616
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from langchain.chains.structured_output.base import (
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create_openai_fn_runnable,
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create_structured_output_runnable,
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)
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__all__ = ["create_structured_output_runnable", "create_openai_fn_runnable"]
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import json
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from typing import Any, Callable, Dict, Literal, Optional, Sequence, Type, Union
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from langchain_core.output_parsers import (
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BaseGenerationOutputParser,
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BaseOutputParser,
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JsonOutputParser,
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)
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from langchain_core.prompts import BasePromptTemplate
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import Runnable
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from langchain_core.utils.function_calling import convert_to_openai_function
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from langchain.output_parsers import PydanticOutputParser
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from langchain.output_parsers.openai_functions import (
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JsonOutputFunctionsParser,
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PydanticAttrOutputFunctionsParser,
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PydanticOutputFunctionsParser,
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)
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def create_openai_fn_runnable(
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functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
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llm: Runnable,
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prompt: BasePromptTemplate,
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*,
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enforce_single_function_usage: bool = True,
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output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
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**kwargs: Any,
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) -> Runnable:
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"""Create a runnable sequence that uses OpenAI functions.
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Args:
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functions: A sequence of either dictionaries, pydantic.BaseModels classes, or
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Python functions. If dictionaries are passed in, they are assumed to
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already be a valid OpenAI functions. If only a single
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function is passed in, then it will be enforced that the model use that
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function. pydantic.BaseModels and Python functions should have docstrings
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describing what the function does. For best results, pydantic.BaseModels
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should have descriptions of the parameters and Python functions should have
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Google Python style args descriptions in the docstring. Additionally,
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Python functions should only use primitive types (str, int, float, bool) or
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pydantic.BaseModels for arguments.
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llm: Language model to use, assumed to support the OpenAI function-calling API.
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prompt: BasePromptTemplate to pass to the model.
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enforce_single_function_usage: only used if a single function is passed in. If
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True, then the model will be forced to use the given function. If False,
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then the model will be given the option to use the given function or not.
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output_parser: BaseLLMOutputParser to use for parsing model outputs. By default
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will be inferred from the function types. If pydantic.BaseModels are passed
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in, then the OutputParser will try to parse outputs using those. Otherwise
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model outputs will simply be parsed as JSON. If multiple functions are
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passed in and they are not pydantic.BaseModels, the chain output will
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include both the name of the function that was returned and the arguments
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to pass to the function.
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Returns:
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A runnable sequence that will pass in the given functions to the model when run.
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Example:
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.. code-block:: python
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from typing import Optional
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from langchain.chains.structured_output import create_openai_fn_runnable
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel, Field
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class RecordPerson(BaseModel):
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'''Record some identifying information about a person.'''
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name: str = Field(..., description="The person's name")
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age: int = Field(..., description="The person's age")
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fav_food: Optional[str] = Field(None, description="The person's favorite food")
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class RecordDog(BaseModel):
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'''Record some identifying information about a dog.'''
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name: str = Field(..., description="The dog's name")
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color: str = Field(..., description="The dog's color")
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fav_food: Optional[str] = Field(None, description="The dog's favorite food")
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llm = ChatOpenAI(model="gpt-4", temperature=0)
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", "You are a world class algorithm for recording entities."),
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("human", "Make calls to the relevant function to record the entities in the following input: {input}"),
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("human", "Tip: Make sure to answer in the correct format"),
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]
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)
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chain = create_openai_fn_runnable([RecordPerson, RecordDog], llm, prompt)
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chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
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# -> RecordDog(name="Harry", color="brown", fav_food="chicken")
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""" # noqa: E501
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if not functions:
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raise ValueError("Need to pass in at least one function. Received zero.")
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openai_functions = [convert_to_openai_function(f) for f in functions]
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llm_kwargs: Dict[str, Any] = {"functions": openai_functions, **kwargs}
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if len(openai_functions) == 1 and enforce_single_function_usage:
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llm_kwargs["function_call"] = {"name": openai_functions[0]["name"]}
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output_parser = output_parser or get_openai_output_parser(functions)
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return prompt | llm.bind(**llm_kwargs) | output_parser
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# TODO: implement mode='openai-tools'.
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def create_structured_output_runnable(
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output_schema: Union[Dict[str, Any], Type[BaseModel]],
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llm: Runnable,
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prompt: BasePromptTemplate,
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*,
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output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
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mode: Literal["openai-functions", "openai-json"] = "openai-functions",
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enforce_single_function_usage: bool = True,
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**kwargs: Any,
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) -> Runnable:
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"""Create a runnable for extracting structured outputs.
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Args:
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output_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary
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is passed in, it's assumed to already be a valid JsonSchema.
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For best results, pydantic.BaseModels should have docstrings describing what
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the schema represents and descriptions for the parameters.
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llm: Language model to use. Assumed to support the OpenAI function-calling API
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if mode is 'openai-function'. Assumed to support OpenAI response_format
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parameter if mode is 'openai-json'.
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prompt: BasePromptTemplate to pass to the model. If mode is 'openai-json' and
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prompt has input variable 'output_schema' then the given output_schema
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will be converted to a JsonSchema and inserted in the prompt.
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output_parser: Output parser to use for parsing model outputs. By default
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will be inferred from the function types. If pydantic.BaseModel is passed
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in, then the OutputParser will try to parse outputs using the pydantic
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class. Otherwise model outputs will be parsed as JSON.
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mode: How structured outputs are extracted from the model. If 'openai-functions'
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then OpenAI function calling is used. If 'openai-json' then OpenAI model
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with response_format set to JSON is used.
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enforce_single_function_usage: Only used if mode is 'openai-functions'. Only
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used if a single function is passed in. If
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True, then the model will be forced to use the given function. If False,
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then the model will be given the option to use the given function or not.
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**kwargs: Additional named arguments.
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Returns:
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A runnable sequence that will return a structured output matching the given
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output_schema.
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OpenAI functions example:
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.. code-block:: python
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from typing import Optional
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from langchain.chains.structured_output import create_structured_output_runnable
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel, Field
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class Dog(BaseModel):
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'''Identifying information about a dog.'''
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name: str = Field(..., description="The dog's name")
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color: str = Field(..., description="The dog's color")
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fav_food: Optional[str] = Field(None, description="The dog's favorite food")
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llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", "You are a world class algorithm for extracting information in structured formats."),
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("human", "Use the given format to extract information from the following input: {input}"),
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("human", "Tip: Make sure to answer in the correct format"),
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]
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)
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chain = create_structured_output_runnable(Dog, llm, prompt, mode="openai-functions")
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chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
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# -> Dog(name="Harry", color="brown", fav_food="chicken")
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OpenAI json response format example:
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.. code-block:: python
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from typing import Optional
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from langchain.chains.structured_output import create_structured_output_runnable
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from langchain_openai import ChatOpenAI
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel, Field
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class Dog(BaseModel):
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'''Identifying information about a dog.'''
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name: str = Field(..., description="The dog's name")
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color: str = Field(..., description="The dog's color")
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fav_food: Optional[str] = Field(None, description="The dog's favorite food")
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llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
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system = '''You are a world class assistant for extracting information in structured JSON formats. \
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Extract a valid JSON blob from the user input that matches the following JSON Schema:
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{output_schema}'''
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", system),
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("human", "{input}"),
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]
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)
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chain = create_structured_output_runnable(Dog, llm, prompt, mode="openai-json")
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chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"})
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""" # noqa: E501
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if mode == "openai-functions":
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return _create_openai_functions_structured_output_runnable(
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output_schema,
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llm,
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prompt,
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output_parser=output_parser,
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enforce_single_function_usage=enforce_single_function_usage,
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**kwargs,
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)
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elif mode == "openai-json":
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return _create_openai_json_runnable(
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output_schema, llm, prompt, output_parser=output_parser, **kwargs
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)
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else:
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raise ValueError(
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f"Invalid mode {mode}. Expected one of 'openai-functions', "
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f"'openai-json'."
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)
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def get_openai_output_parser(
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functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]],
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) -> Union[BaseOutputParser, BaseGenerationOutputParser]:
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"""Get the appropriate function output parser given the user functions.
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Args:
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functions: Sequence where element is a dictionary, a pydantic.BaseModel class,
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or a Python function. If a dictionary is passed in, it is assumed to
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already be a valid OpenAI function.
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Returns:
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A PydanticOutputFunctionsParser if functions are Pydantic classes, otherwise
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a JsonOutputFunctionsParser. If there's only one function and it is
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not a Pydantic class, then the output parser will automatically extract
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only the function arguments and not the function name.
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"""
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function_names = [convert_to_openai_function(f)["name"] for f in functions]
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if isinstance(functions[0], type) and issubclass(functions[0], BaseModel):
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if len(functions) > 1:
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pydantic_schema: Union[Dict, Type[BaseModel]] = {
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name: fn for name, fn in zip(function_names, functions)
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}
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else:
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pydantic_schema = functions[0]
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output_parser: Union[
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BaseOutputParser, BaseGenerationOutputParser
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] = PydanticOutputFunctionsParser(pydantic_schema=pydantic_schema)
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else:
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output_parser = JsonOutputFunctionsParser(args_only=len(functions) <= 1)
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return output_parser
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def _create_openai_json_runnable(
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output_schema: Union[Dict[str, Any], Type[BaseModel]],
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llm: Runnable,
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prompt: BasePromptTemplate,
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*,
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output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
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) -> Runnable:
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""""""
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if isinstance(output_schema, type) and issubclass(output_schema, BaseModel):
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output_parser = output_parser or PydanticOutputParser(
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pydantic_object=output_schema,
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)
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schema_as_dict = convert_to_openai_function(output_schema)["parameters"]
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else:
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output_parser = output_parser or JsonOutputParser()
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schema_as_dict = output_schema
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if "output_schema" in prompt.input_variables:
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prompt = prompt.partial(output_schema=json.dumps(schema_as_dict, indent=2))
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llm = llm.bind(response_format={"type": "json_object"})
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return prompt | llm | output_parser
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def _create_openai_functions_structured_output_runnable(
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output_schema: Union[Dict[str, Any], Type[BaseModel]],
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llm: Runnable,
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prompt: BasePromptTemplate,
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*,
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output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None,
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**kwargs: Any,
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) -> Runnable:
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if isinstance(output_schema, dict):
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function: Any = {
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"name": "output_formatter",
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"description": (
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"Output formatter. Should always be used to format your response to the"
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" user."
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),
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"parameters": output_schema,
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}
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else:
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class _OutputFormatter(BaseModel):
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"""Output formatter. Should always be used to format your response to the user.""" # noqa: E501
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output: output_schema # type: ignore
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function = _OutputFormatter
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output_parser = output_parser or PydanticAttrOutputFunctionsParser(
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pydantic_schema=_OutputFormatter, attr_name="output"
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)
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return create_openai_fn_runnable(
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[function],
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llm,
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prompt,
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output_parser=output_parser,
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**kwargs,
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)
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