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@ -9,9 +9,16 @@ from langchain_core.output_parsers import (
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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_core.utils.function_calling import (
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convert_to_openai_function,
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convert_to_openai_tool,
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)
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from langchain.output_parsers import PydanticOutputParser
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from langchain.output_parsers import (
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JsonOutputKeyToolsParser,
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PydanticOutputParser,
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PydanticToolsParser,
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)
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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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@ -26,7 +33,7 @@ def create_openai_fn_runnable(
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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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**llm_kwargs: Any,
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) -> Runnable:
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"""Create a runnable sequence that uses OpenAI functions.
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@ -53,6 +60,7 @@ def create_openai_fn_runnable(
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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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**llm_kwargs: Additional named arguments to pass to the language model.
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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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@ -91,25 +99,27 @@ def create_openai_fn_runnable(
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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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llm_kwargs_: Dict[str, Any] = {"functions": openai_functions, **llm_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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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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if prompt:
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return prompt | llm.bind(**llm_kwargs) | output_parser
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return prompt | llm.bind(**llm_kwargs_) | output_parser
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else:
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return llm.bind(**llm_kwargs) | output_parser
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return 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: Optional[BasePromptTemplate] = None,
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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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enforce_function_usage: bool = True,
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return_single: bool = True,
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mode: Literal[
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"openai-functions", "openai-tools", "openai-json"
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] = "openai-functions",
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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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@ -130,19 +140,107 @@ def create_structured_output_runnable(
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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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then OpenAI function calling is used with the deprecated 'functions',
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'function_call' schema. If 'openai-tools' then OpenAI function
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calling with the latest 'tools', 'tool_choice' schema is used. This is
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recommended over 'openai-functions'. 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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enforce_function_usage: Only applies when mode is 'openai-tools' or
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'openai-functions'. If True, then the model will be forced to use the given
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output schema. If False, then the model can elect whether to use the output
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schema.
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return_single: Only applies when mode is 'openai-tools'. Whether to a list of
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structured outputs or a single one. If True and model does not return any
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structured outputs then chain output is None. If False and model does not
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return any structured outputs then chain output is an empty list.
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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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A runnable sequence that will return a structured output(s) matching the given
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output_schema.
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OpenAI tools example with Pydantic schema (mode='openai-tools'):
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.. code-block:: python
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from typing import Optional
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from langchain.chains import create_structured_output_runnable
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from langchain_openai import ChatOpenAI
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from langchain_core.pydantic_v1 import BaseModel, Field
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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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OpenAI functions example:
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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 an extraction algorithm. Please extract every possible instance"),
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('human', '{input}')
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]
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)
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structured_llm = create_structured_output_runnable(
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RecordDog,
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llm,
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mode="openai-tools",
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enforce_function_usage=True,
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return_single=True
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)
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structured_llm.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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OpenAI tools example with dict schema (mode="openai-tools"):
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.. code-block:: python
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from typing import Optional
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from langchain.chains import create_structured_output_runnable
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from langchain_openai import ChatOpenAI
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dog_schema = {
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"type": "function",
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"function": {
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"name": "record_dog",
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"description": "Record some identifying information about a dog.",
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"parameters": {
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"type": "object",
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"properties": {
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"name": {
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"description": "The dog's name",
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"type": "string"
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},
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"color": {
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"description": "The dog's color",
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"type": "string"
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},
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"fav_food": {
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"description": "The dog's favorite food",
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"type": "string"
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}
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},
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"required": ["name", "color"]
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}
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}
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}
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llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
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structured_llm = create_structured_output_runnable(
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doc_schema,
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llm,
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mode="openai-tools",
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enforce_function_usage=True,
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return_single=True
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)
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structured_llm.invoke("Harry was a chubby brown beagle who loved chicken")
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# -> {'name': 'Harry', 'color': 'brown', 'fav_food': 'chicken'}
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OpenAI functions example (mode="openai-functions"):
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.. code-block:: python
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from typing import Optional
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@ -189,7 +287,7 @@ def create_structured_output_runnable(
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chain = prompt | structured_llm
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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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OpenAI json response format example (mode="openai-json"):
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.. code-block:: python
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from typing import Optional
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@ -219,26 +317,96 @@ def create_structured_output_runnable(
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chain = prompt | structured_llm
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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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# for backwards compatibility
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force_function_usage = kwargs.get(
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"enforce_single_function_usage", enforce_function_usage
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)
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if mode == "openai-tools":
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# Protect against typos in kwargs
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keys_in_kwargs = set(kwargs.keys())
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# Backwards compatibility keys
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unrecognized_keys = keys_in_kwargs - {"enforce_single_function_usage"}
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if unrecognized_keys:
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raise TypeError(
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f"Got an unexpected keyword argument(s): {unrecognized_keys}."
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)
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return _create_openai_tools_runnable(
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output_schema,
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llm,
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prompt=prompt,
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output_parser=output_parser,
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enforce_tool_usage=force_function_usage,
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first_tool_only=return_single,
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)
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elif 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=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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enforce_single_function_usage=force_function_usage,
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**kwargs, # llm-specific kwargs
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)
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elif mode == "openai-json":
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if force_function_usage:
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raise ValueError(
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"enforce_single_function_usage is not supported for mode='openai-json'."
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)
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return _create_openai_json_runnable(
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output_schema, llm, prompt=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"Invalid mode {mode}. Expected one of 'openai-tools', 'openai-functions', "
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f"'openai-json'."
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)
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def _create_openai_tools_runnable(
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tool: Union[Dict[str, Any], Type[BaseModel], Callable],
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llm: Runnable,
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*,
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prompt: Optional[BasePromptTemplate],
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output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]],
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enforce_tool_usage: bool,
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first_tool_only: bool,
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) -> Runnable:
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oai_tool = convert_to_openai_tool(tool)
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llm_kwargs: Dict[str, Any] = {"tools": [oai_tool]}
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if enforce_tool_usage:
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llm_kwargs["tool_choice"] = {
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"type": "function",
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"function": {"name": oai_tool["function"]["name"]},
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}
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output_parser = output_parser or _get_openai_tool_output_parser(
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tool, first_tool_only=first_tool_only
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)
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if prompt:
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return prompt | llm.bind(**llm_kwargs) | output_parser
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else:
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return llm.bind(**llm_kwargs) | output_parser
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def _get_openai_tool_output_parser(
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tool: Union[Dict[str, Any], Type[BaseModel], Callable],
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*,
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first_tool_only: bool = False,
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) -> Union[BaseOutputParser, BaseGenerationOutputParser]:
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if isinstance(tool, type) and issubclass(tool, BaseModel):
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output_parser: Union[
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BaseOutputParser, BaseGenerationOutputParser
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] = PydanticToolsParser(tools=[tool], first_tool_only=first_tool_only)
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else:
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key_name = convert_to_openai_tool(tool)["function"]["name"]
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output_parser = JsonOutputKeyToolsParser(
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first_tool_only=first_tool_only, key_name=key_name
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)
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return output_parser
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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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@ -255,11 +423,10 @@ def get_openai_output_parser(
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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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convert_to_openai_function(fn)["name"]: fn for fn in functions
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}
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else:
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pydantic_schema = functions[0]
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@ -304,7 +471,7 @@ def _create_openai_functions_structured_output_runnable(
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prompt: Optional[BasePromptTemplate] = None,
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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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**llm_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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@ -331,5 +498,5 @@ def _create_openai_functions_structured_output_runnable(
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llm,
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prompt=prompt,
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output_parser=output_parser,
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**kwargs,
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**llm_kwargs,
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)
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|