3fi2ddlmZddlmZddlmZddlmZmZddl m Z m Z ddl m Z ddlmZddlmZdd lmZmZmZd ed efd Zd Zedddd ddedede dzdeedzded ef dZedddd ddedede dzded ef dZy))Any) deprecated)BaseLanguageModel)JsonKeyOutputFunctionsParser!PydanticAttrOutputFunctionsParser)BasePromptTemplateChatPromptTemplate) BaseModel)Chain)LLMChain)_convert_schema_resolve_schema_referencesget_llm_kwargs entity_schemareturnc4dddddt|didgddS) Ninformation_extractionz3Extracts the relevant information from the passage.objectinfoarray)typeitems)r propertiesrequired)name description parameters)r )rs r/var/www/auto_recruiter/arenv/lib/python3.12/site-packages/langchain_classic/chains/openai_functions/extraction.py_get_extraction_functionrs5(L?=3QR     a<Extract and save the relevant entities mentioned in the following passage together with their properties. Only extract the properties mentioned in the 'information_extraction' function. If a property is not present and is not required in the function parameters, do not include it in the output. Passage: {input} z0.1.14zLangChain has introduced a method called `with_structured_output` thatis available on ChatModels capable of tool calling.You can read more about the method here: .z1.0a, from pydantic import BaseModel, Field from langchain_anthropic import ChatAnthropic class Joke(BaseModel): setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") # Or any other chat model that supports tools. # Please reference to the documentation of structured_output # to see an up to date list of which models support # with_structured_output. model = ChatAnthropic(model="claude-opus-4-1-20250805", temperature=0) structured_model = model.with_structured_output(Joke) structured_model.invoke("Tell me a joke about cats. Make sure to call the Joke function.") )sincemessageremoval alternativeNschemallmprompttagsverbosect|}|xstjt}t d}t |}t ||||||S)aCreates a chain that extracts information from a passage. Args: schema: The schema of the entities to extract. llm: The language model to use. prompt: The prompt to use for extraction. tags: Optional list of tags to associate with the chain. verbose: Whether to run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Returns: Chain that can be used to extract information from a passage. r)key_name)r&r' llm_kwargs output_parserr(r))rr from_template_EXTRACTION_TEMPLATErrr ) r%r&r'r(r)functionextraction_promptr-r,s rcreate_extraction_chainr2/sZb(/HX"4"B"BCW"X0&AM)J   #   r aLangChain has introduced a method called `with_structured_output` thatis available on ChatModels capable of tool calling.You can read more about the method here: . Please follow our extraction use case documentation for more guidelineson how to do information extraction with LLMs.. If you notice other issues, please provide feedback here:pydantic_schemacXGfddt}tdrj}nj}t ||j di}t |}|xstjt}t|d}t|} t||| ||S)aCreates a chain that extracts information from a passage using Pydantic schema. Args: pydantic_schema: The Pydantic schema of the entities to extract. llm: The language model to use. prompt: The prompt to use for extraction. verbose: Whether to run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Returns: Chain that can be used to extract information from a passage. c"eZdZUeWed<y)8create_extraction_chain_pydantic..PydanticSchemarN)__name__ __module__ __qualname__list__annotations__)r3srPydanticSchemar6s?##r r<model_json_schema definitionsr)r3 attr_name)r&r'r,r-r)) r hasattrr=r%rgetrr r.r/rrr ) r3r&r'r)r< openai_schemar0r1r-r,s ` r create_extraction_chain_pydanticrCnsl$$ 34'99; '..0 .-,M ( 6HX"4"B"BCW"X5&M )J   #  r )NNF)NF)typingrlangchain_core._apirlangchain_core.language_modelsr.langchain_core.output_parsers.openai_functionsrrlangchain_core.promptsrr pydanticr langchain_classic.chains.baser langchain_classic.chains.llmr /langchain_classic.chains.openai_functions.utilsr rrdictrr/r:strboolr2rCr rrQs0*<J/1 D T    W  @)-!     % s)d      ;:D  H  !"L)- -- -  %- -  -G"F-r