tj2RddlmZddlmZddlmZddlmZmZddl m Z m Z ddl m Z ddlmZddlmZdd lmZmZmZd ed efd Zd Zedddd ddedede dzdeedzded ef dZedddd ddedede dzded ef dZdS))Any) deprecated)BaseLanguageModel)JsonKeyOutputFunctionsParser!PydanticAttrOutputFunctionsParser)BasePromptTemplateChatPromptTemplate) BaseModel)Chain)LLMChain)_convert_schema_resolve_schema_referencesget_llm_kwargs entity_schemareturnc<dddddt|didgddS) Ninformation_extractionz3Extracts the relevant information from the passage.objectinfoarray)typeitems)r propertiesrequired)name description parameters)r )rs r/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_classic/chains/openai_functions/extraction.py_get_extraction_functionrsE(L?=3Q3QRR       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} z0.1.14z2.0.0z%ChatModel.with_structured_output(...)zwAvailable on chat models capable of tool calling. See https://docs.langchain.com/oss/python/langchain/structured-output)sinceremoval alternativeaddendumNFschemallmprompttagsverbosect|}|ptjt}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/so:(//HX"4"BCW"X"X0&AAAM))J   #     r pydantic_schemacGfddt}tdr}n}t ||di}t |}|ptjt}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(eZdZUeed<dS)8create_extraction_chain_pydantic..PydanticSchemarN)__name__ __module__ __qualname__list__annotations__)r3srPydanticSchemar6vs$?######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_pydanticrCZs 8$$$$$$$$$$ 3441'99;; '..00 .-,,M ( 66HX"4"BCW"X"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 rrQs7******<<<<<<JIIIIIII//////111111 D T        7 L )-!     % s)d      D   7 L )- --- -  %- -  ------r