a dg @sddlmZmZmZmZmZddlmZddlm Z m Z ddl m Z ddl mZddlmZddlmZedgd d Zdeeeefeefe e ee eed d dZd S))AnyDictOptionalTypeUnion)create_structured_output_chain)BaseLLMOutputParserBasePromptTemplate) ChatOpenAI)PromptTemplate) BaseModel)SyntheticDataGeneratorZexamplez {example})Zinput_variablestemplateN) output_schemallmprompt output_parserkwargsreturncKs(t|||fd|i|}t||d}|S)af Create an instance of SyntheticDataGenerator tailored for OpenAI models. This function creates an LLM chain designed for structured output based on the provided schema, language model, and prompt template. The resulting chain is then used to instantiate and return a SyntheticDataGenerator. Args: output_schema (Union[Dict[str, Any], Type[BaseModel]]): Schema for expected output. This can be either a dictionary representing a valid JsonSchema or a Pydantic BaseModel class. llm (ChatOpenAI): OpenAI language model to use. prompt (BasePromptTemplate): Template to be used for generating prompts. output_parser (Optional[BaseLLMOutputParser], optional): Parser for processing model outputs. If none is provided, a default will be inferred from the function types. kwargs: Additional keyword arguments to be passed to `create_structured_output_chain`. Returns: SyntheticDataGenerator: An instance of the data generator set up with the constructed chain. Usage: To generate synthetic data with a structured output, first define your desired output schema. Then, use this function to create a SyntheticDataGenerator instance. After obtaining the generator, you can utilize its methods to produce the desired synthetic data. r)rZ llm_chain)rr )rrrrrchain generatorr/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain_experimental/tabular_synthetic_data/openai.pycreate_openai_data_generators, r)N)typingrrrrrZ!langchain.chains.openai_functionsrZlangchain.schemarr Zlangchain_community.chat_modelsr Zlangchain_core.promptsr Zpydanticr Z2langchain_experimental.tabular_synthetic_data.baser ZOPENAI_TEMPLATEstrrrrrrs