Source code for langchain_experimental.tabular_synthetic_data.openai

from typing import Any, Dict, Optional, Type, Union

from langchain.chains.openai_functions import create_structured_output_chain
from langchain.pydantic_v1 import BaseModel
from langchain.schema import BaseLLMOutputParser, BasePromptTemplate
from langchain_community.chat_models import ChatOpenAI
from langchain_core.prompts import PromptTemplate

from langchain_experimental.tabular_synthetic_data.base import SyntheticDataGenerator

OPENAI_TEMPLATE = PromptTemplate(input_variables=["example"], template="{example}")

[docs]def create_openai_data_generator( output_schema: Union[Dict[str, Any], Type[BaseModel]], llm: ChatOpenAI, prompt: BasePromptTemplate, output_parser: Optional[BaseLLMOutputParser] = None, **kwargs: Any, ) -> SyntheticDataGenerator: """ 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. """ # Create function calling chain to ensure structured output chain = create_structured_output_chain( output_schema, llm, prompt, output_parser=output_parser, **kwargs ) # Create the SyntheticDataGenerator instance with the created chain generator = SyntheticDataGenerator(template=prompt, llm_chain=chain) return generator