mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
3dc0f3c371
Changed import of `PromptTemplate` from `langchain` to `langchain_core` in `langchain_experimental`
65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
from typing import Any, Dict, Optional, Type, Union
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from langchain.chains.openai_functions import create_structured_output_chain
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from langchain.pydantic_v1 import BaseModel
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from langchain.schema import BaseLLMOutputParser, BasePromptTemplate
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from langchain_community.chat_models import ChatOpenAI
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from langchain_core.prompts import PromptTemplate
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from langchain_experimental.tabular_synthetic_data.base import SyntheticDataGenerator
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OPENAI_TEMPLATE = PromptTemplate(input_variables=["example"], template="{example}")
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def create_openai_data_generator(
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output_schema: Union[Dict[str, Any], Type[BaseModel]],
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llm: ChatOpenAI,
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prompt: BasePromptTemplate,
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output_parser: Optional[BaseLLMOutputParser] = None,
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**kwargs: Any,
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) -> SyntheticDataGenerator:
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"""
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Create an instance of SyntheticDataGenerator tailored for OpenAI models.
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This function creates an LLM chain designed for structured output based on the
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provided schema, language model, and prompt template. The resulting chain is then
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used to instantiate and return a SyntheticDataGenerator.
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Args:
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output_schema (Union[Dict[str, Any], Type[BaseModel]]): Schema for expected
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output. This can be either a dictionary representing a valid JsonSchema or a
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Pydantic BaseModel class.
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llm (ChatOpenAI): OpenAI language model to use.
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prompt (BasePromptTemplate): Template to be used for generating prompts.
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output_parser (Optional[BaseLLMOutputParser], optional): Parser for
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processing model outputs. If none is provided, a default will be inferred
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from the function types.
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**kwargs: Additional keyword arguments to be passed to
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`create_structured_output_chain`.
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Returns: SyntheticDataGenerator: An instance of the data generator set up with
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the constructed chain.
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Usage:
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To generate synthetic data with a structured output, first define your desired
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output schema. Then, use this function to create a SyntheticDataGenerator
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instance. After obtaining the generator, you can utilize its methods to produce
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the desired synthetic data.
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"""
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# Create function calling chain to ensure structured output
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chain = create_structured_output_chain(
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output_schema, llm, prompt, output_parser=output_parser, **kwargs
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)
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# Create the SyntheticDataGenerator instance with the created chain
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generator = SyntheticDataGenerator(template=prompt, llm_chain=chain)
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return generator
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