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71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
from typing import Any, List
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from pydantic import BaseModel, Field
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from langchain.base_language import BaseLanguageModel
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from langchain.chains.llm import LLMChain
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from langchain.chains.openai_functions.utils import get_llm_kwargs
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from langchain.output_parsers.openai_functions import (
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OutputFunctionsParser,
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PydanticOutputFunctionsParser,
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)
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from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
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from langchain.schema import BaseLLMOutputParser, HumanMessage, SystemMessage
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class AnswerWithSources(BaseModel):
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"""An answer to the question being asked, with sources."""
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answer: str = Field(..., description="Answer to the question that was asked")
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sources: List[str] = Field(
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..., description="List of sources used to answer the question"
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)
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def create_qa_with_structure_chain(
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llm: BaseLanguageModel, schema: Any, output_parser: str = "base"
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) -> LLMChain:
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if output_parser == "pydantic":
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_output_parser: BaseLLMOutputParser = PydanticOutputFunctionsParser(
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pydantic_schema=schema
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)
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elif output_parser == "base":
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_output_parser = OutputFunctionsParser()
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else:
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raise ValueError(
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f"Got unexpected output_parser: {output_parser}. "
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f"Should be one of `pydantic` or `base`."
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)
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schema = AnswerWithSources.schema()
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function = {
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"name": schema["title"],
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"description": schema["description"],
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"parameters": schema,
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}
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llm_kwargs = get_llm_kwargs(function)
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messages = [
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SystemMessage(
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content=(
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"You are a world class algorithm to answer "
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"questions in a specific format."
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)
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),
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HumanMessage(content="Answer question using the following context"),
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HumanMessagePromptTemplate.from_template("{context}"),
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HumanMessagePromptTemplate.from_template("Question: {question}"),
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HumanMessage(content="Tips: Make sure to answer in the correct format"),
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]
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prompt = ChatPromptTemplate(messages=messages)
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chain = LLMChain(
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llm=llm,
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prompt=prompt,
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llm_kwargs=llm_kwargs,
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output_parser=_output_parser,
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)
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return chain
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def create_qa_with_sources_chain(llm: BaseLanguageModel, **kwargs: Any) -> LLMChain:
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return create_qa_with_structure_chain(llm, AnswerWithSources, **kwargs)
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