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from typing import Any, List, Optional, Type, Union
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import BaseLLMOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs
from langchain.output_parsers.openai_functions import (
OutputFunctionsParser,
PydanticOutputFunctionsParser,
)
class AnswerWithSources(BaseModel):
"""An answer to the question, with sources."""
answer: str = Field(..., description="Answer to the question that was asked")
sources: List[str] = Field(
..., description="List of sources used to answer the question"
)
def create_qa_with_structure_chain(
llm: BaseLanguageModel,
schema: Union[dict, Type[BaseModel]],
output_parser: str = "base",
prompt: Optional[Union[PromptTemplate, ChatPromptTemplate]] = None,
verbose: bool = False,
) -> LLMChain:
"""Create a question answering chain that returns an answer with sources
based on schema.
Args:
llm: Language model to use for the chain.
schema: Pydantic schema to use for the output.
output_parser: Output parser to use. Should be one of `pydantic` or `base`.
Default to `base`.
prompt: Optional prompt to use for the chain.
Returns:
"""
if output_parser == "pydantic":
if not (isinstance(schema, type) and issubclass(schema, BaseModel)):
raise ValueError(
"Must provide a pydantic class for schema when output_parser is "
"'pydantic'."
)
_output_parser: BaseLLMOutputParser = PydanticOutputFunctionsParser(
pydantic_schema=schema
)
elif output_parser == "base":
_output_parser = OutputFunctionsParser()
else:
raise ValueError(
f"Got unexpected output_parser: {output_parser}. "
f"Should be one of `pydantic` or `base`."
)
if isinstance(schema, type) and issubclass(schema, BaseModel):
schema_dict = schema.schema()
else:
schema_dict = schema
function = {
"name": schema_dict["title"],
"description": schema_dict["description"],
"parameters": schema_dict,
}
llm_kwargs = get_llm_kwargs(function)
messages = [
SystemMessage(
content=(
"You are a world class algorithm to answer "
"questions in a specific format."
)
),
HumanMessage(content="Answer question using the following context"),
HumanMessagePromptTemplate.from_template("{context}"),
HumanMessagePromptTemplate.from_template("Question: {question}"),
HumanMessage(content="Tips: Make sure to answer in the correct format"),
]
prompt = prompt or ChatPromptTemplate(messages=messages)
chain = LLMChain(
llm=llm,
prompt=prompt,
llm_kwargs=llm_kwargs,
output_parser=_output_parser,
verbose=verbose,
)
return chain
def create_qa_with_sources_chain(
llm: BaseLanguageModel, verbose: bool = False, **kwargs: Any
) -> LLMChain:
"""Create a question answering chain that returns an answer with sources.
Args:
llm: Language model to use for the chain.
verbose: Whether to print the details of the chain
**kwargs: Keyword arguments to pass to `create_qa_with_structure_chain`.
Returns:
Chain (LLMChain) that can be used to answer questions with citations.
"""
return create_qa_with_structure_chain(
llm, AnswerWithSources, verbose=verbose, **kwargs
)
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