cc1e75b3c6
* add rerankers in retrieving pipeline * update example MVP pipeline * add citation pipeline and function call interface * change return type of QA and AgentPipeline to Document
111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
from typing import Iterator, List, Union
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from langchain.schema.messages import HumanMessage, SystemMessage
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from pydantic import BaseModel, Field
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from kotaemon.base import BaseComponent
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from ..llms.chats.base import ChatLLM
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from ..llms.completions.base import LLM
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BaseLLM = Union[ChatLLM, LLM]
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class FactWithEvidence(BaseModel):
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"""Class representing a single statement.
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Each fact has a body and a list of sources.
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If there are multiple facts make sure to break them apart
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such that each one only uses a set of sources that are relevant to it.
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"""
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fact: str = Field(..., description="Body of the sentence, as part of a response")
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substring_quote: List[str] = Field(
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...,
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description=(
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"Each source should be a direct quote from the context, "
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"as a substring of the original content"
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),
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)
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def _get_span(self, quote: str, context: str, errs: int = 100) -> Iterator[str]:
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import regex
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minor = quote
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major = context
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errs_ = 0
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s = regex.search(f"({minor}){{e<={errs_}}}", major)
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while s is None and errs_ <= errs:
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errs_ += 1
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s = regex.search(f"({minor}){{e<={errs_}}}", major)
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if s is not None:
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yield from s.spans()
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def get_spans(self, context: str) -> Iterator[str]:
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for quote in self.substring_quote:
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yield from self._get_span(quote, context)
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class QuestionAnswer(BaseModel):
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"""A question and its answer as a list of facts each one should have a source.
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each sentence contains a body and a list of sources."""
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question: str = Field(..., description="Question that was asked")
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answer: List[FactWithEvidence] = Field(
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...,
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description=(
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"Body of the answer, each fact should be "
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"its separate object with a body and a list of sources"
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),
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)
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class CitationPipeline(BaseComponent):
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"""Citation pipeline to extract cited evidences from source
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(based on input question)"""
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llm: BaseLLM
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def run(
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self,
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context: str,
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question: str,
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) -> QuestionAnswer:
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schema = QuestionAnswer.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 = {
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"functions": [function],
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"function_call": {"name": function["name"]},
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}
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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 with correct and exact citations."
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)
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),
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HumanMessage(content="Answer question using the following context"),
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HumanMessage(content=context),
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HumanMessage(content=f"Question: {question}"),
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HumanMessage(
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content=(
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"Tips: Make sure to cite your sources, "
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"and use the exact words from the context."
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)
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),
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]
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llm_output = self.llm(messages, **llm_kwargs)
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function_output = llm_output.messages[0].additional_kwargs["function_call"][
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"arguments"
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]
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output = QuestionAnswer.parse_raw(function_output)
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return output
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