Source code for langchain.retrievers.re_phraser
import logging
from typing import List
from langchain.callbacks.manager import (
    AsyncCallbackManagerForRetrieverRun,
    CallbackManagerForRetrieverRun,
)
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseRetriever, Document
logger = logging.getLogger(__name__)
# Default template
DEFAULT_TEMPLATE = """You are an assistant tasked with taking a natural language \
query from a user and converting it into a query for a vectorstore. \
In this process, you strip out information that is not relevant for \
the retrieval task. Here is the user query: {question}"""
# Default prompt
DEFAULT_QUERY_PROMPT = PromptTemplate.from_template(DEFAULT_TEMPLATE)
[docs]class RePhraseQueryRetriever(BaseRetriever):
    """Given a query, use an LLM to re-phrase it.
    Then, retrieve docs for the re-phrased query."""
    retriever: BaseRetriever
    llm_chain: LLMChain
[docs]    @classmethod
    def from_llm(
        cls,
        retriever: BaseRetriever,
        llm: BaseLLM,
        prompt: PromptTemplate = DEFAULT_QUERY_PROMPT,
    ) -> "RePhraseQueryRetriever":
        """Initialize from llm using default template.
        The prompt used here expects a single input: `question`
        Args:
            retriever: retriever to query documents from
            llm: llm for query generation using DEFAULT_QUERY_PROMPT
            prompt: prompt template for query generation
        Returns:
            RePhraseQueryRetriever
        """