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Expansion | Документация для разработчиков

Expansion

Обновлено 6 марта 2024

Information retrieval systems can be sensitive to phrasing and specific keywords. To mitigate this, one classic retrieval technique is to generate multiple paraphrased versions of a query and return results for all versions of the query. This is called query expansion. LLMs are a great tool for generating these alternate versions of a query.

Let's take a look at how we might do query expansion for our Q&A bot over the LangChain YouTube videos, which we started in the Quickstart.

Setup

Install dependencies

# %pip install -qU langchain langchain-openai

Set environment variables

We'll use OpenAI in this example:

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

Query generation

To make sure we get multiple paraphrasings we'll use OpenAI's function-calling API.

from langchain_core.pydantic_v1 import BaseModel, Field


class ParaphrasedQuery(BaseModel):
"""You have performed query expansion to generate a paraphrasing of a question."""

paraphrased_query: str = Field(
...,
description="A unique paraphrasing of the original question.",
)
from langchain.output_parsers import PydanticToolsParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

system = """You are an expert at converting user questions into database queries. \
You have access to a database of tutorial videos about a software library for building LLM-powered applications. \

Perform query expansion. If there are multiple common ways of phrasing a user question \
or common synonyms for key words in the question, make sure to return multiple versions \
of the query with the different phrasings.

If there are acronyms or words you are not familiar with, do not try to rephrase them.

Return at least 3 versions of the question."""
prompt = ChatPromptTemplate.from_messages(
[
("system", system),
("human", "{question}"),
]
)
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
llm_with_tools = llm.bind_tools([ParaphrasedQuery])
query_analyzer = prompt | llm_with_tools | PydanticToolsParser(tools=[ParaphrasedQuery])

Let's see what queries our analyzer generates for the questions we searched earlier:

query_analyzer.invoke(
{
"question": "how to use multi-modal models in a chain and turn chain into a rest api"
}
)
    [ParaphrasedQuery(paraphrased_query='How to utilize multi-modal models sequentially and convert the sequence into a REST API'),
ParaphrasedQuery(paraphrased_query='Steps for using multi-modal models in a series and transforming the series into a RESTful API'),
ParaphrasedQuery(paraphrased_query='Guide on employing multi-modal models in a chain and converting the chain into a RESTful API')]
query_analyzer.invoke({"question": "stream events from llm agent"})
    [ParaphrasedQuery(paraphrased_query='How to stream events from LLM agent?'),
ParaphrasedQuery(paraphrased_query='How can I receive events from LLM agent in real-time?'),
ParaphrasedQuery(paraphrased_query='What is the process for capturing events from LLM agent?')]
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