如何處理在查詢分析時的多個檢索器

有時，查詢分析技術可能允許選擇要使用的檢索器。 若要使用此功能，您需要新增一些邏輯來選擇要執行的檢索器。 我們將展示一個簡單的範例（使用模擬資料）來說明如何做到這一點。

設定

安裝依賴項

%pip install -qU langchain langchain-community langchain-openai langchain-chroma

Note: you may need to restart the kernel to use updated packages.

設定環境變數

在此範例中，我們將使用 OpenAI

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass()

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

建立索引

我們將在虛假資訊上建立向量儲存庫。

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

texts = ["Harrison worked at Kensho"]
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_texts(texts, embeddings, collection_name="harrison")
retriever_harrison = vectorstore.as_retriever(search_kwargs={"k": 1})

texts = ["Ankush worked at Facebook"]
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_texts(texts, embeddings, collection_name="ankush")
retriever_ankush = vectorstore.as_retriever(search_kwargs={"k": 1})

API 參考：OpenAIEmbeddings | RecursiveCharacterTextSplitter

查詢分析

我們將使用函式呼叫來組織輸出。 我們將使其返回多個查詢。

from typing import List, Optional

from pydantic import BaseModel, Field


class Search(BaseModel):
    """Search for information about a person."""

    query: str = Field(
        ...,
        description="Query to look up",
    )
    person: str = Field(
        ...,
        description="Person to look things up for. Should be `HARRISON` or `ANKUSH`.",
    )

from langchain_core.output_parsers.openai_tools import PydanticToolsParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI

output_parser = PydanticToolsParser(tools=[Search])

system = """You have the ability to issue search queries to get information to help answer user information."""
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "{question}"),
    ]
)
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
structured_llm = llm.with_structured_output(Search)
query_analyzer = {"question": RunnablePassthrough()} | prompt | structured_llm

API 參考：PydanticToolsParser | ChatPromptTemplate | RunnablePassthrough | ChatOpenAI

我們可以看到這允許在檢索器之間進行路由

query_analyzer.invoke("where did Harrison Work")

Search(query='work history', person='HARRISON')

query_analyzer.invoke("where did ankush Work")

Search(query='work history', person='ANKUSH')

使用查詢分析進行檢索

那麼我們該如何將其包含在鏈中呢？ 我們只需要一些簡單的邏輯來選擇檢索器並傳遞搜尋查詢

from langchain_core.runnables import chain

API 參考：chain

retrievers = {
    "HARRISON": retriever_harrison,
    "ANKUSH": retriever_ankush,
}

@chain
def custom_chain(question):
    response = query_analyzer.invoke(question)
    retriever = retrievers[response.person]
    return retriever.invoke(response.query)

custom_chain.invoke("where did Harrison Work")

[Document(page_content='Harrison worked at Kensho')]

custom_chain.invoke("where did ankush Work")

[Document(page_content='Ankush worked at Facebook')]