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Routing

有时我们可能有不同领域的多个索引，针对不同问题我们想要查询这些索引的不同子集。例如，假设我们有一个针对所有LangChain Python文档的向量存储索引，以及一个针对所有LangChain js文档的索引。对于有关LangChain使用的问题，我们希望推断问题所涉及的语言，并查询相应的文档。 查询路由是对查询应在哪个索引或索引子集上执行的分类过程。

Setup ​

Install dependencies ​ 

%pip install -qU langchain-core langchain-openai

Set environment variables ​

我们将在这个示例中使用 OpenAI。 

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()

Routing with function calling models ​

使用函数调用模型很容易将模型用于分类，这就是路由归结为的内容。 

from typing import Literal

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI


class RouteQuery(BaseModel):
    """Route a user query to the most relevant datasource."""

    datasource: Literal["python_docs", "js_docs", "golang_docs"] = Field(
        ...,
        description="Given a user question choose which datasource would be most relevant for answering their question",
    )


llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm = llm.with_structured_output(RouteQuery)

system = """You are an expert at routing a user question to the appropriate data source.

Based on the programming language the question is referring to, route it to the relevant data source."""
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system),
        ("human", "{question}"),
    ]
)

router = prompt | structured_llm
/Users/bagatur/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.
  warn_beta(
question = """Why doesn't the following code work:

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(["human", "speak in {language}"])
prompt.invoke("french")
"""
router.invoke({"question": question})
RouteQuery(datasource='python_docs')
question = """Why doesn't the following code work:


import { ChatPromptTemplate } from "@langchain/core/prompts";


const chatPrompt = ChatPromptTemplate.fromMessages([
  ["human", "speak in {language}"],
]);

const formattedChatPrompt = await chatPrompt.invoke({
  input_language: "french"
});
"""
router.invoke({"question": question})
RouteQuery(datasource='js_docs')

Routing to multiple indexes ​

如果我们想要查询多个索引，我们也可以这样做，通过更新我们的模式来接受一个数据源列表： 

from typing import List


class RouteQuery(BaseModel):
    """Route a user query to the most relevant datasource."""

    datasources: List[Literal["python_docs", "js_docs", "golang_docs"]] = Field(
        ...,
        description="Given a user question choose which datasources would be most relevant for answering their question",
    )


llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm = llm.with_structured_output(RouteQuery)
router = prompt | structured_llm
router.invoke(
    {
        "question": "is there feature parity between the Python and JS implementations of OpenAI chat models"
    }
)
RouteQuery(datasources=['python_docs', 'js_docs'])

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