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*   Techniques
*   Routing

On this page

Routing
=======

Sometimes we have multiple indexes for different domains, and for different questions we want to query different subsets of these indexes. For example, suppose we had one vector store index for all of the LangChain python documentation and one for all of the LangChain js documentation. Given a question about LangChain usage, we’d want to infer which language the the question was referring to and query the appropriate docs. **Query routing** is the process of classifying which index or subset of indexes a query should be performed on.

Setup[​](#setup "Direct link to Setup")
---------------------------------------

#### Install dependencies[​](#install-dependencies "Direct link to Install dependencies")

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   yarn
*   pnpm

    npm i @langchain/core zod

    yarn add @langchain/core zod

    pnpm add @langchain/core zod

#### Set environment variables[​](#set-environment-variables "Direct link to Set environment variables")

    # Optional, use LangSmith for best-in-class observabilityLANGSMITH_API_KEY=your-api-keyLANGCHAIN_TRACING_V2=true

Routing with function calling models[​](#routing-with-function-calling-models "Direct link to Routing with function calling models")
------------------------------------------------------------------------------------------------------------------------------------

With function-calling models it’s simple to use models for classification, which is what routing comes down to:

### Pick your chat model:

*   OpenAI
*   Anthropic
*   FireworksAI
*   MistralAI

#### Install dependencies

tip

See [this section for general instructions on installing integration packages](/docs/get_started/installation#installing-integration-packages).

*   npm
*   yarn
*   pnpm

    npm i @langchain/openai 

    yarn add @langchain/openai 

    pnpm add @langchain/openai 

#### Add environment variables

    OPENAI_API_KEY=your-api-key

#### Instantiate the model

    import { ChatOpenAI } from "@langchain/openai";const llm = new ChatOpenAI({  model: "gpt-3.5-turbo-0125",  temperature: 0});

#### Install dependencies

tip

See [this section for general instructions on installing integration packages](/docs/get_started/installation#installing-integration-packages).

*   npm
*   yarn
*   pnpm

    npm i @langchain/anthropic 

    yarn add @langchain/anthropic 

    pnpm add @langchain/anthropic 

#### Add environment variables

    ANTHROPIC_API_KEY=your-api-key

#### Instantiate the model

    import { ChatAnthropic } from "@langchain/anthropic";const llm = new ChatAnthropic({  model: "claude-3-sonnet-20240229",  temperature: 0});

#### Install dependencies

tip

See [this section for general instructions on installing integration packages](/docs/get_started/installation#installing-integration-packages).

*   npm
*   yarn
*   pnpm

    npm i @langchain/community 

    yarn add @langchain/community 

    pnpm add @langchain/community 

#### Add environment variables

    FIREWORKS_API_KEY=your-api-key

#### Instantiate the model

    import { ChatFireworks } from "@langchain/community/chat_models/fireworks";const llm = new ChatFireworks({  model: "accounts/fireworks/models/firefunction-v1",  temperature: 0});

#### Install dependencies

tip

See [this section for general instructions on installing integration packages](/docs/get_started/installation#installing-integration-packages).

*   npm
*   yarn
*   pnpm

    npm i @langchain/mistralai 

    yarn add @langchain/mistralai 

    pnpm add @langchain/mistralai 

#### Add environment variables

    MISTRAL_API_KEY=your-api-key

#### Instantiate the model

    import { ChatMistralAI } from "@langchain/mistralai";const llm = new ChatMistralAI({  model: "mistral-large-latest",  temperature: 0});

    import { ChatPromptTemplate } from "@langchain/core/prompts";import { z } from "zod";const routeQuerySchema = z.object({  datasource: z    .union([      z.literal("python_docs"),      z.literal("js_docs"),      z.literal("golang_docs"),    ])    .describe(      "Given a user question choose which datasource would be most relevant for answering their question"    ),});const 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.`;const prompt = ChatPromptTemplate.fromMessages([  ["system", system],  ["human", "{question}"],]);const llmWithTools = llm.withStructuredOutput(routeQuerySchema, {  name: "RouteQuery",});const router = prompt.pipe(llmWithTools);

    const question = `Why doesn't the following code work:from langchain_core.prompts import ChatPromptTemplateprompt = ChatPromptTemplate.from_messages(["human", "speak in {language}"])prompt.invoke("french")`;await router.invoke({ question: question });

    { datasource: "python_docs" }

    const 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"});`;await router.invoke({ question: question });

    { datasource: "js_docs" }

Routing to multiple indexes[​](#routing-to-multiple-indexes "Direct link to Routing to multiple indexes")
---------------------------------------------------------------------------------------------------------

If we may want to query multiple indexes we can do that, too, by updating our schema to accept a List of data sources:

### Pick your chat model:

*   OpenAI
*   Anthropic
*   FireworksAI
*   MistralAI

#### Install dependencies

tip

See [this section for general instructions on installing integration packages](/docs/get_started/installation#installing-integration-packages).

*   npm
*   yarn
*   pnpm

    npm i @langchain/openai 

    yarn add @langchain/openai 

    pnpm add @langchain/openai 

#### Add environment variables

    OPENAI_API_KEY=your-api-key

#### Instantiate the model

    import { ChatOpenAI } from "@langchain/openai";const llm = new ChatOpenAI({  model: "gpt-3.5-turbo-0125",  temperature: 0});

#### Install dependencies

tip

See [this section for general instructions on installing integration packages](/docs/get_started/installation#installing-integration-packages).

*   npm
*   yarn
*   pnpm

    npm i @langchain/anthropic 

    yarn add @langchain/anthropic 

    pnpm add @langchain/anthropic 

#### Add environment variables

    ANTHROPIC_API_KEY=your-api-key

#### Instantiate the model

    import { ChatAnthropic } from "@langchain/anthropic";const llm = new ChatAnthropic({  model: "claude-3-sonnet-20240229",  temperature: 0});

#### Install dependencies

tip

See [this section for general instructions on installing integration packages](/docs/get_started/installation#installing-integration-packages).

*   npm
*   yarn
*   pnpm

    npm i @langchain/community 

    yarn add @langchain/community 

    pnpm add @langchain/community 

#### Add environment variables

    FIREWORKS_API_KEY=your-api-key

#### Instantiate the model

    import { ChatFireworks } from "@langchain/community/chat_models/fireworks";const llm = new ChatFireworks({  model: "accounts/fireworks/models/firefunction-v1",  temperature: 0});

#### Install dependencies

tip

See [this section for general instructions on installing integration packages](/docs/get_started/installation#installing-integration-packages).

*   npm
*   yarn
*   pnpm

    npm i @langchain/mistralai 

    yarn add @langchain/mistralai 

    pnpm add @langchain/mistralai 

#### Add environment variables

    MISTRAL_API_KEY=your-api-key

#### Instantiate the model

    import { ChatMistralAI } from "@langchain/mistralai";const llm = new ChatMistralAI({  model: "mistral-large-latest",  temperature: 0});

    import { z } from "zod";const routeQuerySchema = z  .object({    datasources: z      .array(        z.union([          z.literal("python_docs"),          z.literal("js_docs"),          z.literal("golang_docs"),        ])      )      .describe(        "Given a user question choose which datasources would be most relevant for answering their question"      ),  })  .describe("Route a user query to the most relevant datasource.");const llmWithTools = llm.withStructuredOutput(routeQuerySchema, {  name: "RouteQuery",});const router = prompt.pipe(llmWithTools);await router.invoke({  question:    "is there feature parity between the Python and JS implementations of OpenAI chat models",});

    { datasources: [ "python_docs", "js_docs" ] }

* * *

#### Help us out by providing feedback on this documentation page:

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Hypothetical Document Embeddings

](/v0.1/docs/use_cases/query_analysis/techniques/hyde/)[

Next

Step Back Prompting

](/v0.1/docs/use_cases/query_analysis/techniques/step_back/)

*   [Setup](#setup)
*   [Routing with function calling models](#routing-with-function-calling-models)
*   [Routing to multiple indexes](#routing-to-multiple-indexes)

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