!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()||function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}())

[Skip to main content](#__docusaurus_skipToContent_fallback)

LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/).

[

![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png)

](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com)

[More](#)

*   [People](/v0.1/docs/people/)
*   [Community](/v0.1/docs/community/)
*   [Tutorials](/v0.1/docs/additional_resources/tutorials/)
*   [Contributing](/v0.1/docs/contributing/)

[v0.1](#)

*   [v0.2](https://js.langchain.com/v0.2/docs/introduction)
*   [v0.1](/v0.1/docs/get_started/introduction/)

[🦜🔗](#)

*   [LangSmith](https://smith.langchain.com)
*   [LangSmith Docs](https://docs.smith.langchain.com)
*   [LangChain Hub](https://smith.langchain.com/hub)
*   [LangServe](https://github.com/langchain-ai/langserve)
*   [Python Docs](https://python.langchain.com/)

[Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs)

Search

*   [Use cases](/v0.1/docs/use_cases/)
    
    *   [SQL](/v0.1/docs/use_cases/sql/)
        
    *   [Chatbots](/v0.1/docs/use_cases/chatbots/)
        
    *   [Extraction](/v0.1/docs/use_cases/extraction/)
        
    *   [Query Analysis](/v0.1/docs/use_cases/query_analysis/)
        
        *   [Quickstart](/v0.1/docs/use_cases/query_analysis/quickstart/)
        *   [Techniques](/v0.1/docs/use_cases/query_analysis/techniques/decomposition/)
            
        *   [How-To Guides](/v0.1/docs/use_cases/query_analysis/how_to/few_shot/)
            
            *   [Add Examples to the Prompt](/v0.1/docs/use_cases/query_analysis/how_to/few_shot/)
            *   [Handle Cases Where No Queries are Generated](/v0.1/docs/use_cases/query_analysis/how_to/no_queries/)
            *   [Handle Multiple Queries](/v0.1/docs/use_cases/query_analysis/how_to/multiple_queries/)
            *   [Handle Multiple Retrievers](/v0.1/docs/use_cases/query_analysis/how_to/multiple_retrievers/)
            *   [Construct Filters](/v0.1/docs/use_cases/query_analysis/how_to/constructing_filters/)
            *   [Deal with High Cardinality Categoricals](/v0.1/docs/use_cases/query_analysis/how_to/high_cardinality/)
    *   [Q&A with RAG](/v0.1/docs/use_cases/question_answering/)
        
    *   [Tool use](/v0.1/docs/use_cases/tool_use/)
        
    *   [Interacting with APIs](/v0.1/docs/use_cases/api/)
    *   [Tabular Question Answering](/v0.1/docs/use_cases/tabular/)
    *   [Graphs](/v0.1/docs/use_cases/graph/)
        
    *   [Summarization](/v0.1/docs/use_cases/summarization/)
    *   [Agent Simulations](/v0.1/docs/use_cases/agent_simulations/)
        
    *   [Autonomous Agents](/v0.1/docs/use_cases/autonomous_agents/)
        
    *   [Code Understanding](/v0.1/docs/use_cases/code_understanding/)
    *   [Audio/Video Structured Extraction](/v0.1/docs/use_cases/media/)

*   [](/v0.1/)
*   [Use cases](/v0.1/docs/use_cases/)
*   [Query Analysis](/v0.1/docs/use_cases/query_analysis/)
*   How-To Guides
*   Handle Cases Where No Queries are Generated

On this page

Handle Cases Where No Queries are Generated
===========================================

Sometimes, a query analysis technique may allow for any number of queries to be generated - including no queries! In this case, our overall chain will need to inspect the result of the query analysis before deciding whether to call the retriever or not.

We will use mock data for this example.

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 @langchain/community @langchain/openai zod chromadb

    yarn add @langchain/core @langchain/community @langchain/openai zod chromadb

    pnpm add @langchain/core @langchain/community @langchain/openai zod chromadb

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

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

### Create Index[​](#create-index "Direct link to Create Index")

We will create a vectorstore over fake information.

    import { Chroma } from "@langchain/community/vectorstores/chroma";import { OpenAIEmbeddings } from "@langchain/openai";import "chromadb";const texts = ["Harrison worked at Kensho"];const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" });const vectorstore = await Chroma.fromTexts(texts, {}, embeddings, {  collectionName: "harrison",});const retriever = vectorstore.asRetriever(1);

    [Module: null prototype] {  AdminClient: [class AdminClient],  ChromaClient: [class ChromaClient],  CloudClient: [class CloudClient extends ChromaClient],  CohereEmbeddingFunction: [class CohereEmbeddingFunction],  Collection: [class Collection],  DefaultEmbeddingFunction: [class _DefaultEmbeddingFunction],  GoogleGenerativeAiEmbeddingFunction: [class _GoogleGenerativeAiEmbeddingFunction],  HuggingFaceEmbeddingServerFunction: [class HuggingFaceEmbeddingServerFunction],  IncludeEnum: {    Documents: "documents",    Embeddings: "embeddings",    Metadatas: "metadatas",    Distances: "distances"  },  JinaEmbeddingFunction: [class JinaEmbeddingFunction],  OpenAIEmbeddingFunction: [class _OpenAIEmbeddingFunction],  TransformersEmbeddingFunction: [class _TransformersEmbeddingFunction]}

Query analysis[​](#query-analysis "Direct link to Query analysis")
------------------------------------------------------------------

We will use function calling to structure the output. However, we will configure the LLM such that is doesn’t NEED to call the function representing a search query (should it decide not to). We will also then use a prompt to do query analysis that explicitly lays when it should and shouldn’t make a search.

    import { z } from "zod";const searchSchema = z.object({  query: z.string().describe("Similarity search query applied to job record."),});

### 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 { zodToJsonSchema } from "zod-to-json-schema";import { ChatPromptTemplate } from "@langchain/core/prompts";import {  RunnableSequence,  RunnablePassthrough,} from "@langchain/core/runnables";const system = `You have the ability to issue search queries to get information to help answer user information.You do not NEED to look things up. If you don't need to, then just respond normally.`;const prompt = ChatPromptTemplate.fromMessages([  ["system", system],  ["human", "{question}"],]);const llmWithTools = llm.bind({  tools: [    {      type: "function" as const,      function: {        name: "search",        description: "Search over a database of job records.",        parameters: zodToJsonSchema(searchSchema),      },    },  ],});const queryAnalyzer = RunnableSequence.from([  {    question: new RunnablePassthrough(),  },  prompt,  llmWithTools,]);

We can see that by invoking this we get an message that sometimes - but not always - returns a tool call.

    await queryAnalyzer.invoke("where did Harrison Work");

    AIMessage {  lc_serializable: true,  lc_kwargs: {    content: "",    additional_kwargs: {      function_call: undefined,      tool_calls: [        {          id: "call_uqHm5OMbXBkmqDr7Xzj8EMmd",          type: "function",          function: [Object]        }      ]    }  },  lc_namespace: [ "langchain_core", "messages" ],  content: "",  name: undefined,  additional_kwargs: {    function_call: undefined,    tool_calls: [      {        id: "call_uqHm5OMbXBkmqDr7Xzj8EMmd",        type: "function",        function: { name: "search", arguments: '{"query":"Harrison"}' }      }    ]  }}

    await queryAnalyzer.invoke("hi!");

    AIMessage {  lc_serializable: true,  lc_kwargs: {    content: "Hello! How can I assist you today?",    additional_kwargs: { function_call: undefined, tool_calls: undefined }  },  lc_namespace: [ "langchain_core", "messages" ],  content: "Hello! How can I assist you today?",  name: undefined,  additional_kwargs: { function_call: undefined, tool_calls: undefined }}

Retrieval with query analysis[​](#retrieval-with-query-analysis "Direct link to Retrieval with query analysis")
---------------------------------------------------------------------------------------------------------------

So how would we include this in a chain? Let’s look at an example below.

    import { JsonOutputKeyToolsParser } from "@langchain/core/output_parsers/openai_tools";const outputParser = new JsonOutputKeyToolsParser({  keyName: "search",});

    import { RunnableConfig, RunnableLambda } from "@langchain/core/runnables";const chain = async (question: string, config?: RunnableConfig) => {  const response = await queryAnalyzer.invoke(question, config);  if (    "tool_calls" in response.additional_kwargs &&    response.additional_kwargs.tool_calls !== undefined  ) {    const query = await outputParser.invoke(response, config);    return retriever.invoke(query[0].query, config);  } else {    return response;  }};const customChain = new RunnableLambda({ func: chain });

    await customChain.invoke("where did Harrison Work");

    [ Document { pageContent: "Harrison worked at Kensho", metadata: {} } ]

    await customChain.invoke("hi!");

    AIMessage {  lc_serializable: true,  lc_kwargs: {    content: "Hello! How can I assist you today?",    additional_kwargs: { function_call: undefined, tool_calls: undefined }  },  lc_namespace: [ "langchain_core", "messages" ],  content: "Hello! How can I assist you today?",  name: undefined,  additional_kwargs: { function_call: undefined, tool_calls: undefined }}

* * *

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

[

Previous

Add Examples to the Prompt

](/v0.1/docs/use_cases/query_analysis/how_to/few_shot/)[

Next

Handle Multiple Queries

](/v0.1/docs/use_cases/query_analysis/how_to/multiple_queries/)

*   [Setup](#setup)
    *   [Create Index](#create-index)
*   [Query analysis](#query-analysis)
*   [Retrieval with query analysis](#retrieval-with-query-analysis)

Community

*   [Discord](https://discord.gg/cU2adEyC7w)
*   [Twitter](https://twitter.com/LangChainAI)

GitHub

*   [Python](https://github.com/langchain-ai/langchain)
*   [JS/TS](https://github.com/langchain-ai/langchainjs)

More

*   [Homepage](https://langchain.com)
*   [Blog](https://blog.langchain.dev)

Copyright © 2024 LangChain, Inc.