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*   [Get started](/v0.1/docs/get_started/)
    
    *   [Introduction](/v0.1/docs/get_started/introduction/)
    *   [Installation](/v0.1/docs/get_started/installation/)
    *   [Quickstart](/v0.1/docs/get_started/quickstart/)
*   [LangChain Expression Language](/v0.1/docs/expression_language/)
    
    *   [Get started](/v0.1/docs/expression_language/get_started/)
    *   [Why use LCEL?](/v0.1/docs/expression_language/why/)
    *   [Interface](/v0.1/docs/expression_language/interface/)
    *   [Streaming](/v0.1/docs/expression_language/streaming/)
    *   [How to](/v0.1/docs/expression_language/how_to/routing/)
        
    *   [Cookbook](/v0.1/docs/expression_language/cookbook/)
        
        *   [Prompt + LLM](/v0.1/docs/expression_language/cookbook/prompt_llm_parser/)
        *   [Multiple chains](/v0.1/docs/expression_language/cookbook/multiple_chains/)
        *   [Retrieval augmented generation (RAG)](/v0.1/docs/expression_language/cookbook/retrieval/)
        *   [Querying a SQL DB](/v0.1/docs/expression_language/cookbook/sql_db/)
        *   [Adding memory](/v0.1/docs/expression_language/cookbook/adding_memory/)
        *   [Using tools](/v0.1/docs/expression_language/cookbook/tools/)
        *   [Agents](/v0.1/docs/expression_language/cookbook/agents/)
    *   [LangChain Expression Language (LCEL)](/v0.1/docs/expression_language/)
*   [Modules](/v0.1/docs/modules/)
    
    *   [Model I/O](/v0.1/docs/modules/model_io/)
        
    *   [Retrieval](/v0.1/docs/modules/data_connection/)
        
    *   [Chains](/v0.1/docs/modules/chains/)
    *   [Agents](/v0.1/docs/modules/agents/)
        
    *   [More](/v0.1/docs/modules/memory/)
        
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*   * * *
    

*   [](/v0.1/)
*   [LangChain Expression Language](/v0.1/docs/expression_language/)
*   [Cookbook](/v0.1/docs/expression_language/cookbook/)
*   Querying a SQL DB

Querying a SQL DB
=================

We can replicate our SQLDatabaseChain with Runnables.

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

We'll need the Chinook sample DB for this example.

First install `typeorm`:

*   npm
*   Yarn
*   pnpm

    npm install typeorm

    yarn add typeorm

    pnpm add typeorm

Then install the dependencies needed for your database. For example, for SQLite:

*   npm
*   Yarn
*   pnpm

    npm install sqlite3

    yarn add sqlite3

    pnpm add sqlite3

For other databases see [https://typeorm.io/#installation](https://typeorm.io/#installation).

Finally follow the instructions on [https://database.guide/2-sample-databases-sqlite/](https://database.guide/2-sample-databases-sqlite/) to get the sample database for this example.

Composition[​](#composition "Direct link to Composition")
---------------------------------------------------------

tip

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

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

    import { DataSource } from "typeorm";import { SqlDatabase } from "langchain/sql_db";import { ChatOpenAI } from "@langchain/openai";import {  RunnablePassthrough,  RunnableSequence,} from "@langchain/core/runnables";import { PromptTemplate } from "@langchain/core/prompts";import { StringOutputParser } from "@langchain/core/output_parsers";const datasource = new DataSource({  type: "sqlite",  database: "Chinook.db",});const db = await SqlDatabase.fromDataSourceParams({  appDataSource: datasource,});const prompt =  PromptTemplate.fromTemplate(`Based on the table schema below, write a SQL query that would answer the user's question:{schema}Question: {question}SQL Query:`);const model = new ChatOpenAI();// The `RunnablePassthrough.assign()` is used here to passthrough the input from the `.invoke()`// call (in this example it's the question), along with any inputs passed to the `.assign()` method.// In this case, we're passing the schema.const sqlQueryGeneratorChain = RunnableSequence.from([  RunnablePassthrough.assign({    schema: async () => db.getTableInfo(),  }),  prompt,  model.bind({ stop: ["\nSQLResult:"] }),  new StringOutputParser(),]);const result = await sqlQueryGeneratorChain.invoke({  question: "How many employees are there?",});console.log({  result,});/*  {    result: "SELECT COUNT(EmployeeId) AS TotalEmployees FROM Employee"  }*/const finalResponsePrompt =  PromptTemplate.fromTemplate(`Based on the table schema below, question, sql query, and sql response, write a natural language response:{schema}Question: {question}SQL Query: {query}SQL Response: {response}`);const fullChain = RunnableSequence.from([  RunnablePassthrough.assign({    query: sqlQueryGeneratorChain,  }),  {    schema: async () => db.getTableInfo(),    question: (input) => input.question,    query: (input) => input.query,    response: (input) => db.run(input.query),  },  finalResponsePrompt,  model,]);const finalResponse = await fullChain.invoke({  question: "How many employees are there?",});console.log(finalResponse);/*  AIMessage {    content: 'There are 8 employees.',    additional_kwargs: { function_call: undefined }  }*/

#### API Reference:

*   [SqlDatabase](https://api.js.langchain.com/classes/langchain_sql_db.SqlDatabase.html) from `langchain/sql_db`
*   [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [RunnablePassthrough](https://api.js.langchain.com/classes/langchain_core_runnables.RunnablePassthrough.html) from `@langchain/core/runnables`
*   [RunnableSequence](https://api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables`
*   [PromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts`
*   [StringOutputParser](https://api.js.langchain.com/classes/langchain_core_output_parsers.StringOutputParser.html) from `@langchain/core/output_parsers`

* * *

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

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