*   [](/v0.2/)
*   [Tutorials](/v0.2/docs/tutorials/)
*   Build an Extraction Chain

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Build an Extraction Chain
=========================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Chat Models](/v0.2/docs/concepts/#chat-models)
*   [Tools](/v0.2/docs/concepts/#tools)
*   [Tool calling](/v0.2/docs/concepts/#function-tool-calling)

In this tutorial, we will build a chain to extract structured information from unstructured text.

info

This tutorial will only work with models that support **function/tool calling**

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

### Installation[​](#installation "Direct link to Installation")

To install LangChain run:

*   npm
*   yarn
*   pnpm

    npm i langchain

    yarn add langchain

    pnpm add langchain

For more details, see our [Installation guide](/v0.2/docs/how_to/installation/).

### LangSmith[​](#langsmith "Direct link to LangSmith")

Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. The best way to do this is with [LangSmith](https://smith.langchain.com).

After you sign up at the link above, make sure to set your environment variables to start logging traces:

    export LANGCHAIN_TRACING_V2="true"export LANGCHAIN_API_KEY="..."

The Schema[​](#the-schema "Direct link to The Schema")
------------------------------------------------------

First, we need to describe what information we want to extract from the text.

We’ll use [Zod](https://zod.dev) to define an example schema that extracts personal information.

*   npm
*   yarn
*   pnpm

    npm i zod @langchain/core

    yarn add zod @langchain/core

    pnpm add zod @langchain/core

    import { z } from "zod";const personSchema = z.object({  name: z.string().nullish().describe("The name of the person"),  hair_color: z    .string()    .nullish()    .describe("The color of the person's hair if known"),  height_in_meters: z.string().nullish().describe("Height measured in meters"),});

There are two best practices when defining schema:

1.  Document the **attributes** and the **schema** itself: This information is sent to the LLM and is used to improve the quality of information extraction.
2.  Do not force the LLM to make up information! Above we used `.nullish()` for the attributes allowing the LLM to output `null` or `undefined` if it doesn’t know the answer.

info

For best performance, document the schema well and make sure the model isn’t force to return results if there’s no information to be extracted in the text.

The Extractor[​](#the-extractor "Direct link to The Extractor")
---------------------------------------------------------------

Let’s create an information extractor using the schema we defined above.

    import { ChatPromptTemplate } from "@langchain/core/prompts";// Define a custom prompt to provide instructions and any additional context.// 1) You can add examples into the prompt template to improve extraction quality// 2) Introduce additional parameters to take context into account (e.g., include metadata//    about the document from which the text was extracted.)const prompt = ChatPromptTemplate.fromMessages([  [    "system",    `You are an expert extraction algorithm.Only extract relevant information from the text.If you do not know the value of an attribute asked to extract,return null for the attribute's value.`,  ],  // Please see the how-to about improving performance with  // reference examples.  // ["placeholder", "{examples}"],  ["human", "{text}"],]);

We need to use a model that supports function/tool calling.

Please review [the documentation](/v0.2/docs/concepts#function-tool-calling) for list of some models that can be used with this API.

    import { ChatAnthropic } from "@langchain/anthropic";const llm = new ChatAnthropic({  model: "claude-3-sonnet-20240229",  temperature: 0,});const runnable = prompt.pipe(llm.withStructuredOutput(personSchema));const text = "Alan Smith is 6 feet tall and has blond hair.";await runnable.invoke({ text });

    { name: "Alan Smith", hair_color: "blond", height_in_meters: "1.83" }

info

Extraction is Generative 🤯

LLMs are generative models, so they can do some pretty cool things like correctly extract the height of the person in meters even though it was provided in feet!

We can see the LangSmith trace [here](https://smith.langchain.com/public/3d44b7e8-e7ca-4e02-951d-3290ccc89d64/r).

Even though we defined our schema with the variable name `personSchema`, Zod is unable to infer this name and therefore does not pass it along to the model. To help give the LLM more clues as to what your provided schema represents, you can also give the schema you pass to `withStructuredOutput()` a name:

    const runnable = prompt.pipe(  llm.withStructuredOutput(personSchema, { name: "person" }));const text = "Alan Smith is 6 feet tall and has blond hair.";await runnable.invoke({ text });

    { name: "Alan Smith", hair_color: "blond", height_in_meters: "1.83" }

This can improve performance in many cases.

Multiple Entities[​](#multiple-entities "Direct link to Multiple Entities")
---------------------------------------------------------------------------

In **most cases**, you should be extracting a list of entities rather than a single entity.

This can be easily achieved using Zod by nesting models inside one another.

    import { z } from "zod";const personSchema = z.object({  name: z.string().nullish().describe("The name of the person"),  hair_color: z    .string()    .nullish()    .describe("The color of the person's hair if known"),  height_in_meters: z.number().nullish().describe("Height measured in meters"),});const dataSchema = z.object({  people: z.array(personSchema).describe("Extracted data about people"),});

info

Extraction might not be perfect here. Please continue to see how to use **Reference Examples** to improve the quality of extraction, and see the **guidelines** section!

    const runnable = prompt.pipe(llm.withStructuredOutput(dataSchema));const text =  "My name is Jeff, my hair is black and i am 6 feet tall. Anna has the same color hair as me.";await runnable.invoke({ text });

    {  people: [    { name: "Jeff", hair_color: "black", height_in_meters: 1.83 },    { name: "Anna", hair_color: "black", height_in_meters: null }  ]}

tip

When the schema accommodates the extraction of **multiple entities**, it also allows the model to extract **no entities** if no relevant information is in the text by providing an empty list.

This is usually a **good** thing! It allows specifying **required** attributes on an entity without necessarily forcing the model to detect this entity.

We can see the LangSmith trace [here](https://smith.langchain.com/public/272096ab-9ac5-43f9-aa00-3b8443477d17/r)

Next steps[​](#next-steps "Direct link to Next steps")
------------------------------------------------------

Now that you understand the basics of extraction with LangChain, you’re ready to proceed to the rest of the how-to guides:

*   [Add Examples](/v0.2/docs/how_to/extraction_examples): Learn how to use **reference examples** to improve performance.
*   [Handle Long Text](/v0.2/docs/how_to/extraction_long_text): What should you do if the text does not fit into the context window of the LLM?
*   [Use a Parsing Approach](/v0.2/docs/how_to/extraction_parse): Use a prompt based approach to extract with models that do not support **tool/function calling**.

* * *

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*   [Setup](#setup)
    *   [Installation](#installation)
    *   [LangSmith](#langsmith)
*   [The Schema](#the-schema)
*   [The Extractor](#the-extractor)
*   [Multiple Entities](#multiple-entities)
*   [Next steps](#next-steps)