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*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
    *   [Build a Query Analysis System](/v0.2/docs/tutorials/query_analysis)
    *   [Build a Chatbot](/v0.2/docs/tutorials/chatbot)
    *   [Build an Agent](/v0.2/docs/tutorials/agents)
    *   [Build an Extraction Chain](/v0.2/docs/tutorials/extraction)
    *   [Summarize Text](/v0.2/docs/tutorials/summarization)
    *   [Tagging](/v0.2/docs/tutorials/classification)
    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
    *   [Conversational RAG](/v0.2/docs/tutorials/qa_chat_history)
    *   [Build a Retrieval Augmented Generation (RAG) App](/v0.2/docs/tutorials/rag)
    *   [Build a Question/Answering system over SQL data](/v0.2/docs/tutorials/sql_qa)
*   [How-to guides](/v0.2/docs/how_to/)
    
    *   [How-to guides](/v0.2/docs/how_to/)
    *   [How to use example selectors](/v0.2/docs/how_to/example_selectors)
    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
    *   [How to create a custom LLM class](/v0.2/docs/how_to/custom_llm)
    *   [How to use few shot examples](/v0.2/docs/how_to/few_shot_examples)
    *   [How to use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
    *   [How to return structured data from a model](/v0.2/docs/how_to/structured_output)
    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
    *   [How to create a custom chat model class](/v0.2/docs/how_to/custom_chat)
    *   [How to do per-user retrieval](/v0.2/docs/how_to/qa_per_user)
    *   [How to track token usage](/v0.2/docs/how_to/chat_token_usage_tracking)
    *   [How to track token usage](/v0.2/docs/how_to/llm_token_usage_tracking)
    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
    *   [How to compose prompts together](/v0.2/docs/how_to/prompts_composition)
    *   [How to use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)
    *   [How to add values to a chain's state](/v0.2/docs/how_to/assign)
    *   [How to attach runtime arguments to a Runnable](/v0.2/docs/how_to/binding)
    *   [How to cache embedding results](/v0.2/docs/how_to/caching_embeddings)
    *   [How to split by character](/v0.2/docs/how_to/character_text_splitter)
    *   [How to manage memory](/v0.2/docs/how_to/chatbots_memory)
    *   [How to do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
    *   [How to do retrieval with contextual compression](/v0.2/docs/how_to/contextual_compression)
    *   [How to write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
    *   [How to create custom Tools](/v0.2/docs/how_to/custom_tools)
    *   [How to debug your LLM apps](/v0.2/docs/how_to/debugging)
    *   [How to load CSV data](/v0.2/docs/how_to/document_loader_csv)
    *   [How to write a custom document loader](/v0.2/docs/how_to/document_loader_custom)
    *   [How to load data from a directory](/v0.2/docs/how_to/document_loader_directory)
    *   [How to load PDF files](/v0.2/docs/how_to/document_loader_pdf)
    *   [How to load JSON data](/v0.2/docs/how_to/document_loaders_json)
    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
    *   [How to select examples by similarity](/v0.2/docs/how_to/example_selectors_similarity)
    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
    *   [How to handle long text](/v0.2/docs/how_to/extraction_long_text)
    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
    *   [How to run custom functions](/v0.2/docs/how_to/functions)
    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
    *   [How to map values to a database](/v0.2/docs/how_to/graph_mapping)
    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
    *   [How to return sources](/v0.2/docs/how_to/qa_sources)
    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
    *   [How to split text by tokens](/v0.2/docs/how_to/split_by_token)
    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
*   [Conceptual guide](/v0.2/docs/concepts)
*   Ecosystem
    
    *   [🦜🛠️ LangSmith](/v0.2/docs/langsmith/)
        
    *   [🦜🕸️LangGraph.js](/v0.2/docs/langgraph)
*   Versions
    
    *   [Overview](/v0.2/docs/versions/overview)
    *   [v0.2](/v0.2/docs/versions/v0_2)
    *   [Release Policy](/v0.2/docs/versions/release_policy)
    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

*   [](/v0.2/)
*   [Tutorials](/v0.2/docs/tutorials/)
*   Tagging

On this page

Classify Text into Labels
=========================

Tagging means labeling a document with classes such as:

*   sentiment
*   language
*   style (formal, informal etc.)
*   covered topics
*   political tendency

![Image description](/v0.2/assets/images/tagging-93990e95451d92b715c2b47066384224.png)

Overview[​](#overview "Direct link to Overview")
------------------------------------------------

Tagging has a few components:

*   `function`: Like [extraction](/v0.2/docs/tutorials/extraction), tagging uses [functions](https://openai.com/blog/function-calling-and-other-api-updates) to specify how the model should tag a document
*   `schema`: defines how we want to tag the document

Quickstart[​](#quickstart "Direct link to Quickstart")
------------------------------------------------------

Let’s see a very straightforward example of how we can use OpenAI tool calling for tagging in LangChain. We’ll use the `.withStructuredOutput()` method supported by OpenAI models:

*   npm
*   yarn
*   pnpm

    npm i langchain @langchain/openai @langchain/core zod

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

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

Let’s specify a Pydantic model with a few properties and their expected type in our schema.

    import { ChatPromptTemplate } from "@langchain/core/prompts";import { ChatOpenAI } from "@langchain/openai";import { z } from "zod";const taggingPrompt = ChatPromptTemplate.fromTemplate(  `Extract the desired information from the following passage.Only extract the properties mentioned in the 'Classification' function.Passage:{input}`);const classificationSchema = z.object({  sentiment: z.string().describe("The sentiment of the text"),  aggressiveness: z    .number()    .int()    .min(1)    .max(10)    .describe("How aggressive the text is on a scale from 1 to 10"),  language: z.string().describe("The language the text is written in"),});// LLMconst llm = new ChatOpenAI({  temperature: 0,  model: "gpt-3.5-turbo-0125",});const llmWihStructuredOutput = llm.withStructuredOutput(classificationSchema);const taggingChain = taggingPrompt.pipe(llmWihStructuredOutput);

    const input =  "Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!";await taggingChain.invoke({ input });

    { sentiment: "positive", aggressiveness: 1, language: "Spanish" }

As we can see in the example, it correctly interprets what we want.

The results vary so that we may get, for example, sentiments in different languages (‘positive’, ‘enojado’ etc.).

We will see how to control these results in the next section.

Finer control[​](#finer-control "Direct link to Finer control")
---------------------------------------------------------------

Careful schema definition gives us more control over the model’s output.

Specifically, we can define:

*   possible values for each property
*   description to make sure that the model understands the property
*   required properties to be returned

Let’s redeclare our Pydantic model to control for each of the previously mentioned aspects using enums:

    import { z } from "zod";const classificationSchema = z.object({  sentiment: z    .enum(["happy", "neutral", "sad"])    .describe("The sentiment of the text"),  aggressiveness: z    .number()    .int()    .min(1)    .max(5)    .describe(      "describes how aggressive the statement is, the higher the number the more aggressive"    ),  language: z    .enum(["spanish", "english", "french", "german", "italian"])    .describe("The language the text is written in"),});

    const taggingPrompt = ChatPromptTemplate.fromTemplate(  `Extract the desired information from the following passage.Only extract the properties mentioned in the 'Classification' function.Passage:{input}`);// LLMconst llm = new ChatOpenAI({  temperature: 0,  model: "gpt-3.5-turbo-0125",});const llmWihStructuredOutput = llm.withStructuredOutput(classificationSchema);const chain = taggingPrompt.pipe(llmWihStructuredOutput);

Now the answers will be restricted in a way we expect!

    const input =  "Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!";await chain.invoke({ input });

    { sentiment: "happy", aggressiveness: 3, language: "spanish" }

    const input = "Estoy muy enojado con vos! Te voy a dar tu merecido!";await chain.invoke({ input });

    { sentiment: "sad", aggressiveness: 5, language: "spanish" }

    const input =  "Weather is ok here, I can go outside without much more than a coat";await chain.invoke({ input });

    { sentiment: "neutral", aggressiveness: 3, language: "english" }

The [LangSmith trace](https://smith.langchain.com/public/455f5404-8784-49ce-8851-0619b99e936f/r) lets us peek under the hood:

### TODO ADD[​](#todo-add "Direct link to TODO ADD")

![Image description](/v0.2/assets/images/classification_ls_trace-7b269b067c3751c6d06289c560505656.png)

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