> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lytix.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Metadata

> How to start sending metadata

![Workflow Example](https://lytix-cdn.s3.amazonaws.com/documentationAssets/workflows/metadataIntro.gif)

Metadata allows you to add custom tags to your LLM calls. This can be useful for grouping and filtering data by environment (i.e. `dev`, `stage`, `prod`), regions (i.e. `us-east-1`, `eu-west-1`) or model/device versioning.

<Note>
  **Note:** Metada works best with >50 unique key-value pairs. Metadata is not useful for storing highly unique strings (such as user id's, randomly generated values, e.g. anything with high cardinality). Columns with high cardinality with result in perfromance issues. Contact [support@lytix.co](mailto:support@lytix.co) if you need to store highly unique values.
</Note>

<Note>
  **Note:** There are some reserved keys you should not use:

  * `env`: This is used to track the environment of the LLM call. This is a key managed by Lytix and only accepts values of `dev`, `stage` or `prod`.
</Note>

#### Use [Optimodel](/Quickstart/optimodel-getting-started) to push metadata (see code below)

<CodeGroup>
  ```py Python
  from optimodel import queryModel, ModelTypes, ModelMessage, ModelMessageContentEntry

  response = await queryModel(
      model=ModelTypes.llama_3_70b_instruct,
      messages=[
          ModelMessage(
              role="system",
              content="Always respond with a JSON object",
          ),
          ModelMessage(role="user", content=[
              ModelMessageContentEntry(type="text", text="Hello how are you again?"),
          ]),
      ],
      # Just set this field
      metadata={
          "key": "value"
      }
  )
  ```

  ```py Typescript
  import { queryModel, ModelTypes } from "@lytix/client";

  const main = async () => {
    const prompt = "Hello How are you?";
    const response = await queryModel({
      model: ModelTypes.gpt_4o_mini,
      messages: [
        { role: "user", content: prompt },
        { role: "system", content: "Always respond with a JSON object" },
      ],
      // Just set this field
      metadata={
          "key": "value"
      }
    });
    console.log(`Got response: ${response}`);
  };
  ```
</CodeGroup>

#### Use the native OpenAI client to push metadata

<CodeGroup>
  ```py Python
  response = client.chat.completions.create(
      model=ModelTypes.mistral_large_latest.name,
      temperature=0,
      max_tokens=1000,
      messages=[
          {"role": "system", "content": "You are a helpful assistant. Always JSON"},
          {"role": "user", "content": "How are you?"},
      ],
      extra_headers={
          # Set this field, replace {METADATA_KEY} and {METADATA_VALUE} with your metadata
          "lytix-metadata-key-{METADATA_KEY}": "{METADATA_VALUE}"
      }
  )
  ```

  ```js Typescript
  const response = await client.chat.completions.create(
    {
      messages: [{ role: "user", content: "Hello world" }],
      model: "gpt-4",
    },
    {
      headers: {
        // Add your workflowName to track workflows
        // Replace {METADATA_KEY} and {METADATA_VALUE} with your metadata
        "lytix-metadata-key-{METADATA_KEY}": "{METADATA_VALUE}"
      },
    }
  );
  ```
</CodeGroup>