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

# Enable user analytics

> Keywords AI allows you to track user data and monitor the user's data in the platform.

## How does user analytics work?

On Keywords AI, we built a [Users page](https://platform.keywordsai.co/platform/users) to help you understand your users and their behavior on your LLM applications. With our user analytics feature, you can;

* Track user LLM usage
* Set user budget
* Filter logs by user identifier

<Frame>
  <img src="https://keywordsai-static.s3.us-east-1.amazonaws.com/docs/observability/user-monitoring.png" alt="Users Page" />
</Frame>

## Enable user analytics in the code

You can pass a `customer_params` to the request to track the user's behavior on your LLM applications.

<Tabs>
  <Tab title="OpenAI Python SDK">
    Here is an example of how to send a user's data with the parameter `customer_params` to Keywords AI in the OpenAI Python SDK.

    ```python {13-19} theme={"system"}
    from openai import OpenAI

    client = OpenAI(
        base_url="https://api.keywordsai.co/api/",
        api_key="YOUR_KEYWORDSAI_API_KEY",
    )

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[
            {"role": "user", "content": "Tell me a long story"}
        ],
        extra_body={
            "customer_params": {
                "customer_identifier": "customer_1",
                "name": "Hendrix Liu", # optional parameter 
                "email": "hendrix@keywordsai.co" # optional parameter
            }
        }
    )
    ```
  </Tab>

  <Tab title="OpenAI TypeScript SDK">
    Here is an example of how to send a user's data with the parameter `customer_params` to Keywords AI in the OpenAI TypeScript SDK. In OpenAI TypeScript SDK, you should add a `// @ts-expect-error` before the metadata field.

    ```typescript {12-17} theme={"system"}
    import { OpenAI } from "openai";

    const client = new OpenAI({
      baseURL: "https://api.keywordsai.co/api",
      apiKey: "YOUR_KEYWORDSAI_API_KEY",
    });

    const response = await client.chat.completions
      .create({
        messages: [{ role: "user", content: "Say this is a test" }],
        model: "gpt-4o-mini",
        // @ts-expect-error
        customer_params: {
            "customer_identifier": "customer_1",
            "name": "Hendrix Liu", // optional parameter
            "email": "hendrix@keywordsai.co" // optional parameter
        }
      })
      .asResponse();

    console.log(await response.json());
    ```
  </Tab>

  <Tab title="Standard API">
    <CodeGroup>
      ```python LLM proxy {14-18} theme={"system"}
      import requests
      def demo_call(input, 
                    model="gpt-4o-mini",
                    token="YOUR_KEYWORDS_AI_API_KEY",
                    ):
          headers = {
              'Content-Type': 'application/json',
              'Authorization': f'Bearer {token}',
          }

          data = {
              'model': model,
              'messages': [{'role': 'user', 'content': input}],
              'customer_params': {
                  "customer_identifier": "customer_1",
                  "name": "Hendrix Liu", # optional parameter
                  "email": "hendrix@keywordsai.co" # optional parameter
              }
          }

          response = requests.post('https://api.keywordsai.co/api/chat/completions', headers=headers, json=data)
          return response

      messages = "Say 'Hello World'"
      print(demo_call(messages).json())
      ```

      ```python Async logging {16-20} theme={"system"}
      import requests

      url = "https://api.keywordsai.co/api/request-logs/create/"
      payload = {
          "model": "gpt-4o-mini",
          "prompt_messages": [
              {
                  "role": "user",
                  "content": "Hi"
              },
          ],
          "completion_message": {
              "role": "assistant",
              "content": "Hi, how can I assist you today?"
          },
          "customer_params": {
              "customer_identifier": "customer_1",
              "name": "Hendrix Liu", # optional parameter
              "email": "hendrix@keywordsai.co" # optional parameter
          }
          # other parameters
      }
      headers = {
          "Authorization": "Bearer YOUR_KEYWORDS_AI_API_KEY",
          "Content-Type": "application/json"
      }

      response = requests.request("POST", url, headers=headers, json=payload)
      ```
    </CodeGroup>
  </Tab>

  <Tab title="Other SDKs">
    We also support adding credentials in other SDKs or languages, please check out our [integration section](/documentation/admin/llm_provider_keys) for more information.
  </Tab>
</Tabs>

## See user data on the platform

After you send the user's data to Keywords AI, there are 3 places you can see the user's data on the platform.

### 1. [Users page](https://platform.keywordsai.co/platform/users)

You will see each user's detail information and their LLM usage on the Users page and the side panel.

<Frame>
  <img src="https://keywordsai-static.s3.us-east-1.amazonaws.com/docs/observability/users-page.png" alt="Users Page" />
</Frame>

### 2. [Dashboard page](https://platform.keywordsai.co/platform/dashboard)

There are several charts on the Dashboard page to help you understand your users' behavior, including;

* Top users
* Daily active users count
* LLM cost by user

<Frame>
  <img src="https://keywordsai-static.s3.us-east-1.amazonaws.com/docs/observability/dashboard-users.png" alt="Users Page" />
</Frame>

### 3. [Logs page](https://platform.keywordsai.co/platform/requests)

For each LLM log, you can see the corresponding user's data in the side panel. You can also filter logs by choosing a User Identifier.

<Frame>
  <img src="https://keywordsai-static.s3.us-east-1.amazonaws.com/docs/observability/logs-users.png" alt="Users Page" />
</Frame>

## Create a user without sending a log