[MLOB-2058] Add Node.js Quickstart Sections for LLM Observability

content/en/llm_observability/quickstart.md

@@ -13,7 +13,7 @@
 
 ## Overview
 
-This guide uses the [LLM Observability SDK for Python][1]. If your application is written in another language, you can create traces by calling the [API][8] instead.
+This guide uses the LLM Observability SDKs for [Python][1] and [Node.js][2]. If your application is written in another language, you can create traces by calling the [API][8] instead.
 
 ## Setup
 
@@ -23,7 +23,7 @@
 
 ## Command line
 
-To generate an LLM Observability trace, you can run a Python script.
+To generate an LLM Observability trace, you can run a Python or Node.js script.
 
 ### Prerequisites
 
@@ -32,16 +32,33 @@
     - An OpenAI API key stored in your environment as `OPENAI_API_KEY`. To create one, see [Account Setup][4] and [Set up your API key][6] in the official OpenAI documentation.
     - The OpenAI Python library installed. See [Setting up Python][5] in the official OpenAI documentation for instructions.
 
-1. Install the SDK by adding the `ddtrace` and `openai` packages:
+1. Install the SDK and OpenAI packages:
 
-   {{< code-block lang="shell" >}}
+   {{< tabs >}}
+   {{% tab "Python" %}}
+
+   ```shell
    pip install ddtrace
    pip install openai
-   {{< /code-block >}}
+   ```
+
+   {{% /tab %}}
+   {{% tab "Node.js" %}}
+
+   ```shell
+   npm install dd-trace
+   npm install openai
+   ```
 
-1. Create a Python script and save it as `quickstart.py`. This Python script makes a single OpenAI call.
+   {{% /tab %}}
+   {{< /tabs >}}
+
+2. Create a script, which will make a single OpenAI call.
 
-   {{< code-block lang="python" filename="quickstart.py" >}}
+   {{< tabs >}}
+   {{% tab "Python" %}}
+
+   ```python
    import os
    from openai import OpenAI
 
@@ -54,40 +71,85 @@
         {"role": "user", "content": "I'd like to buy a chair for my living room."},
     ],
    )
-   {{< /code-block >}}
+   ```
+
+   {{% /tab %}}
+   {{% tab "Node.js" %}}
+
+   ```javascript
+   const { OpenAI } = require('openai');
+
+   const oaiClient = new OpenAI(process.env.OPENAI_API_KEY);
+
+   function main () {
+      const completion = await oaiClient.chat.completions.create({
+         model: 'gpt-3.5-turbo',
+         messages: [
+            { role: 'system', content: 'You are a helpful customer assistant for a furniture store.' },
+            { role: 'user', content: 'I\'d like to buy a chair for my living room.' },
+         ]
+      });
+   }
 
-1. Run the Python script with the following shell command. This sends a trace of the OpenAI call to Datadog.
+   main();
+   ```
+
+   {{% /tab %}}
+   {{< /tabs >}}
+
+3. Run the script with the following shell command. This sends a trace of the OpenAI call to Datadog.
+
+   {{< tabs >}}
+   {{% tab "Python" %}}
 
    ```shell
    DD_LLMOBS_ENABLED=1 DD_LLMOBS_ML_APP=onboarding-quickstart \
    DD_API_KEY=<YOUR_DATADOG_API_KEY> DD_SITE={{< region-param key="dd_site" >}} \
    DD_LLMOBS_AGENTLESS_ENABLED=1 ddtrace-run python quickstart.py
    ```
 
-   For more information about required environment variables, see [the SDK documentation][9]. 
+   For more information about required environment variables, see [the SDK documentation][1].
+
+   [1]: /llm_observability/setup/sdk/python/#command-line-setup
+
+   {{% /tab %}}
+   {{% tab "Node.js" %}}
+
+   ```shell
+   DD_LLMOBS_ENABLED=1 DD_LLMOBS_ML_APP=onboarding-quickstart \
+   DD_API_KEY=<YOUR_DATADOG_API_KEY> DD_SITE={{< region-param key="dd_site" >}} \
+   DD_LLMOBS_AGENTLESS_ENABLED=1 NODE_OPTIONS="--import dd-trace/initialize.mjs" node quickstart.js
+   ```
+
+   For more information about required environment variables, see [the SDK documentation][1].
+
+   [1]: /llm_observability/setup/sdk/nodejs/#command-line-setup
+
+   {{% /tab %}}
+   {{< /tabs >}}
 
    **Note**: `DD_LLMOBS_AGENTLESS_ENABLED` is only required if you do not have the Datadog Agent running. If the Agent is running in your production environment, make sure this environment variable is unset.
 
-1. View the trace of your LLM call on the **Traces** tab [of the **LLM Observability** page][3] in Datadog.
+4. View the trace of your LLM call on the **Traces** tab [of the **LLM Observability** page][3] in Datadog.
 
    {{< img src="llm_observability/quickstart_trace_1.png" alt="An LLM Observability trace displaying a single LLM request" style="width:100%;" >}}
 
-The trace you see is composed of a single LLM span. The `ddtrace-run` command automatically traces your LLM calls from [Datadog's list of supported integrations][10].
+The trace you see is composed of a single LLM span. The `ddtrace-run` or `NODE_OPTIONS="--import dd-trace/initialize.mjs"` command automatically traces your LLM calls from [Datadog's list of supported integrations][10].
 
 If your application consists of more elaborate prompting or complex chains or workflows involving LLMs, you can trace it using the [Setup documentation][11] and the [SDK documentation][1].
 
 ## Further Reading
 
 {{< partial name="whats-next/whats-next.html" >}}
 
-[1]: /llm_observability/setup/sdk/
+[1]: /llm_observability/setup/sdk/python
+[2]: /llm_observability/setup/sdk/nodejs
 [3]: https://app.datadoghq.com/llm/traces
 [4]: https://platform.openai.com/docs/quickstart/account-setup
 [5]: https://platform.openai.com/docs/quickstart/step-1-setting-up-python
 [6]: https://platform.openai.com/docs/quickstart/step-2-set-up-your-api-key
 [7]: /account_management/api-app-keys/#add-an-api-key-or-client-token
 [8]: /llm_observability/setup/api
-[9]: /llm_observability/setup/sdk/#command-line-setup
 [10]: /llm_observability/setup/auto_instrumentation/
 [11]: /llm_observability/setup/
 [12]: https://github.com/DataDog/llm-observability