[llmobs] Clean up setup documentation

config/_default/menus/main.en.yaml

@@ -4604,36 +4604,26 @@ menu:
       identifier: llm_obs
       parent: ai_observability_heading
       weight: 10000
-    - name: Terms and Concepts
-      url: llm_observability/terms/
-      parent: llm_obs
-      identifier: llm_obs_terms
-      weight: 1
     - name: Quickstart
       url: llm_observability/quickstart/
       parent: llm_obs
       identifier: llm_obs_quickstart
       weight: 2
-    - name: Setup
-      url: llm_observability/setup/
+    - name: Instrumentation
+      url: llm_observability/instrumentation/
       parent: llm_obs
-      identifier: llm_obs_setup
+      identifier: llm_obs_instrumentation
       weight: 3
     - name: LLM Obs SDK
       url: llm_observability/sdk
-      parent: llm_obs
+      parent: llm_obs_instrumentation
       identifier: llm_obs_sdk
-      weight: 4
-    - name: Auto Instrumentation
-      url: llm_observability/sdk/auto_instrumentation
-      parent: llm_obs_sdk
-      identifier: llm_obs_setup_auto_instrumentation
-      weight: 401
+      weight: 301
     - name: LLM Obs API
       url: llm_observability/setup/api/
-      parent: llm_obs
+      parent: llm_obs_instrumentation
       identifier: llm_obs_api
-      weight: 6
+      weight: 302
     - name: Monitoring
       url: llm_observability/monitoring
       parent: llm_obs
@@ -4684,6 +4674,11 @@ menu:
       parent: llm_obs
       identifier: llm_obs_experiments
       weight: 9
+    - name: Terms and Concepts
+      url: llm_observability/terms/
+      parent: llm_obs
+      identifier: llm_obs_terms
+      weight: 1
     - name: Guides
       url: llm_observability/guide/
       parent: llm_obs

content/en/llm_observability/quickstart.md

@@ -6,126 +6,70 @@ further_reading:
     - link: '/llm_observability'
       tag: 'Documentation'
       text: 'Learn about LLM Observability'
+    - link: '/llm_observability/sdk'
+      tag: 'SDK'
+      text: 'SDK reference'
 ---
 
-## Overview
-
-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
-
-### Jupyter notebooks
-
-To better understand LLM Observability terms and concepts, you can explore the examples in the [LLM Observability Jupyter Notebooks repository][12]. These notebooks provide a hands-on experience, and allow you to apply these concepts in real time.
 
 ## Trace an LLM application
 
-To generate an LLM Observability trace, you can run a Python or Node.js script.
-
 ### Prerequisites
 
-- LLM Observability requires a Datadog API key. For more information, see [the instructions for creating an API key][7].
-- The following example script uses OpenAI, but you can modify it to use a different provider. To run the script as written, you need:
-    - 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.
+- LLM Observability requires a Datadog API key if you don't have an Agent running. Find your API key [in the Datadog application](https://app.datadoghq.com/organization-settings/api-keys).
 
 {{< tabs >}}
 {{% tab "Python" %}}
 
-1. Install the SDK and OpenAI packages:
+1. Install the SDK:
 
    ```shell
    pip install ddtrace
-   pip install openai
-   ```
-
-2. Create a script, which makes a single OpenAI call.
-
-   ```python
-   import os
-   from openai import OpenAI
-
-   oai_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
-
-   completion = oai_client.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."},
-    ],
-   )
    ```
 
-3. Run the script with the following shell command. This sends a trace of the OpenAI call to Datadog.
+2. Prefix your Python start command with `ddtrace-run`:
 
    ```shell
-   DD_LLMOBS_ENABLED=1 DD_LLMOBS_ML_APP=onboarding-quickstart \
-   DD_API_KEY=<YOUR_DATADOG_API_KEY> DD_SITE=<YOUR_DD_SITE> \
-   DD_LLMOBS_AGENTLESS_ENABLED=1 ddtrace-run python quickstart.py
+   DD_LLMOBS_ENABLED=1 \
+   DD_LLMOBS_ML_APP=quickstart-app \
+   DD_API_KEY=<YOUR_DATADOG_API_KEY> \
+   DD_SITE={{< region-param key="dd_site" code="true" >}} \
+   DD_LLMOBS_AGENTLESS_ENABLED=1 \
+   ddtrace-run <your application command>
    ```
 
-   Replace `<YOUR_DATADOG_API_KEY>` with your Datadog API key, and replace `<YOUR_DD_SITE>` with your [Datadog site][2].
+   Replace `<YOUR_DATADOG_API_KEY>` with your Datadog API key.
 
-   For more information about required environment variables, see [the SDK documentation][1].
 
 [1]: /llm_observability/setup/sdk/python/#command-line-setup
 [2]: /getting_started/site/
 {{% /tab %}}
 
 {{% tab "Node.js" %}}
-1. Install the SDK and OpenAI packages:
+1. Install the SDK:
 
    ```shell
    npm install dd-trace
-   npm install openai
    ```
-2. Create a script, which makes a single OpenAI call.
-
-   ```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.' },
-         ]
-      });
-   }
-
-   main();
-   ```
-
-3. Run the script with the following shell command. This sends a trace of the OpenAI call to Datadog.
+2. Add `NODE_OPTIONS` your Node.js start command:
    ```shell
-   DD_LLMOBS_ENABLED=1 DD_LLMOBS_ML_APP=onboarding-quickstart \
-   DD_API_KEY=<YOUR_DATADOG_API_KEY> DD_SITE=<YOUR_DD_SITE> \
-   DD_LLMOBS_AGENTLESS_ENABLED=1 NODE_OPTIONS="--import dd-trace/initialize.mjs" node quickstart.js
+   DD_LLMOBS_ENABLED=1 \
+   DD_LLMOBS_ML_APP=quickstart-app \
+   DD_API_KEY=<YOUR_DATADOG_API_KEY> \
+   DD_SITE={{< region-param key="dd_site" code="true" >}} \
+   DD_LLMOBS_AGENTLESS_ENABLED=1 \
+   NODE_OPTIONS="--import dd-trace/initialize.mjs" <your application command>
    ```
 
-   Replace `<YOUR_DATADOG_API_KEY>` with your Datadog API key, and replace `<YOUR_DD_SITE>` with your [Datadog site][2].
-
-   For more information about required environment variables, see [the SDK documentation][1].
+   Replace `<YOUR_DATADOG_API_KEY>` with your Datadog API key.
 
 [1]: /llm_observability/setup/sdk/nodejs/#command-line-setup
 [2]: /getting_started/site/
 
 {{% /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.
-
-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` 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].
-
 ## Trace an LLM application in AWS Lambda
 The following steps generate an LLM Observability trace in an AWS Lambda environment and create an Amazon Bedrock based chatbot running with LLM Observability in AWS Lambda.
 
@@ -195,6 +139,9 @@ export const handler = async (event) => {
 {{% /tab %}}
 {{< /tabs >}}
 
+3. Make requests to your application triggering LLM calls and then view traces in the **Traces** tab [of the **LLM Observability** page][3] in Datadog. If you don't see any traces, make sure you are using a supported library else you may need to instrument your application's LLM calls manually.
+
+
 ## Further Reading
 
 {{< partial name="whats-next/whats-next.html" >}}

content/en/llm_observability/sdk/_index.md

@@ -6,26 +6,39 @@ aliases:
     - /llm_observability/setup/sdk/python
     - /llm_observability/setup/sdk/nodejs
     - /llm_observability/setup/sdk
+    - /llm_observability/instrumentation
 ---
 
 ## Overview
 
-Datadog's LLM Observability SDK for enhances the observability of your LLM applications. The SDK supports Python versions 3.7 and newer. 
+Datadog's LLM Observability SDK provides automatic instrumentation as well as manual APIs that give you deep and rich insights into the cost, health and quality of your LLM applications.
 
-### Supported runtimes
-
-| Runtime | Version |
-| ------- | ------- |
-| Python  | 3.7+    |
-| Node.js | 16+     |
 
 For information about LLM Observability's integration support, see [Auto Instrumentation][13].
 
 You can install and configure tracing of various operations such as workflows, tasks, and API calls with function decorators or context managers. You can also annotate these traces with metadata for deeper insights into the performance and behavior of your applications, supporting multiple LLM services or models from the same environment.
 
-For usage examples you can run from a Jupyter notebook, see the [LLM Observability Jupyter Notebooks repository][10].
+## Installation
+
+## Enabling
+
+## Configuration
+
+## Automatic Instrumentation
+
+## Manual Instrumentation
+
+### Capturing LLM Operations
+
+#### Distributed workflows
 
-## Setup
+#### Annotating LLM Operations
+
+#### Tagging User Sessions
+
+### Submitting Evaluations
+
+### Modifying Operation Input and Output
 
 ### Prerequisites
 
@@ -841,7 +854,7 @@ getRelevantDocs = llmobs.wrap({ kind: 'retrieval' }, getRelevantDocs)
 
 ## Tracking user sessions
 
-Session tracking allows you to associate multiple interactions with a given user. 
+Session tracking allows you to associate multiple interactions with a given user.
 
 {{< tabs >}}
 {{% tab "Python" %}}
@@ -1635,6 +1648,14 @@ tracer.use('http', false) // disable the http integration
 {{< /tabs >}}
 
 
+### Supported runtimes
+
+| Runtime | Version |
+| ------- | ------- |
+| Python  | 3.7+    |
+| Node.js | 16+     |
+
+
 [1]: https://github.com/openai/openai-python
 [2]: https://boto3.amazonaws.com/v1/documentation/api/latest/index.html
 [3]: https://botocore.amazonaws.com/v1/documentation/api/latest/tutorial/index.html