docs fix order of logging integrations

docs/my-website/docs/proxy/logging.md

@@ -2,11 +2,11 @@
 
 Log Proxy input, output, and exceptions using:
 
-- Lunary
-- MLflow
 - Langfuse
 - OpenTelemetry
 - GCS, s3, Azure (Blob) Buckets
+- Lunary
+- MLflow
 - Custom Callbacks
 - Langsmith
 - DataDog
@@ -184,107 +184,6 @@ Found under `kwargs["standard_logging_object"]`. This is a standard payload, log
 
 [👉 **Standard Logging Payload Specification**](./logging_spec)
 
-## Lunary
-### Step1: Install dependencies and set your environment variables 
-Install the dependencies
-```shell
-pip install litellm lunary
-```
-
-Get you Lunary public key from from https://app.lunary.ai/settings 
-```shell
-export LUNARY_PUBLIC_KEY="<your-public-key>"
-```
-
-### Step 2: Create a `config.yaml` and set `lunary` callbacks
-
-```yaml
-model_list:
-  - model_name: "*"
-    litellm_params:
-      model: "*"
-litellm_settings:
-  success_callback: ["lunary"]
-  failure_callback: ["lunary"]
-```
-
-### Step 3: Start the LiteLLM proxy
-```shell
-litellm --config config.yaml
-```
-
-### Step 4: Make a request
-
-```shell
-curl -X POST 'http://0.0.0.0:4000/chat/completions' \
--H 'Content-Type: application/json' \
--d '{
-    "model": "gpt-4o",
-    "messages": [
-      {
-        "role": "system",
-        "content": "You are a helpful math tutor. Guide the user through the solution step by step."
-      },
-      {
-        "role": "user",
-        "content": "how can I solve 8x + 7 = -23"
-      }
-    ]
-}'
-```
-
-## MLflow
-
-
-### Step1: Install dependencies
-Install the dependencies.
-
-```shell
-pip install litellm mlflow
-```
-
-### Step 2: Create a `config.yaml` with `mlflow` callback
-
-```yaml
-model_list:
-  - model_name: "*"
-    litellm_params:
-      model: "*"
-litellm_settings:
-  success_callback: ["mlflow"]
-  failure_callback: ["mlflow"]
-```
-
-### Step 3: Start the LiteLLM proxy
-```shell
-litellm --config config.yaml
-```
-
-### Step 4: Make a request
-
-```shell
-curl -X POST 'http://0.0.0.0:4000/chat/completions' \
--H 'Content-Type: application/json' \
--d '{
-    "model": "gpt-4o-mini",
-    "messages": [
-      {
-        "role": "user",
-        "content": "What is the capital of France?"
-      }
-    ]
-}'
-```
-
-### Step 5: Review traces
-
-Run the following command to start MLflow UI and review recorded traces.
-
-```shell
-mlflow ui
-```
-
-
 ## Langfuse
 
 We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successfull LLM calls to langfuse. Make sure to set `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` in your environment
@@ -1298,6 +1197,109 @@ LiteLLM supports customizing the following Datadog environment variables
 | `HOSTNAME` | Hostname tag for your logs | "" | ❌ No |
 | `POD_NAME` | Pod name tag (useful for Kubernetes deployments) | "unknown" | ❌ No |
 
+
+## Lunary
+### Step1: Install dependencies and set your environment variables 
+Install the dependencies
+```shell
+pip install litellm lunary
+```
+
+Get you Lunary public key from from https://app.lunary.ai/settings 
+```shell
+export LUNARY_PUBLIC_KEY="<your-public-key>"
+```
+
+### Step 2: Create a `config.yaml` and set `lunary` callbacks
+
+```yaml
+model_list:
+  - model_name: "*"
+    litellm_params:
+      model: "*"
+litellm_settings:
+  success_callback: ["lunary"]
+  failure_callback: ["lunary"]
+```
+
+### Step 3: Start the LiteLLM proxy
+```shell
+litellm --config config.yaml
+```
+
+### Step 4: Make a request
+
+```shell
+curl -X POST 'http://0.0.0.0:4000/chat/completions' \
+-H 'Content-Type: application/json' \
+-d '{
+    "model": "gpt-4o",
+    "messages": [
+      {
+        "role": "system",
+        "content": "You are a helpful math tutor. Guide the user through the solution step by step."
+      },
+      {
+        "role": "user",
+        "content": "how can I solve 8x + 7 = -23"
+      }
+    ]
+}'
+```
+
+## MLflow
+
+
+### Step1: Install dependencies
+Install the dependencies.
+
+```shell
+pip install litellm mlflow
+```
+
+### Step 2: Create a `config.yaml` with `mlflow` callback
+
+```yaml
+model_list:
+  - model_name: "*"
+    litellm_params:
+      model: "*"
+litellm_settings:
+  success_callback: ["mlflow"]
+  failure_callback: ["mlflow"]
+```
+
+### Step 3: Start the LiteLLM proxy
+```shell
+litellm --config config.yaml
+```
+
+### Step 4: Make a request
+
+```shell
+curl -X POST 'http://0.0.0.0:4000/chat/completions' \
+-H 'Content-Type: application/json' \
+-d '{
+    "model": "gpt-4o-mini",
+    "messages": [
+      {
+        "role": "user",
+        "content": "What is the capital of France?"
+      }
+    ]
+}'
+```
+
+### Step 5: Review traces
+
+Run the following command to start MLflow UI and review recorded traces.
+
+```shell
+mlflow ui
+```
+
+
+
 ## Custom Callback Class [Async]
 
 Use this when you want to run custom callbacks in `python`