docs(enterprise.md): add logging spend with custom metadata

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

@@ -427,4 +427,23 @@ model_list:
 
 ## Custom Input/Output Pricing
 
-👉 Head to [Custom Input/Output Pricing](https://docs.litellm.ai/docs/proxy/custom_pricing) to setup custom pricing or your models
+👉 Head to [Custom Input/Output Pricing](https://docs.litellm.ai/docs/proxy/custom_pricing) to setup custom pricing or your models
+
+## ✨ Custom k,v pairs
+
+Log specific key,value pairs as part of the metadata for a spend log
+
+:::info 
+
+Logging specific key,value pairs in spend logs metadata is an enterprise feature. [See here](./enterprise.md#tracking-spend-with-custom-metadata)
+
+:::
+
+
+## ✨ Custom Tags
+
+:::info 
+
+Tracking spend with Custom tags is an enterprise feature. [See here](./enterprise.md#tracking-spend-for-custom-tags)
+
+:::

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

@@ -205,6 +205,146 @@ curl -X GET "http://0.0.0.0:4000/spend/tags" \
 ```
 
 
+## Tracking Spend with custom metadata
+
+Requirements: 
+
+- Virtual Keys & a database should be set up, see [virtual keys](https://docs.litellm.ai/docs/proxy/virtual_keys)
+
+#### Usage - /chat/completions requests with special spend logs metadata 
+
+
+<Tabs>
+
+
+<TabItem value="openai" label="OpenAI Python v1.0.0+">
+
+Set `extra_body={"metadata": { }}` to `metadata` you want to pass
+
+```python
+import openai
+client = openai.OpenAI(
+    api_key="anything",
+    base_url="http://0.0.0.0:4000"
+)
+
+# request sent to model set on litellm proxy, `litellm --model`
+response = client.chat.completions.create(
+    model="gpt-3.5-turbo",
+    messages = [
+        {
+            "role": "user",
+            "content": "this is a test request, write a short poem"
+        }
+    ],
+    extra_body={
+        "metadata": {
+            "spend_logs_metadata": {
+                "hello": "world"
+            }
+        }
+    }
+)
+
+print(response)
+```
+</TabItem>
+
+<TabItem value="Curl" label="Curl Request">
+
+Pass `metadata` as part of the request body
+
+```shell
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+    --header 'Content-Type: application/json' \
+    --data '{
+    "model": "gpt-3.5-turbo",
+    "messages": [
+        {
+        "role": "user",
+        "content": "what llm are you"
+        }
+    ],
+    "metadata": {
+        "spend_logs_metadata": {
+            "hello": "world"
+        }
+    }
+}'
+```
+</TabItem>
+<TabItem value="langchain" label="Langchain">
+
+```python
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
+)
+from langchain.schema import HumanMessage, SystemMessage
+
+chat = ChatOpenAI(
+    openai_api_base="http://0.0.0.0:4000",
+    model = "gpt-3.5-turbo",
+    temperature=0.1,
+    extra_body={
+        "metadata": {
+            "spend_logs_metadata": {
+                "hello": "world"
+            }
+        }
+    }
+)
+
+messages = [
+    SystemMessage(
+        content="You are a helpful assistant that im using to make a test request to."
+    ),
+    HumanMessage(
+        content="test from litellm. tell me why it's amazing in 1 sentence"
+    ),
+]
+response = chat(messages)
+
+print(response)
+```
+
+</TabItem>
+</Tabs>
+
+
+#### Viewing Spend w/ custom metadata
+
+#### `/spend/logs` Request Format 
+
+```bash
+curl -X GET "http://0.0.0.0:4000/spend/logs?request_id=<your-call-id" \ # e.g.: chatcmpl-9ZKMURhVYSi9D6r6PJ9vLcayIK0Vm
+-H "Authorization: Bearer sk-1234"
+```
+
+#### `/spend/logs` Response Format
+```bash
+[
+    {
+        "request_id": "chatcmpl-9ZKMURhVYSi9D6r6PJ9vLcayIK0Vm",
+        "call_type": "acompletion",
+        "metadata": {
+            "user_api_key": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
+            "user_api_key_alias": null,
+            "spend_logs_metadata": { # 👈 LOGGED CUSTOM METADATA
+                "hello": "world"
+            },
+            "user_api_key_team_id": null,
+            "user_api_key_user_id": "116544810872468347480",
+            "user_api_key_team_alias": null
+        },
+    }
+]
+```
+
+
+
 ## Enforce Required Params for LLM Requests
 Use this when you want to enforce all requests to include certain params. Example you need all requests to include the `user` and `["metadata]["generation_name"]` params.