v1.73.6.rc

.circleci/config.yml

@@ -1358,6 +1358,7 @@ jobs:
       # - run: python ./tests/documentation_tests/test_general_setting_keys.py
       - run: python ./tests/code_coverage_tests/check_licenses.py
       - run: python ./tests/code_coverage_tests/router_code_coverage.py
+      - run: python ./tests/code_coverage_tests/test_proxy_types_import.py
       - run: python ./tests/code_coverage_tests/callback_manager_test.py
       - run: python ./tests/code_coverage_tests/recursive_detector.py
       - run: python ./tests/code_coverage_tests/test_router_strategy_async.py

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

@@ -255,6 +255,198 @@ curl -L -X GET 'http://localhost:4000/user/daily/activity?start_date=2025-03-20&
 
 See our [Swagger API](https://litellm-api.up.railway.app/#/Budget%20%26%20Spend%20Tracking/get_user_daily_activity_user_daily_activity_get) for more details on the `/user/daily/activity` endpoint
 
+## Custom Tags
+
+Requirements: 
+
+- Virtual Keys & a database should be set up, see [virtual keys](https://docs.litellm.ai/docs/proxy/virtual_keys)
+
+**Note:** By default, LiteLLM will track `User-Agent` as a custom tag for cost tracking. This enables viewing usage for tools like Claude Code, Gemini CLI, etc. 
+
+
+<Image img={require('../../img/claude_cli_tag_usage.png')} />
+
+
+### Client-side spend tag
+
+<Tabs>
+<TabItem value="key" label="Set on Key">
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+    "metadata": {
+        "tags": ["tag1", "tag2", "tag3"]
+    }
+}
+
+'
+```
+
+</TabItem>
+<TabItem value="team" label="Set on Team">
+
+```bash
+curl -L -X POST 'http://0.0.0.0:4000/team/new' \
+-H 'Authorization: Bearer sk-1234' \
+-H 'Content-Type: application/json' \
+-d '{
+    "metadata": {
+        "tags": ["tag1", "tag2", "tag3"]
+    }
+}
+
+'
+```
+
+</TabItem>
+<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"
+)
+
+
+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": {
+            "tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"] # 👈 Key Change
+        }
+    }
+)
+
+print(response)
+```
+</TabItem>
+
+
+<TabItem value="openai js" label="OpenAI JS">
+
+```js
+const openai = require('openai');
+
+async function runOpenAI() {
+  const client = new openai.OpenAI({
+    apiKey: 'sk-1234',
+    baseURL: 'http://0.0.0.0:4000'
+  });
+
+  try {
+    const response = await client.chat.completions.create({
+      model: 'gpt-3.5-turbo',
+      messages: [
+        {
+          role: 'user',
+          content: "this is a test request, write a short poem"
+        },
+      ],
+      metadata: {
+        tags: ["model-anthropic-claude-v2.1", "app-ishaan-prod"] // 👈 Key Change
+      }
+    });
+    console.log(response);
+  } catch (error) {
+    console.log("got this exception from server");
+    console.error(error);
+  }
+}
+
+// Call the asynchronous function
+runOpenAI();
+```
+</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": {"tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"]}
+}'
+```
+</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": {
+            "tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"]
+        }
+    }
+)
+
+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>
+
+
+
+### Add custom headers to spend tracking
+
+You can add custom headers to the request to track spend and usage.
+
+```yaml
+litellm_settings:
+  extra_spend_tag_headers:
+    - "x-custom-header"
+```
+
+### Disable user-agent tracking
+
+You can disable user-agent tracking by setting `litellm_settings.disable_user_agent_tracking` to `true`.
+
+```yaml
+litellm_settings:
+  disable_user_agent_tracking: true
+```
 ## ✨ (Enterprise) Generate Spend Reports 
 
 Use this to charge other teams, customers, users
@@ -617,11 +809,5 @@ Logging specific key,value pairs in spend logs metadata is an enterprise feature
 :::
 
 
-## ✨ 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/custom_root_ui.md

@@ -12,6 +12,9 @@ Requires v1.72.3 or higher.
 
 :::
 
+Limitations:
+- This does not work in [litellm non-root](./deploy#non-root---without-internet-connection) images, as it requires write access to the UI files.
+
 ## Usage
 
 ### 1. Set `SERVER_ROOT_PATH` in your .env