mirror of
https://github.com/tiennm99/litellm.git
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/v1/messages - Remove hardcoded model name on streaming + Tags - enable setting custom header tags (#12131)
* fix(anthropic/experimental_pass_through): use given model name when returning streaming chunks don't harcode model name on streaming confusing for user * fix(anthropic/streaming_iterator.py): remove scope of import * feat(litellm_logging.py): allow admin to specify additional headers for using as spend tags Closes https://github.com/BerriAI/litellm/issues/12129 * test(test_litellm_logging.py): add unit tests * feat(openweb_ui.md): add custom tag tutorial to docs * docs(cost_tracking.md): add tag based usage UI screenshot * test: update test * fix: fix import
This commit is contained in:
Krish Dholakia
@@ -255,6 +255,198 @@ curl -L -X GET 'http://localhost:4000/user/daily/activity?start_date=2025-03-20&
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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
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## Custom Tags
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Requirements:
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- Virtual Keys & a database should be set up, see [virtual keys](https://docs.litellm.ai/docs/proxy/virtual_keys)
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**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.
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<Image img={require('../../img/claude_cli_tag_usage.png')} />
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### Client-side spend tag
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<Tabs>
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<TabItem value="key" label="Set on Key">
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```bash
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curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
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-H 'Authorization: Bearer sk-1234' \
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-H 'Content-Type: application/json' \
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-d '{
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"metadata": {
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"tags": ["tag1", "tag2", "tag3"]
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}
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}
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'
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```
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</TabItem>
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<TabItem value="team" label="Set on Team">
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```bash
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curl -L -X POST 'http://0.0.0.0:4000/team/new' \
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-H 'Authorization: Bearer sk-1234' \
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-H 'Content-Type: application/json' \
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-d '{
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"metadata": {
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"tags": ["tag1", "tag2", "tag3"]
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}
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}
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'
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```
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</TabItem>
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<TabItem value="openai" label="OpenAI Python v1.0.0+">
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Set `extra_body={"metadata": { }}` to `metadata` you want to pass
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```python
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import openai
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client = openai.OpenAI(
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api_key="anything",
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base_url="http://0.0.0.0:4000"
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)
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response = client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages = [
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{
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"role": "user",
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"content": "this is a test request, write a short poem"
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}
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],
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extra_body={
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"metadata": {
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"tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"] # 👈 Key Change
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}
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}
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)
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print(response)
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```
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</TabItem>
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<TabItem value="openai js" label="OpenAI JS">
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```js
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const openai = require('openai');
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async function runOpenAI() {
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const client = new openai.OpenAI({
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apiKey: 'sk-1234',
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baseURL: 'http://0.0.0.0:4000'
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});
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try {
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const response = await client.chat.completions.create({
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model: 'gpt-3.5-turbo',
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messages: [
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{
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role: 'user',
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content: "this is a test request, write a short poem"
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},
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],
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metadata: {
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tags: ["model-anthropic-claude-v2.1", "app-ishaan-prod"] // 👈 Key Change
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}
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});
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console.log(response);
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} catch (error) {
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console.log("got this exception from server");
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console.error(error);
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}
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}
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// Call the asynchronous function
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runOpenAI();
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```
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</TabItem>
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<TabItem value="Curl" label="Curl Request">
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Pass `metadata` as part of the request body
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```shell
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curl --location 'http://0.0.0.0:4000/chat/completions' \
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--header 'Content-Type: application/json' \
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--data '{
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"model": "gpt-3.5-turbo",
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"messages": [
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{
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"role": "user",
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"content": "what llm are you"
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}
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],
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"metadata": {"tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"]}
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}'
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```
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</TabItem>
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<TabItem value="langchain" label="Langchain">
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```python
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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SystemMessagePromptTemplate,
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)
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from langchain.schema import HumanMessage, SystemMessage
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chat = ChatOpenAI(
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openai_api_base="http://0.0.0.0:4000",
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model = "gpt-3.5-turbo",
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temperature=0.1,
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extra_body={
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"metadata": {
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"tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"]
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}
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}
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)
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messages = [
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SystemMessage(
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content="You are a helpful assistant that im using to make a test request to."
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),
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HumanMessage(
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content="test from litellm. tell me why it's amazing in 1 sentence"
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),
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]
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response = chat(messages)
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print(response)
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```
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</TabItem>
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</Tabs>
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### Add custom headers to spend tracking
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You can add custom headers to the request to track spend and usage.
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```yaml
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litellm_settings:
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extra_spend_tag_headers:
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- "x-custom-header"
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```
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### Disable user-agent tracking
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You can disable user-agent tracking by setting `litellm_settings.disable_user_agent_tracking` to `true`.
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```yaml
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litellm_settings:
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disable_user_agent_tracking: true
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```
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## ✨ (Enterprise) Generate Spend Reports
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Use this to charge other teams, customers, users
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@@ -617,11 +809,5 @@ Logging specific key,value pairs in spend logs metadata is an enterprise feature
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:::
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## ✨ Custom Tags
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:::info
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Tracking spend with Custom tags is an enterprise feature. [See here](./enterprise.md#tracking-spend-for-custom-tags)
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:::
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@@ -29,7 +29,6 @@ Features:
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- ✅ [Team Based Logging](./team_logging.md) - Allow each team to use their own Langfuse Project / custom callbacks
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- ✅ [Disable Logging for a Team](./team_logging.md#disable-logging-for-a-team) - Switch off all logging for a team/project (GDPR Compliance)
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- **Spend Tracking & Data Exports**
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- ✅ [Tracking Spend for Custom Tags](#tracking-spend-for-custom-tags)
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- ✅ [Set USD Budgets Spend for Custom Tags](./provider_budget_routing#-tag-budgets)
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- ✅ [Set Model budgets for Virtual Keys](./users#-virtual-key-model-specific)
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- ✅ [Exporting LLM Logs to GCS Bucket, Azure Blob Storage](./proxy/bucket#🪣-logging-gcs-s3-buckets)
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@@ -332,174 +331,6 @@ curl --location 'http://0.0.0.0:4000/embeddings' \
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## Spend Tracking
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### Custom Tags
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Requirements:
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- Virtual Keys & a database should be set up, see [virtual keys](https://docs.litellm.ai/docs/proxy/virtual_keys)
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#### Usage - /chat/completions requests with request tags
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<Tabs>
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<TabItem value="key" label="Set on Key">
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|
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```bash
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curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
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-H 'Authorization: Bearer sk-1234' \
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-H 'Content-Type: application/json' \
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-d '{
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"metadata": {
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"tags": ["tag1", "tag2", "tag3"]
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}
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}
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'
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```
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</TabItem>
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<TabItem value="team" label="Set on Team">
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```bash
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curl -L -X POST 'http://0.0.0.0:4000/team/new' \
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-H 'Authorization: Bearer sk-1234' \
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-H 'Content-Type: application/json' \
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-d '{
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"metadata": {
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"tags": ["tag1", "tag2", "tag3"]
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}
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}
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'
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```
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</TabItem>
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<TabItem value="openai" label="OpenAI Python v1.0.0+">
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Set `extra_body={"metadata": { }}` to `metadata` you want to pass
|
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|
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```python
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import openai
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client = openai.OpenAI(
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api_key="anything",
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base_url="http://0.0.0.0:4000"
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)
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response = client.chat.completions.create(
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model="gpt-3.5-turbo",
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messages = [
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{
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"role": "user",
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"content": "this is a test request, write a short poem"
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}
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],
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extra_body={
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"metadata": {
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"tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"] # 👈 Key Change
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}
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}
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)
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print(response)
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```
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</TabItem>
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<TabItem value="openai js" label="OpenAI JS">
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```js
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const openai = require('openai');
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async function runOpenAI() {
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const client = new openai.OpenAI({
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apiKey: 'sk-1234',
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baseURL: 'http://0.0.0.0:4000'
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});
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try {
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const response = await client.chat.completions.create({
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model: 'gpt-3.5-turbo',
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messages: [
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{
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role: 'user',
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content: "this is a test request, write a short poem"
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},
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],
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metadata: {
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tags: ["model-anthropic-claude-v2.1", "app-ishaan-prod"] // 👈 Key Change
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}
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});
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console.log(response);
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} catch (error) {
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console.log("got this exception from server");
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console.error(error);
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}
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}
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// Call the asynchronous function
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runOpenAI();
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```
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</TabItem>
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<TabItem value="Curl" label="Curl Request">
|
||||
|
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Pass `metadata` as part of the request body
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|
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```shell
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curl --location 'http://0.0.0.0:4000/chat/completions' \
|
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--header 'Content-Type: application/json' \
|
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--data '{
|
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"model": "gpt-3.5-turbo",
|
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"messages": [
|
||||
{
|
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"role": "user",
|
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"content": "what llm are you"
|
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}
|
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],
|
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"metadata": {"tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"]}
|
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}'
|
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```
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</TabItem>
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<TabItem value="langchain" label="Langchain">
|
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|
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```python
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from langchain.chat_models import ChatOpenAI
|
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from langchain.prompts.chat import (
|
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ChatPromptTemplate,
|
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HumanMessagePromptTemplate,
|
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SystemMessagePromptTemplate,
|
||||
)
|
||||
from langchain.schema import HumanMessage, SystemMessage
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|
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chat = ChatOpenAI(
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openai_api_base="http://0.0.0.0:4000",
|
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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>
|
||||
|
||||
|
||||
#### Viewing Spend per tag
|
||||
|
||||
#### `/spend/tags` Request Format
|
||||
|
||||
@@ -124,7 +124,6 @@ Expected response on failure:
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
|
||||
<TabItem label="Successful Call" value="allowed">
|
||||
|
||||
```shell
|
||||
|
||||
@@ -135,3 +135,18 @@ On the models dropdown select `thinking-anthropic-claude-3-7-sonnet`
|
||||
|
||||
## Additional Resources
|
||||
- Running LiteLLM and Open WebUI on Windows Localhost: A Comprehensive Guide [https://www.tanyongsheng.com/note/running-litellm-and-openwebui-on-windows-localhost-a-comprehensive-guide/](https://www.tanyongsheng.com/note/running-litellm-and-openwebui-on-windows-localhost-a-comprehensive-guide/)
|
||||
|
||||
|
||||
## 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"
|
||||
```
|
||||
|
||||
You can add custom headers to the request to track spend and usage.
|
||||
|
||||
<Image img={require('../../img/custom_tag_headers.png')} />
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 266 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 290 KiB |
+6
-2
@@ -218,6 +218,7 @@ disable_streaming_logging: bool = False
|
||||
disable_token_counter: bool = False
|
||||
disable_add_transform_inline_image_block: bool = False
|
||||
disable_add_user_agent_to_request_tags: bool = False
|
||||
extra_spend_tag_headers: Optional[List[str]] = None
|
||||
in_memory_llm_clients_cache: LLMClientCache = LLMClientCache()
|
||||
safe_memory_mode: bool = False
|
||||
enable_azure_ad_token_refresh: Optional[bool] = False
|
||||
@@ -320,9 +321,11 @@ priority_reservation: Optional[Dict[str, float]] = None
|
||||
use_aiohttp_transport: bool = (
|
||||
True # Older variable, aiohttp is now the default. use disable_aiohttp_transport instead.
|
||||
)
|
||||
aiohttp_trust_env: bool = False # set to true to use HTTP_ Proxy settings
|
||||
aiohttp_trust_env: bool = False # set to true to use HTTP_ Proxy settings
|
||||
disable_aiohttp_transport: bool = False # Set this to true to use httpx instead
|
||||
disable_aiohttp_trust_env: bool = False # When False, aiohttp will respect HTTP(S)_PROXY env vars
|
||||
disable_aiohttp_trust_env: bool = (
|
||||
False # When False, aiohttp will respect HTTP(S)_PROXY env vars
|
||||
)
|
||||
force_ipv4: bool = (
|
||||
False # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
|
||||
)
|
||||
@@ -1154,6 +1157,7 @@ from .fine_tuning.main import *
|
||||
from .files.main import *
|
||||
from .scheduler import *
|
||||
from .cost_calculator import response_cost_calculator, cost_per_token
|
||||
|
||||
### ADAPTERS ###
|
||||
from .types.adapter import AdapterItem
|
||||
import litellm.anthropic_interface as anthropic
|
||||
|
||||
@@ -26,50 +26,53 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper):
|
||||
Wrapper for streaming Google GenAI generate_content responses.
|
||||
Transforms OpenAI streaming chunks to Google GenAI format.
|
||||
"""
|
||||
|
||||
|
||||
sent_first_chunk: bool = False
|
||||
# State tracking for accumulating partial tool calls
|
||||
accumulated_tool_calls: Dict[str, Dict[str, Any]]
|
||||
|
||||
|
||||
def __init__(self, completion_stream: Any):
|
||||
super().__init__(completion_stream)
|
||||
self.sent_first_chunk = False
|
||||
self.accumulated_tool_calls = {}
|
||||
|
||||
|
||||
def __next__(self):
|
||||
try:
|
||||
for chunk in self.completion_stream:
|
||||
if chunk == "None" or chunk is None:
|
||||
continue
|
||||
|
||||
|
||||
# Transform OpenAI streaming chunk to Google GenAI format
|
||||
transformed_chunk = GoogleGenAIAdapter().translate_streaming_completion_to_generate_content(chunk, self)
|
||||
transformed_chunk = GoogleGenAIAdapter().translate_streaming_completion_to_generate_content(
|
||||
chunk, self
|
||||
)
|
||||
if transformed_chunk: # Only return non-empty chunks
|
||||
return transformed_chunk
|
||||
|
||||
|
||||
raise StopIteration
|
||||
except StopIteration:
|
||||
raise StopIteration
|
||||
except Exception:
|
||||
raise StopIteration
|
||||
|
||||
|
||||
async def __anext__(self):
|
||||
try:
|
||||
async for chunk in self.completion_stream:
|
||||
if chunk == "None" or chunk is None:
|
||||
continue
|
||||
|
||||
# Transform OpenAI streaming chunk to Google GenAI format
|
||||
transformed_chunk = GoogleGenAIAdapter().translate_streaming_completion_to_generate_content(chunk, self)
|
||||
|
||||
# Transform OpenAI streaming chunk to Google GenAI format
|
||||
transformed_chunk = GoogleGenAIAdapter().translate_streaming_completion_to_generate_content(
|
||||
chunk, self
|
||||
)
|
||||
if transformed_chunk: # Only return non-empty chunks
|
||||
return transformed_chunk
|
||||
|
||||
|
||||
raise StopAsyncIteration
|
||||
except StopAsyncIteration:
|
||||
raise StopAsyncIteration
|
||||
except Exception:
|
||||
raise StopAsyncIteration
|
||||
|
||||
|
||||
def google_genai_sse_wrapper(self) -> Iterator[bytes]:
|
||||
"""
|
||||
Convert Google GenAI streaming chunks to Server-Sent Events format.
|
||||
@@ -80,7 +83,7 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper):
|
||||
yield payload.encode()
|
||||
else:
|
||||
yield chunk
|
||||
|
||||
|
||||
async def async_google_genai_sse_wrapper(self) -> AsyncIterator[bytes]:
|
||||
"""
|
||||
Async version of google_genai_sse_wrapper.
|
||||
@@ -95,40 +98,39 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper):
|
||||
|
||||
class GoogleGenAIAdapter:
|
||||
"""Adapter for transforming Google GenAI generate_content requests to/from litellm.completion format"""
|
||||
|
||||
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
|
||||
def translate_generate_content_to_completion(
|
||||
self,
|
||||
model: str,
|
||||
contents: Union[List[Dict[str, Any]], Dict[str, Any]],
|
||||
config: Optional[Dict[str, Any]] = None,
|
||||
**kwargs
|
||||
**kwargs,
|
||||
) -> ChatCompletionRequest:
|
||||
"""
|
||||
Transform generate_content request to litellm completion format
|
||||
|
||||
|
||||
Args:
|
||||
model: The model name
|
||||
contents: Generate content contents (can be list or single dict)
|
||||
config: Optional config parameters
|
||||
**kwargs: Additional parameters
|
||||
|
||||
|
||||
Returns:
|
||||
ChatCompletionRequest in OpenAI format
|
||||
"""
|
||||
|
||||
|
||||
# Normalize contents to list format
|
||||
if isinstance(contents, dict):
|
||||
contents_list = [contents]
|
||||
else:
|
||||
contents_list = contents
|
||||
|
||||
|
||||
# Transform contents to OpenAI messages format
|
||||
messages = self._transform_contents_to_messages(contents_list)
|
||||
|
||||
|
||||
# Create base request
|
||||
completion_request: ChatCompletionRequest = ChatCompletionRequest(
|
||||
model=model,
|
||||
@@ -146,7 +148,7 @@ class GoogleGenAIAdapter:
|
||||
# - tools
|
||||
# - tool_choice
|
||||
#########################################################
|
||||
|
||||
|
||||
# Add config parameters if provided
|
||||
if config:
|
||||
# Map common Google GenAI config parameters to OpenAI equivalents
|
||||
@@ -161,89 +163,91 @@ class GoogleGenAIAdapter:
|
||||
pass
|
||||
if "stopSequences" in config:
|
||||
completion_request["stop"] = config["stopSequences"]
|
||||
|
||||
|
||||
# Handle tools transformation
|
||||
if "tools" in kwargs:
|
||||
tools = kwargs["tools"]
|
||||
|
||||
|
||||
# Check if tools are already in OpenAI format or Google GenAI format
|
||||
if isinstance(tools, list) and len(tools) > 0:
|
||||
# Tools are in Google GenAI format, transform them
|
||||
openai_tools = self._transform_google_genai_tools_to_openai(tools)
|
||||
if openai_tools:
|
||||
completion_request["tools"] = openai_tools
|
||||
|
||||
|
||||
# Handle tool_config (tool choice)
|
||||
if "tool_config" in kwargs:
|
||||
tool_choice = self._transform_google_genai_tool_config_to_openai(kwargs["tool_config"])
|
||||
tool_choice = self._transform_google_genai_tool_config_to_openai(
|
||||
kwargs["tool_config"]
|
||||
)
|
||||
if tool_choice:
|
||||
completion_request["tool_choice"] = tool_choice
|
||||
|
||||
|
||||
return completion_request
|
||||
|
||||
def translate_completion_output_params_streaming(
|
||||
self, completion_stream: Any
|
||||
) -> Union[AsyncIterator[bytes], None]:
|
||||
"""Transform streaming completion output to Google GenAI format"""
|
||||
google_genai_wrapper = GoogleGenAIStreamWrapper(completion_stream=completion_stream)
|
||||
google_genai_wrapper = GoogleGenAIStreamWrapper(
|
||||
completion_stream=completion_stream
|
||||
)
|
||||
# Return the SSE-wrapped version for proper event formatting
|
||||
return google_genai_wrapper.async_google_genai_sse_wrapper()
|
||||
|
||||
def _transform_google_genai_tools_to_openai(self, tools: List[Dict[str, Any]]) -> List[ChatCompletionToolParam]:
|
||||
def _transform_google_genai_tools_to_openai(
|
||||
self, tools: List[Dict[str, Any]]
|
||||
) -> List[ChatCompletionToolParam]:
|
||||
"""Transform Google GenAI tools to OpenAI tools format"""
|
||||
openai_tools: List[Dict[str, Any]] = []
|
||||
|
||||
|
||||
for tool in tools:
|
||||
if "functionDeclarations" in tool:
|
||||
for func_decl in tool["functionDeclarations"]:
|
||||
function_chunk: Dict[str, Any] = {
|
||||
"name": func_decl.get("name", ""),
|
||||
}
|
||||
|
||||
|
||||
if "description" in func_decl:
|
||||
function_chunk["description"] = func_decl["description"]
|
||||
if "parameters" in func_decl:
|
||||
function_chunk["parameters"] = func_decl["parameters"]
|
||||
|
||||
openai_tool = {
|
||||
"type": "function",
|
||||
"function": function_chunk
|
||||
}
|
||||
|
||||
openai_tool = {"type": "function", "function": function_chunk}
|
||||
openai_tools.append(openai_tool)
|
||||
|
||||
|
||||
# normalize the tool schemas
|
||||
normalized_tools = [normalize_tool_schema(tool) for tool in openai_tools]
|
||||
|
||||
|
||||
return cast(List[ChatCompletionToolParam], normalized_tools)
|
||||
|
||||
def _transform_google_genai_tool_config_to_openai(self, tool_config: Dict[str, Any]) -> Optional[ChatCompletionToolChoiceValues]:
|
||||
def _transform_google_genai_tool_config_to_openai(
|
||||
self, tool_config: Dict[str, Any]
|
||||
) -> Optional[ChatCompletionToolChoiceValues]:
|
||||
"""Transform Google GenAI tool_config to OpenAI tool_choice"""
|
||||
function_calling_config = tool_config.get("functionCallingConfig", {})
|
||||
mode = function_calling_config.get("mode", "AUTO")
|
||||
|
||||
mode_mapping = {
|
||||
"AUTO": "auto",
|
||||
"ANY": "required",
|
||||
"NONE": "none"
|
||||
}
|
||||
|
||||
|
||||
mode_mapping = {"AUTO": "auto", "ANY": "required", "NONE": "none"}
|
||||
|
||||
tool_choice = mode_mapping.get(mode, "auto")
|
||||
return cast(ChatCompletionToolChoiceValues, tool_choice)
|
||||
|
||||
def _transform_contents_to_messages(self, contents: List[Dict[str, Any]]) -> List[AllMessageValues]:
|
||||
def _transform_contents_to_messages(
|
||||
self, contents: List[Dict[str, Any]]
|
||||
) -> List[AllMessageValues]:
|
||||
"""Transform Google GenAI contents to OpenAI messages format"""
|
||||
messages: List[AllMessageValues] = []
|
||||
|
||||
|
||||
for content in contents:
|
||||
role = content.get("role", "user")
|
||||
parts = content.get("parts", [])
|
||||
|
||||
|
||||
if role == "user":
|
||||
# Handle user messages with potential function responses
|
||||
combined_text = ""
|
||||
tool_messages: List[ChatCompletionToolMessage] = []
|
||||
|
||||
|
||||
for part in parts:
|
||||
if isinstance(part, dict):
|
||||
if "text" in part:
|
||||
@@ -254,27 +258,26 @@ class GoogleGenAIAdapter:
|
||||
tool_message = ChatCompletionToolMessage(
|
||||
role="tool",
|
||||
tool_call_id=f"call_{func_response.get('name', 'unknown')}",
|
||||
content=json.dumps(func_response.get("response", {}))
|
||||
content=json.dumps(func_response.get("response", {})),
|
||||
)
|
||||
tool_messages.append(tool_message)
|
||||
elif isinstance(part, str):
|
||||
combined_text += part
|
||||
|
||||
|
||||
# Add user message if there's text content
|
||||
if combined_text:
|
||||
messages.append(ChatCompletionUserMessage(
|
||||
role="user",
|
||||
content=combined_text
|
||||
))
|
||||
|
||||
messages.append(
|
||||
ChatCompletionUserMessage(role="user", content=combined_text)
|
||||
)
|
||||
|
||||
# Add tool messages
|
||||
messages.extend(tool_messages)
|
||||
|
||||
|
||||
elif role == "model":
|
||||
# Handle assistant messages with potential function calls
|
||||
combined_text = ""
|
||||
tool_calls: List[ChatCompletionAssistantToolCall] = []
|
||||
|
||||
|
||||
for part in parts:
|
||||
if isinstance(part, dict):
|
||||
if "text" in part:
|
||||
@@ -287,28 +290,28 @@ class GoogleGenAIAdapter:
|
||||
type="function",
|
||||
function=ChatCompletionToolCallFunctionChunk(
|
||||
name=func_call.get("name", ""),
|
||||
arguments=json.dumps(func_call.get("args", {}))
|
||||
)
|
||||
arguments=json.dumps(func_call.get("args", {})),
|
||||
),
|
||||
)
|
||||
tool_calls.append(tool_call)
|
||||
elif isinstance(part, str):
|
||||
combined_text += part
|
||||
|
||||
|
||||
# Create assistant message
|
||||
if tool_calls:
|
||||
assistant_message = ChatCompletionAssistantMessage(
|
||||
role="assistant",
|
||||
content=combined_text if combined_text else None,
|
||||
tool_calls=tool_calls
|
||||
tool_calls=tool_calls,
|
||||
)
|
||||
else:
|
||||
assistant_message = ChatCompletionAssistantMessage(
|
||||
role="assistant",
|
||||
content=combined_text if combined_text else None
|
||||
content=combined_text if combined_text else None,
|
||||
)
|
||||
|
||||
|
||||
messages.append(assistant_message)
|
||||
|
||||
|
||||
return messages
|
||||
|
||||
def translate_completion_to_generate_content(
|
||||
@@ -316,57 +319,62 @@ class GoogleGenAIAdapter:
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Transform litellm completion response to Google GenAI generate_content format
|
||||
|
||||
|
||||
Args:
|
||||
response: ModelResponse from litellm.completion
|
||||
|
||||
|
||||
Returns:
|
||||
Dict in Google GenAI generate_content response format
|
||||
"""
|
||||
|
||||
|
||||
# Extract the main response content
|
||||
choice = response.choices[0] if response.choices else None
|
||||
if not choice:
|
||||
raise ValueError("Invalid completion response: no choices found")
|
||||
|
||||
|
||||
# Handle different choice types (Choices vs StreamingChoices)
|
||||
if isinstance(choice, Choices):
|
||||
if not choice.message:
|
||||
raise ValueError("Invalid completion response: no message found in choice")
|
||||
raise ValueError(
|
||||
"Invalid completion response: no message found in choice"
|
||||
)
|
||||
parts = self._transform_openai_message_to_google_genai_parts(choice.message)
|
||||
elif isinstance(choice, StreamingChoices):
|
||||
if not choice.delta:
|
||||
raise ValueError("Invalid completion response: no delta found in streaming choice")
|
||||
raise ValueError(
|
||||
"Invalid completion response: no delta found in streaming choice"
|
||||
)
|
||||
parts = self._transform_openai_delta_to_google_genai_parts(choice.delta)
|
||||
else:
|
||||
# Fallback for generic choice objects
|
||||
message_content = getattr(choice, 'message', {}).get('content', '') or getattr(choice, 'delta', {}).get('content', '')
|
||||
message_content = getattr(choice, "message", {}).get(
|
||||
"content", ""
|
||||
) or getattr(choice, "delta", {}).get("content", "")
|
||||
parts = [{"text": message_content}] if message_content else []
|
||||
|
||||
|
||||
# Create Google GenAI format response
|
||||
generate_content_response: Dict[str, Any] = {
|
||||
"candidates": [
|
||||
{
|
||||
"content": {
|
||||
"parts": parts,
|
||||
"role": "model"
|
||||
},
|
||||
"finishReason": self._map_finish_reason(getattr(choice, 'finish_reason', None)),
|
||||
"content": {"parts": parts, "role": "model"},
|
||||
"finishReason": self._map_finish_reason(
|
||||
getattr(choice, "finish_reason", None)
|
||||
),
|
||||
"index": 0,
|
||||
"safetyRatings": []
|
||||
"safetyRatings": [],
|
||||
}
|
||||
],
|
||||
"usageMetadata": (
|
||||
self._map_usage(getattr(response, 'usage', None))
|
||||
if hasattr(response, 'usage') and getattr(response, 'usage', None)
|
||||
self._map_usage(getattr(response, "usage", None))
|
||||
if hasattr(response, "usage") and getattr(response, "usage", None)
|
||||
else {
|
||||
"promptTokenCount": 0,
|
||||
"candidatesTokenCount": 0,
|
||||
"totalTokenCount": 0
|
||||
"totalTokenCount": 0,
|
||||
}
|
||||
)
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
# Add text field for convenience (common in Google GenAI responses)
|
||||
text_content = ""
|
||||
for part in parts:
|
||||
@@ -374,7 +382,7 @@ class GoogleGenAIAdapter:
|
||||
text_content += part["text"]
|
||||
if text_content:
|
||||
generate_content_response["text"] = text_content
|
||||
|
||||
|
||||
return generate_content_response
|
||||
|
||||
def translate_streaming_completion_to_generate_content(
|
||||
@@ -382,62 +390,69 @@ class GoogleGenAIAdapter:
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Transform streaming litellm completion chunk to Google GenAI generate_content format
|
||||
|
||||
|
||||
Args:
|
||||
response: Streaming ModelResponse chunk from litellm.completion
|
||||
wrapper: GoogleGenAIStreamWrapper instance
|
||||
|
||||
|
||||
Returns:
|
||||
Dict in Google GenAI streaming generate_content response format
|
||||
"""
|
||||
|
||||
|
||||
# Extract the main response content from streaming chunk
|
||||
choice = response.choices[0] if response.choices else None
|
||||
if not choice:
|
||||
# Return empty chunk if no choices
|
||||
return {}
|
||||
|
||||
|
||||
# Handle streaming choice
|
||||
if isinstance(choice, StreamingChoices):
|
||||
if choice.delta:
|
||||
parts = self._transform_openai_delta_to_google_genai_parts_with_accumulation(choice.delta, wrapper)
|
||||
parts = self._transform_openai_delta_to_google_genai_parts_with_accumulation(
|
||||
choice.delta, wrapper
|
||||
)
|
||||
else:
|
||||
parts = []
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
finish_reason = getattr(choice, "finish_reason", None)
|
||||
else:
|
||||
# Fallback for generic choice objects
|
||||
message_content = getattr(choice, 'delta', {}).get('content', '')
|
||||
message_content = getattr(choice, "delta", {}).get("content", "")
|
||||
parts = [{"text": message_content}] if message_content else []
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
|
||||
finish_reason = getattr(choice, "finish_reason", None)
|
||||
|
||||
# Only create response chunk if we have parts or it's the final chunk
|
||||
if not parts and not finish_reason:
|
||||
return {}
|
||||
|
||||
|
||||
# Create Google GenAI streaming format response
|
||||
streaming_chunk: Dict[str, Any] = {
|
||||
"candidates": [
|
||||
{
|
||||
"content": {
|
||||
"parts": parts,
|
||||
"role": "model"
|
||||
},
|
||||
"finishReason": self._map_finish_reason(finish_reason) if finish_reason else None,
|
||||
"content": {"parts": parts, "role": "model"},
|
||||
"finishReason": (
|
||||
self._map_finish_reason(finish_reason)
|
||||
if finish_reason
|
||||
else None
|
||||
),
|
||||
"index": 0,
|
||||
"safetyRatings": []
|
||||
"safetyRatings": [],
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
|
||||
# Add usage metadata only in the final chunk (when finish_reason is present)
|
||||
if finish_reason:
|
||||
usage_metadata = self._map_usage(getattr(response, 'usage', None)) if hasattr(response, 'usage') and getattr(response, 'usage', None) else {
|
||||
"promptTokenCount": 0,
|
||||
"candidatesTokenCount": 0,
|
||||
"totalTokenCount": 0
|
||||
}
|
||||
usage_metadata = (
|
||||
self._map_usage(getattr(response, "usage", None))
|
||||
if hasattr(response, "usage") and getattr(response, "usage", None)
|
||||
else {
|
||||
"promptTokenCount": 0,
|
||||
"candidatesTokenCount": 0,
|
||||
"totalTokenCount": 0,
|
||||
}
|
||||
)
|
||||
streaming_chunk["usageMetadata"] = usage_metadata
|
||||
|
||||
|
||||
# Add text field for convenience (common in Google GenAI responses)
|
||||
text_content = ""
|
||||
for part in parts:
|
||||
@@ -445,64 +460,69 @@ class GoogleGenAIAdapter:
|
||||
text_content += part["text"]
|
||||
if text_content:
|
||||
streaming_chunk["text"] = text_content
|
||||
|
||||
|
||||
return streaming_chunk
|
||||
|
||||
def _transform_openai_message_to_google_genai_parts(self, message: Any) -> List[Dict[str, Any]]:
|
||||
def _transform_openai_message_to_google_genai_parts(
|
||||
self, message: Any
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Transform OpenAI message to Google GenAI parts format"""
|
||||
parts: List[Dict[str, Any]] = []
|
||||
|
||||
|
||||
# Add text content if present
|
||||
if hasattr(message, 'content') and message.content:
|
||||
if hasattr(message, "content") and message.content:
|
||||
parts.append({"text": message.content})
|
||||
|
||||
|
||||
# Add tool calls if present
|
||||
if hasattr(message, 'tool_calls') and message.tool_calls:
|
||||
if hasattr(message, "tool_calls") and message.tool_calls:
|
||||
for tool_call in message.tool_calls:
|
||||
if hasattr(tool_call, 'function') and tool_call.function:
|
||||
if hasattr(tool_call, "function") and tool_call.function:
|
||||
try:
|
||||
args = json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}
|
||||
args = (
|
||||
json.loads(tool_call.function.arguments)
|
||||
if tool_call.function.arguments
|
||||
else {}
|
||||
)
|
||||
except json.JSONDecodeError:
|
||||
args = {}
|
||||
|
||||
|
||||
function_call_part = {
|
||||
"functionCall": {
|
||||
"name": tool_call.function.name,
|
||||
"args": args
|
||||
}
|
||||
"functionCall": {"name": tool_call.function.name, "args": args}
|
||||
}
|
||||
parts.append(function_call_part)
|
||||
|
||||
|
||||
return parts if parts else [{"text": ""}]
|
||||
|
||||
def _transform_openai_delta_to_google_genai_parts(self, delta: Any) -> List[Dict[str, Any]]:
|
||||
def _transform_openai_delta_to_google_genai_parts(
|
||||
self, delta: Any
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Transform OpenAI delta to Google GenAI parts format for streaming"""
|
||||
parts: List[Dict[str, Any]] = []
|
||||
|
||||
|
||||
# Add text content if present
|
||||
if hasattr(delta, 'content') and delta.content:
|
||||
if hasattr(delta, "content") and delta.content:
|
||||
parts.append({"text": delta.content})
|
||||
|
||||
|
||||
# Add tool calls if present (for streaming tool calls)
|
||||
if hasattr(delta, 'tool_calls') and delta.tool_calls:
|
||||
if hasattr(delta, "tool_calls") and delta.tool_calls:
|
||||
for tool_call in delta.tool_calls:
|
||||
if hasattr(tool_call, 'function') and tool_call.function:
|
||||
if hasattr(tool_call, "function") and tool_call.function:
|
||||
# For streaming, we might get partial function arguments
|
||||
args_str = getattr(tool_call.function, 'arguments', '') or ''
|
||||
args_str = getattr(tool_call.function, "arguments", "") or ""
|
||||
try:
|
||||
args = json.loads(args_str) if args_str else {}
|
||||
except json.JSONDecodeError:
|
||||
# For partial JSON in streaming, return as text for now
|
||||
args = {"partial": args_str}
|
||||
|
||||
|
||||
function_call_part = {
|
||||
"functionCall": {
|
||||
"name": getattr(tool_call.function, 'name', '') or '',
|
||||
"args": args
|
||||
"name": getattr(tool_call.function, "name", "") or "",
|
||||
"args": args,
|
||||
}
|
||||
}
|
||||
parts.append(function_call_part)
|
||||
|
||||
|
||||
return parts
|
||||
|
||||
def _transform_openai_delta_to_google_genai_parts_with_accumulation(
|
||||
@@ -510,74 +530,84 @@ class GoogleGenAIAdapter:
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Transform OpenAI delta to Google GenAI parts format with tool call accumulation"""
|
||||
parts: List[Dict[str, Any]] = []
|
||||
|
||||
|
||||
# Add text content if present
|
||||
if hasattr(delta, 'content') and delta.content:
|
||||
if hasattr(delta, "content") and delta.content:
|
||||
parts.append({"text": delta.content})
|
||||
|
||||
|
||||
# Handle tool calls with accumulation for streaming
|
||||
if hasattr(delta, 'tool_calls') and delta.tool_calls:
|
||||
if hasattr(delta, "tool_calls") and delta.tool_calls:
|
||||
for tool_call in delta.tool_calls:
|
||||
if hasattr(tool_call, 'function') and tool_call.function:
|
||||
tool_call_id = getattr(tool_call, 'id', '') or 'call_unknown'
|
||||
function_name = getattr(tool_call.function, 'name', '') or ''
|
||||
args_str = getattr(tool_call.function, 'arguments', '') or ''
|
||||
|
||||
if hasattr(tool_call, "function") and tool_call.function:
|
||||
tool_call_id = getattr(tool_call, "id", "") or "call_unknown"
|
||||
function_name = getattr(tool_call.function, "name", "") or ""
|
||||
args_str = getattr(tool_call.function, "arguments", "") or ""
|
||||
|
||||
# Initialize accumulation for this tool call if not exists
|
||||
if tool_call_id not in wrapper.accumulated_tool_calls:
|
||||
wrapper.accumulated_tool_calls[tool_call_id] = {
|
||||
'name': '',
|
||||
'arguments': '',
|
||||
'complete': False
|
||||
"name": "",
|
||||
"arguments": "",
|
||||
"complete": False,
|
||||
}
|
||||
|
||||
|
||||
# Accumulate function name if provided
|
||||
if function_name:
|
||||
wrapper.accumulated_tool_calls[tool_call_id]['name'] = function_name
|
||||
|
||||
wrapper.accumulated_tool_calls[tool_call_id][
|
||||
"name"
|
||||
] = function_name
|
||||
|
||||
# Accumulate arguments if provided
|
||||
if args_str:
|
||||
wrapper.accumulated_tool_calls[tool_call_id]['arguments'] += args_str
|
||||
|
||||
wrapper.accumulated_tool_calls[tool_call_id][
|
||||
"arguments"
|
||||
] += args_str
|
||||
|
||||
# Try to parse the accumulated arguments as JSON
|
||||
accumulated_args = wrapper.accumulated_tool_calls[tool_call_id]['arguments']
|
||||
accumulated_args = wrapper.accumulated_tool_calls[tool_call_id][
|
||||
"arguments"
|
||||
]
|
||||
try:
|
||||
if accumulated_args:
|
||||
parsed_args = json.loads(accumulated_args)
|
||||
# JSON is valid, mark as complete and create function call part
|
||||
wrapper.accumulated_tool_calls[tool_call_id]['complete'] = True
|
||||
|
||||
wrapper.accumulated_tool_calls[tool_call_id][
|
||||
"complete"
|
||||
] = True
|
||||
|
||||
function_call_part = {
|
||||
"functionCall": {
|
||||
"name": wrapper.accumulated_tool_calls[tool_call_id]['name'],
|
||||
"args": parsed_args
|
||||
"name": wrapper.accumulated_tool_calls[
|
||||
tool_call_id
|
||||
]["name"],
|
||||
"args": parsed_args,
|
||||
}
|
||||
}
|
||||
parts.append(function_call_part)
|
||||
|
||||
|
||||
# Clean up completed tool call
|
||||
del wrapper.accumulated_tool_calls[tool_call_id]
|
||||
|
||||
|
||||
except json.JSONDecodeError:
|
||||
# JSON is still incomplete, continue accumulating
|
||||
# Don't add to parts yet
|
||||
pass
|
||||
|
||||
|
||||
return parts
|
||||
|
||||
def _map_finish_reason(self, finish_reason: Optional[str]) -> str:
|
||||
"""Map OpenAI finish reasons to Google GenAI finish reasons"""
|
||||
if not finish_reason:
|
||||
return "STOP"
|
||||
|
||||
|
||||
mapping = {
|
||||
"stop": "STOP",
|
||||
"length": "MAX_TOKENS",
|
||||
"length": "MAX_TOKENS",
|
||||
"content_filter": "SAFETY",
|
||||
"tool_calls": "STOP",
|
||||
"function_call": "STOP",
|
||||
}
|
||||
|
||||
|
||||
return mapping.get(finish_reason, "STOP")
|
||||
|
||||
def _map_usage(self, usage: Any) -> Dict[str, int]:
|
||||
@@ -586,4 +616,4 @@ class GoogleGenAIAdapter:
|
||||
"promptTokenCount": getattr(usage, "prompt_tokens", 0) or 0,
|
||||
"candidatesTokenCount": getattr(usage, "completion_tokens", 0) or 0,
|
||||
"totalTokenCount": getattr(usage, "total_tokens", 0) or 0,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -4024,6 +4024,27 @@ class StandardLoggingPayloadSetup:
|
||||
user_agent_tags.append("User-Agent: " + user_agent)
|
||||
return user_agent_tags
|
||||
|
||||
@staticmethod
|
||||
def _get_extra_header_tags(proxy_server_request: dict) -> Optional[List[str]]:
|
||||
"""
|
||||
Extract additional header tags for spend tracking based on config.
|
||||
"""
|
||||
extra_headers: List[str] = litellm.extra_spend_tag_headers or []
|
||||
if not extra_headers:
|
||||
return None
|
||||
|
||||
headers = proxy_server_request.get("headers", {})
|
||||
if not isinstance(headers, dict):
|
||||
return None
|
||||
|
||||
header_tags = []
|
||||
for header_name in extra_headers:
|
||||
header_value = headers.get(header_name)
|
||||
if header_value:
|
||||
header_tags.append(f"{header_name}: {header_value}")
|
||||
|
||||
return header_tags if header_tags else None
|
||||
|
||||
@staticmethod
|
||||
def _get_request_tags(metadata: dict, proxy_server_request: dict) -> List[str]:
|
||||
request_tags = (
|
||||
@@ -4034,8 +4055,13 @@ class StandardLoggingPayloadSetup:
|
||||
user_agent_tags = StandardLoggingPayloadSetup._get_user_agent_tags(
|
||||
proxy_server_request
|
||||
)
|
||||
additional_header_tags = StandardLoggingPayloadSetup._get_extra_header_tags(
|
||||
proxy_server_request
|
||||
)
|
||||
if user_agent_tags is not None:
|
||||
request_tags.extend(user_agent_tags)
|
||||
if additional_header_tags is not None:
|
||||
request_tags.extend(additional_header_tags)
|
||||
return request_tags
|
||||
|
||||
|
||||
|
||||
@@ -153,7 +153,8 @@ class LiteLLMMessagesToCompletionTransformationHandler:
|
||||
if stream:
|
||||
transformed_stream = (
|
||||
ANTHROPIC_ADAPTER.translate_completion_output_params_streaming(
|
||||
completion_response
|
||||
completion_response,
|
||||
model=model,
|
||||
)
|
||||
)
|
||||
if transformed_stream is not None:
|
||||
@@ -239,7 +240,8 @@ class LiteLLMMessagesToCompletionTransformationHandler:
|
||||
if stream:
|
||||
transformed_stream = (
|
||||
ANTHROPIC_ADAPTER.translate_completion_output_params_streaming(
|
||||
completion_response
|
||||
completion_response,
|
||||
model=model,
|
||||
)
|
||||
)
|
||||
if transformed_stream is not None:
|
||||
|
||||
@@ -2,6 +2,7 @@
|
||||
## Translates OpenAI call to Anthropic `/v1/messages` format
|
||||
import json
|
||||
import traceback
|
||||
import uuid
|
||||
from typing import Any, AsyncIterator, Iterator, Optional
|
||||
|
||||
from litellm import verbose_logger
|
||||
@@ -16,6 +17,10 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
|
||||
- finish_reason must map exactly to anthropic reason, else anthropic client won't be able to parse it.
|
||||
"""
|
||||
|
||||
def __init__(self, completion_stream: Any, model: str):
|
||||
super().__init__(completion_stream)
|
||||
self.model = model
|
||||
|
||||
sent_first_chunk: bool = False
|
||||
sent_content_block_start: bool = False
|
||||
sent_content_block_finish: bool = False
|
||||
@@ -31,11 +36,11 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
|
||||
return {
|
||||
"type": "message_start",
|
||||
"message": {
|
||||
"id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY",
|
||||
"id": "msg_{}".format(uuid.uuid4()),
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"content": [],
|
||||
"model": "claude-3-5-sonnet-20240620",
|
||||
"model": self.model,
|
||||
"stop_reason": None,
|
||||
"stop_sequence": None,
|
||||
"usage": UsageDelta(input_tokens=0, output_tokens=0),
|
||||
@@ -100,11 +105,11 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
|
||||
return {
|
||||
"type": "message_start",
|
||||
"message": {
|
||||
"id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY",
|
||||
"id": "msg_{}".format(uuid.uuid4()),
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"content": [],
|
||||
"model": "claude-3-5-sonnet-20240620",
|
||||
"model": self.model,
|
||||
"stop_reason": None,
|
||||
"stop_sequence": None,
|
||||
"usage": UsageDelta(input_tokens=0, output_tokens=0),
|
||||
|
||||
@@ -96,9 +96,11 @@ class AnthropicAdapter:
|
||||
)
|
||||
|
||||
def translate_completion_output_params_streaming(
|
||||
self, completion_stream: Any
|
||||
self, completion_stream: Any, model: str
|
||||
) -> Union[AsyncIterator[bytes], None]:
|
||||
anthropic_wrapper = AnthropicStreamWrapper(completion_stream=completion_stream)
|
||||
anthropic_wrapper = AnthropicStreamWrapper(
|
||||
completion_stream=completion_stream, model=model
|
||||
)
|
||||
# Return the SSE-wrapped version for proper event formatting
|
||||
return anthropic_wrapper.async_anthropic_sse_wrapper()
|
||||
|
||||
|
||||
@@ -115,6 +115,7 @@ class BaseAnthropicMessagesConfig(ABC):
|
||||
self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
|
||||
) -> "BaseLLMException":
|
||||
from litellm.llms.base_llm.chat.transformation import BaseLLMException
|
||||
|
||||
return BaseLLMException(
|
||||
message=error_message, status_code=status_code, headers=headers
|
||||
)
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -1 +1 @@
|
||||
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3:I[12011,["665","static/chunks/3014691f-b7b79b78e27792f3.js","402","static/chunks/402-d418d0a7bf158a22.js","899","static/chunks/899-3dc1e1a0832876e3.js","250","static/chunks/250-b776bd9ac8911291.js","461","static/chunks/app/onboarding/page-9659af55efbf16f8.js"],"default",1]
|
||||
4:I[4707,[],""]
|
||||
5:I[36423,[],""]
|
||||
0:["EUsvrfLmgLy71o8GGPdmU",[[["",{"children":["onboarding",{"children":["__PAGE__",{}]}]},"$undefined","$undefined",true],["",{"children":["onboarding",{"children":["__PAGE__",{},[["$L1",["$","$L2",null,{"props":{"params":{},"searchParams":{}},"Component":"$3"}],null],null],null]},[null,["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children","onboarding","children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","notFoundStyles":"$undefined"}]],null]},[[[["$","link","0",{"rel":"stylesheet","href":"/litellm-asset-prefix/_next/static/css/31b7f215e119031e.css","precedence":"next","crossOrigin":"$undefined"}],["$","link","1",{"rel":"stylesheet","href":"/litellm-asset-prefix/_next/static/css/c1c21001170a99e0.css","precedence":"next","crossOrigin":"$undefined"}]],["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_b0dd8a","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":"404"}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[]}]}]}]],null],null],["$L6",null]]]]
|
||||
0:["EUsvrfLmgLy71o8GGPdmU",[[["",{"children":["onboarding",{"children":["__PAGE__",{}]}]},"$undefined","$undefined",true],["",{"children":["onboarding",{"children":["__PAGE__",{},[["$L1",["$","$L2",null,{"props":{"params":{},"searchParams":{}},"Component":"$3"}],null],null],null]},[null,["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children","onboarding","children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","notFoundStyles":"$undefined"}]],null]},[[[["$","link","0",{"rel":"stylesheet","href":"/_next/static/css/31b7f215e119031e.css","precedence":"next","crossOrigin":"$undefined"}],["$","link","1",{"rel":"stylesheet","href":"/_next/static/css/c1c21001170a99e0.css","precedence":"next","crossOrigin":"$undefined"}]],["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_b0dd8a","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":"404"}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[]}]}]}]],null],null],["$L6",null]]]]
|
||||
6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","link","5",{"rel":"icon","href":"./favicon.ico"}],["$","meta","6",{"name":"next-size-adjust"}]]
|
||||
1:null
|
||||
|
||||
@@ -180,6 +180,101 @@ def test_get_request_tags():
|
||||
assert "User-Agent: litellm/0.1.0" in tags
|
||||
|
||||
|
||||
def test_get_extra_header_tags():
|
||||
"""Test the _get_extra_header_tags method with various scenarios."""
|
||||
import litellm
|
||||
from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup
|
||||
|
||||
# Store original value to restore later
|
||||
original_extra_headers = getattr(litellm, "extra_spend_tag_headers", None)
|
||||
|
||||
try:
|
||||
# Test case 1: No extra headers configured
|
||||
litellm.extra_spend_tag_headers = None
|
||||
result = StandardLoggingPayloadSetup._get_extra_header_tags(
|
||||
proxy_server_request={"headers": {"x-custom": "value"}}
|
||||
)
|
||||
assert result is None
|
||||
|
||||
# Test case 2: Empty extra headers list
|
||||
litellm.extra_spend_tag_headers = []
|
||||
result = StandardLoggingPayloadSetup._get_extra_header_tags(
|
||||
proxy_server_request={"headers": {"x-custom": "value"}}
|
||||
)
|
||||
assert result is None
|
||||
|
||||
# Test case 3: Extra headers configured but request has no headers dict
|
||||
litellm.extra_spend_tag_headers = ["x-custom", "x-tenant"]
|
||||
result = StandardLoggingPayloadSetup._get_extra_header_tags(
|
||||
proxy_server_request={"headers": "not-a-dict"}
|
||||
)
|
||||
assert result is None
|
||||
|
||||
# Test case 4: Extra headers configured but none match request headers
|
||||
litellm.extra_spend_tag_headers = ["x-custom", "x-tenant"]
|
||||
result = StandardLoggingPayloadSetup._get_extra_header_tags(
|
||||
proxy_server_request={
|
||||
"headers": {
|
||||
"content-type": "application/json",
|
||||
"authorization": "Bearer token",
|
||||
}
|
||||
}
|
||||
)
|
||||
assert result is None
|
||||
|
||||
# Test case 5: Some extra headers match request headers
|
||||
litellm.extra_spend_tag_headers = ["x-custom", "x-tenant", "x-missing"]
|
||||
result = StandardLoggingPayloadSetup._get_extra_header_tags(
|
||||
proxy_server_request={
|
||||
"headers": {
|
||||
"x-custom": "my-custom-value",
|
||||
"x-tenant": "tenant-123",
|
||||
"content-type": "application/json",
|
||||
}
|
||||
}
|
||||
)
|
||||
assert result is not None
|
||||
assert len(result) == 2
|
||||
assert "x-custom: my-custom-value" in result
|
||||
assert "x-tenant: tenant-123" in result
|
||||
assert "x-missing: " not in str(result)
|
||||
|
||||
# Test case 6: All extra headers match request headers
|
||||
litellm.extra_spend_tag_headers = ["x-custom", "x-tenant"]
|
||||
result = StandardLoggingPayloadSetup._get_extra_header_tags(
|
||||
proxy_server_request={
|
||||
"headers": {
|
||||
"x-custom": "my-custom-value",
|
||||
"x-tenant": "tenant-123",
|
||||
"content-type": "application/json",
|
||||
}
|
||||
}
|
||||
)
|
||||
assert result is not None
|
||||
assert len(result) == 2
|
||||
assert "x-custom: my-custom-value" in result
|
||||
assert "x-tenant: tenant-123" in result
|
||||
|
||||
# Test case 7: Headers with empty values should not be included
|
||||
litellm.extra_spend_tag_headers = ["x-custom", "x-empty"]
|
||||
result = StandardLoggingPayloadSetup._get_extra_header_tags(
|
||||
proxy_server_request={"headers": {"x-custom": "my-value", "x-empty": ""}}
|
||||
)
|
||||
assert result is not None
|
||||
assert len(result) == 1
|
||||
assert "x-custom: my-value" in result
|
||||
assert "x-empty:" not in str(result)
|
||||
|
||||
finally:
|
||||
# Restore original value
|
||||
if original_extra_headers is not None:
|
||||
litellm.extra_spend_tag_headers = original_extra_headers
|
||||
else:
|
||||
# Remove the attribute if it didn't exist before
|
||||
if hasattr(litellm, "extra_spend_tag_headers"):
|
||||
delattr(litellm, "extra_spend_tag_headers")
|
||||
|
||||
|
||||
def test_response_cost_calculator_with_response_cost_in_hidden_params(logging_obj):
|
||||
from litellm import Router
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging
|
||||
|
||||
+26
-3
@@ -8,9 +8,6 @@ sys.path.insert(0, os.path.abspath("../../../../.."))
|
||||
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
from litellm.llms.anthropic.experimental_pass_through.adapters.streaming_iterator import (
|
||||
AnthropicStreamWrapper,
|
||||
)
|
||||
from litellm.types.utils import Delta, ModelResponse, StreamingChoices
|
||||
|
||||
|
||||
@@ -36,6 +33,32 @@ def test_anthropic_experimental_pass_through_messages_handler():
|
||||
mock_completion.call_args.kwargs["api_key"] == "test-api-key"
|
||||
|
||||
|
||||
def test_anthropic_experimental_pass_through_messages_handler_dynamic_api_key_and_api_base_and_custom_values():
|
||||
"""
|
||||
Test that api key is passed to litellm.completion
|
||||
"""
|
||||
from litellm.llms.anthropic.experimental_pass_through.messages.handler import (
|
||||
anthropic_messages_handler,
|
||||
)
|
||||
|
||||
with patch("litellm.completion", return_value="test-response") as mock_completion:
|
||||
try:
|
||||
anthropic_messages_handler(
|
||||
max_tokens=100,
|
||||
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
||||
model="azure/o1",
|
||||
api_key="test-api-key",
|
||||
api_base="test-api-base",
|
||||
custom_key="custom_value",
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
mock_completion.assert_called_once()
|
||||
mock_completion.call_args.kwargs["api_key"] == "test-api-key"
|
||||
mock_completion.call_args.kwargs["api_base"] == "test-api-base"
|
||||
mock_completion.call_args.kwargs["custom_key"] == "custom_value"
|
||||
|
||||
|
||||
def test_anthropic_experimental_pass_through_messages_handler_custom_llm_provider():
|
||||
"""
|
||||
Test that litellm.completion is called when a custom LLM provider is given
|
||||
|
||||
+9
-3
@@ -56,7 +56,9 @@ class MockCompletionStream:
|
||||
|
||||
def test_anthropic_sse_wrapper_format():
|
||||
"""Test that the SSE wrapper produces proper event and data formatting"""
|
||||
wrapper = AnthropicStreamWrapper(completion_stream=MockCompletionStream())
|
||||
wrapper = AnthropicStreamWrapper(
|
||||
completion_stream=MockCompletionStream(), model="claude-3"
|
||||
)
|
||||
|
||||
# Get the first chunk from the SSE wrapper
|
||||
first_chunk = next(wrapper.anthropic_sse_wrapper())
|
||||
@@ -77,7 +79,9 @@ def test_anthropic_sse_wrapper_format():
|
||||
|
||||
def test_anthropic_sse_wrapper_event_types():
|
||||
"""Test that different chunk types produce correct event types"""
|
||||
wrapper = AnthropicStreamWrapper(completion_stream=MockCompletionStream())
|
||||
wrapper = AnthropicStreamWrapper(
|
||||
completion_stream=MockCompletionStream(), model="claude-3"
|
||||
)
|
||||
|
||||
chunks = []
|
||||
for chunk in wrapper.anthropic_sse_wrapper():
|
||||
@@ -134,7 +138,9 @@ async def test_async_anthropic_sse_wrapper():
|
||||
self.index += 1
|
||||
return response
|
||||
|
||||
wrapper = AnthropicStreamWrapper(completion_stream=AsyncMockCompletionStream())
|
||||
wrapper = AnthropicStreamWrapper(
|
||||
completion_stream=AsyncMockCompletionStream(), model="claude-3"
|
||||
)
|
||||
|
||||
# Get the first chunk from the async SSE wrapper
|
||||
first_chunk = None
|
||||
|
||||
Reference in New Issue
Block a user