Fix the header to highlight the website guardrailsai.com which is the real name/website of this provider.
Guardrails.ai leads to some different website, which is confusing.
```bash
LiteLLM:ERROR: server.py:370 - Error handling MCP request: Task group is not initialized. Make sure to use run().
Traceback (most recent call last):
File "/usr/lib/python3.13/site-packages/litellm/proxy/_experimental/mcp_server/server.py", line 368, in handle_streamable_http_mcp
await session_manager.handle_request(scope, receive, send)
File "/usr/lib/python3.13/site-packages/mcp/server/streamable_http_manager.py", line 137, in handle_request
raise RuntimeError("Task group is not initialized. Make sure to use run().")
RuntimeError: Task group is not initialized. Make sure to use run().
```
The use of asyncio.create_task with an infinite loop is not ideal for integration with FastAPI's lifespan events. Now correctly handles this by manually invoking the __aenter__ and __aexit__ methods of the session managers' async context managers within the application's lifespan.
* Enhance chart readability with short-form notation for large numbers (#11571)
* format y-axis value for total tokens
* format y-axis for the rest of the charts on model-activity
* revert changes for requests per day
* labels modified to plain text
* added plain text label for api_requests and spend
* minor
* improve keyname display (#11573)
* fix tags overlap
- no need to fix decimal points for valueFormatterSpend() bcz we're only going to use it for values on the x and y axis of a chart
- show only top 5 values of tags
- format the values on the x-axis
* format spend values to short form
* format spend to short form
* showing top 5 tags data more perceptively
* remove mock data
* remove console.log
* prettier
* prettier + remove unused functions
* Add _transform_input_image_item_to_image_item() to handle image transformations
* Update content transformation to process input_image items from Responses API format to Chat Completions format
* fix(key_management_endpoints.py): add new `/key/service_account/generate` endpoint
enables controlling team member permissions for creating service account keys
* refactor(ui/): use new /key/service-account/generate endpoint when creating keys on UI
enables team member permissions for service accounts
* fix(permission_definitions.tsx): add permission for creating service accounts on UI
* fix(key_management_endpoints.py): ignore user id for service account keys
* fix(key_management_endpoints.py): improve docstring
* docs(service_accounts.md): update docs
* refactor(aim.py): refactor to support adding aim guardrails on UI
* fix(base.py): add ui_friendly_name to config model
* feat(ui/): support loading new guardrails from backend api call
removes need to onboard each guardrail to ui
* fix: don't show optional params if not set and don't show ui_friendly_name (internal param0
* fix(ui/add_guardrail_form.tsx): ensure dynamic provider value is used
* fix(ui/): just one-time update the provider map dictionary
* fix(ui/): show masked api base / api key on guardrail update
* refactor(aporia_ai/): refactor to show on UI
* feat(aporia_ai/): add aporia ai guardrail to UI
* refactor(guardrails_ai/): refactor to add via UI
* refactor(lasso.py): refactor to enable adding lasso guardrails via UI
* feat(pangea.py): add pangea guardrail on UI
* feat(panw): add panw prisma airs through UI
* test: update tests
* fix: fix ruff linting error
* test: update tests
* fix: add missing docs
* fix: fix guardrail init
* fix: suppress linting errors
* fix(proxy_server.py): fix linting error
* fix(streaming_handler.py): don't return stream options in clientside response
* test: add unit test
* fix(custom_guardrail.py): fix start time
don't start until function actually called
leading to incorrect high latency numbers
* fix(custom_guardrail.py): fix start time
Fixes#9526
The embedding() function can return either an EmbeddingResponse or a
Coroutine[Any, Any, EmbeddingResponse] depending on whether aembedding=True
is passed. This adds @overload decorators to properly indicate the return
type based on the aembedding parameter.
This resolves type checking errors when using embedding() with aembedding=True
where the type checker couldn't determine that a coroutine was being returned.
