* litellm_fix_mapped_tests_core: fix test isolation and mock injection issues
## Problem
Four tests in litellm_mapped_tests_core were failing:
1. test_register_model_with_scientific_notation - KeyError due to test isolation issues
2. test_search_uses_registry_credentials - Mock not being called due to incorrect patch path
3. test_send_email_missing_api_key - Real API calls despite mocking
4. test_stream_transformation_error_sync - Mock not effective, real API called
## Solution
### test_register_model_with_scientific_notation
- Use unique model name to avoid conflicts with other tests
- Clear LRU caches before test to prevent stale data
- Clean up model_cost entry after test
### test_search_uses_registry_credentials
- Use patch.object() on the actual base_llm_http_handler instance
- String-based patching for instance methods can fail; direct object patching is more reliable
### test_send_email_missing_api_key
- Directly inject mock HTTP client into logger instance
- This bypasses any caching issues that could cause the fixture mock to be ineffective
### test_stream_transformation_error_sync
- Patch litellm.completion directly instead of the handler module's litellm reference
- This ensures the mock is effective regardless of import order
## Regression
These tests were affected by LRU caching added in #19606 and HTTP client caching.
* fix(test): use patch.object for container API tests to fix mock injection
## Problem
test_retrieve_container_basic tests were failing because mocks weren't
being applied correctly. The tests used string-based patching:
patch('litellm.containers.main.base_llm_http_handler')
But base_llm_http_handler is imported at module level, so the mock wasn't
intercepting the actual handler calls, resulting in real HTTP requests
to OpenAI API.
## Solution
Use patch.object() to directly mock methods on the imported handler
instance. Import base_llm_http_handler in the test file and patch like:
patch.object(base_llm_http_handler, 'container_retrieve_handler', ...)
This ensures the mock is applied to the actual object being used,
regardless of import order or caching.
* fix(test): add missing Prometheus metric labels to test_proxy_failure_metrics
Add client_ip, user_agent, model_id labels to expected metric patterns.
These labels were added in PRs #19717 and #19678 but test wasn't updated.
* fix(test_resend_email): use direct mock injection for all email tests
Extend the mock injection pattern used in test_send_email_missing_api_key
to all other tests in the file:
- test_send_email_success
- test_send_email_multiple_recipients
Instead of relying on fixture-based patching and respx mocks which can
fail due to import order and caching issues, directly inject the mock
HTTP client into the logger instance. This ensures mocks are always used
regardless of test execution order.
* fix(test): use patch.object for image_edit and vector_store tests
- test_image_edit_merges_headers_and_extra_headers: import base_llm_http_handler
and use patch.object instead of string path patching
- test_search_uses_registry_credentials: import module and patch via
module.base_llm_http_handler to ensure we patch the right instance
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
## Problem
Tests using mocked HTTP clients were hitting real APIs because:
1. HTTP client cache was returning previously cached real clients
2. isinstance checks failed due to module identity issues from sys.path
### Tests affected:
- test_send_email_missing_api_key
- test_send_email_multiple_recipients (resend & sendgrid)
- test_search_uses_registry_credentials
- test_vector_store_create_with_simple_provider_name
- test_vector_store_create_with_provider_api_type
- test_vector_store_create_with_ragflow_provider
- test_image_edit_merges_headers_and_extra_headers
- test_retrieve_container_basic (container API tests)
## Solution
1. Add clear_client_cache fixture (autouse=True) to clear
litellm.in_memory_llm_clients_cache before each test
2. Fix isinstance checks to use type name comparison
(avoids module identity issues from sys.path.insert)
## Why not disable_aiohttp_transport
The default transport is aiohttp, so tests should work with it.
Clearing the cache ensures mocks are used instead of cached real clients.
## Regression
PR #19829 (commit f95572e3ed) added @respx.mock but cached clients
from earlier tests were being reused, bypassing the mocks.
Co-authored-by: shin-bot-litellm <shin-bot-litellm@users.noreply.github.com>
The test had prompt_tokens=1000 but the sum of token details was 1150
(text=700 + audio=100 + cached=200 + cache_creation=150).
This triggered the double-counting detection logic which recalculated
text_tokens to 550, causing the assertion to fail.
Fixed by setting prompt_tokens=1150 to match the sum of details.
* Add async_post_call_response_headers_hook to CustomLogger (#20070)
Allow CustomLogger callbacks to inject custom HTTP response headers
into streaming, non-streaming, and failure responses via a new
async_post_call_response_headers_hook method.
* async_post_call_response_headers_hook
---------
Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
* fix(hosted_vllm): route through base_llm_http_handler to support ssl_verify
The hosted_vllm provider was falling through to the OpenAI catch-all path
which doesn't pass ssl_verify to the HTTP client. This adds an explicit
elif branch that routes hosted_vllm through base_llm_http_handler.completion()
which properly passes ssl_verify to the httpx client.
- Add explicit hosted_vllm branch in main.py completion()
- Add ssl_verify tests for sync and async completion
- Update existing audio_url test to mock httpx instead of OpenAI client
* feat(hosted_vllm): add embedding support with ssl_verify
- Add HostedVLLMEmbeddingConfig for embedding transformations
- Register hosted_vllm embedding config in utils.py
- Add lazy import for embedding transformation module
- Add unit test for ssl_verify parameter handling
* fix(proxy): prevent provider-prefixed model leaks
Proxy clients should not see LiteLLM internal provider prefixes (e.g. hosted_vllm/...) in the OpenAI-compatible response model field.
This patch sanitizes the client-facing model name for both:
- Non-streaming responses returned from base_process_llm_request
- Streaming SSE chunks emitted by async_data_generator
Adds regression tests covering vLLM-style hosted_vllm routing for both streaming and non-streaming paths.
* chore(lint): suppress PLR0915 in proxy handler
Ruff started flagging ProxyBaseLLMRequestProcessing.base_process_llm_request() for too many statements after the hotpatch changes.
Add an explicit '# noqa: PLR0915' on the function definition to avoid a large refactor in a hotpatch.
* refactor(proxy): make model restamp explicit
Replace silent try/except/pass and type ignores with explicit model restamping.
- Logs an error when the downstream response model differs from the client-requested model
- Overwrites the OpenAI `model` field to the client-requested value to avoid leaking internal provider-prefixed identifiers
- Applies the same behavior to streaming chunks, logging the mismatch only once per stream
* chore(lint): drop PLR0915 suppression
The model restamping bugfix made `base_process_llm_request()` slightly exceed Ruff's
PLR0915 (too-many-statements) threshold, requiring a `# noqa` suppression.
Collapse consecutive `hidden_params` extractions into tuple unpacking so the
function falls back under the lint limit and remove the suppression.
No functional change intended; this keeps the proxy model-field bugfix intact
while aligning with project linting rules.
* chore(proxy): log model mismatches as warnings
These model-restamping logs are intentionally verbose: a mismatch is a useful signal
that an internal provider/deployment identifier may be leaking into the public
OpenAI response `model` field.
- Downgrade model mismatch logs from error -> warning
- Keep error logs only for cases where the proxy cannot read/override the model
* fix(proxy): preserve client model for streaming aliasing
Pre-call processing can rewrite request_data['model'] via model alias maps.\n\nOur streaming SSE generator was using the rewritten value when restamping chunk.model, which caused the public 'model' field to differ between streaming and non-streaming responses for alias-based requests.\n\nStash the original client model in request_data as _litellm_client_requested_model after the model has been routed, and prefer it when overriding the outgoing chunk model. Add a regression test for the alias-mapping case.
* chore(lint): satisfy PLR0915 in streaming generator
Ruff started flagging async_data_generator() for too many statements after adding model restamping logic.\n\nExtract the client-model selection + chunk restamping into small helpers to keep behavior unchanged while meeting the project's PLR0915 threshold.
The /health/services endpoint rejected datadog_llm_observability as an
unknown service, even though it was registered in the core callback
registry and __init__.py. Added it to both the Literal type hint and
the hardcoded validation list in the health endpoint.
* fix(vertex_ai): convert image URLs to base64 in tool messages for Anthropic
Fixes#19891
Vertex AI Anthropic models don't support URL sources for images. LiteLLM
already converted image URLs to base64 for user messages, but not for tool
messages (role='tool'). This caused errors when using ToolOutputImage with
image_url in tool outputs.
Changes:
- Add force_base64 parameter to convert_to_anthropic_tool_result()
- Pass force_base64 to create_anthropic_image_param() for tool message images
- Calculate force_base64 in anthropic_messages_pt() based on llm_provider
- Add unit tests for tool message image handling
* chore: remove extra comment from test file header
