# What does this PR do?
## Test Plan
# What does this PR do?
## Test Plan
# What does this PR do?
## Test Plan
Completes the refactoring started in previous commit by:
1. **Fix library client** (critical): Add logic to detect Pydantic model parameters
and construct them properly from request bodies. The key fix is to NOT exclude
any params when converting the body for Pydantic models - we need all fields
to pass to the Pydantic constructor.
Before: _convert_body excluded all params, leaving body empty for Pydantic construction
After: Check for Pydantic params first, skip exclusion, construct model with full body
2. **Update remaining providers** to use new Pydantic-based signatures:
- litellm_openai_mixin: Extract extra fields via __pydantic_extra__
- databricks: Use TYPE_CHECKING import for params type
- llama_openai_compat: Use TYPE_CHECKING import for params type
- sentence_transformers: Update method signatures to use params
3. **Update unit tests** to use new Pydantic signature:
- test_openai_mixin.py: Use OpenAIChatCompletionRequestParams
This fixes test failures where the library client was trying to construct
Pydantic models with empty dictionaries.
The previous fix had a bug: it called _convert_body() which only keeps fields
that match function parameter names. For Pydantic methods with signature:
openai_chat_completion(params: OpenAIChatCompletionRequestParams)
The signature only has 'params', but the body has 'model', 'messages', etc.
So _convert_body() returned an empty dict.
Fix: Skip _convert_body() entirely for Pydantic params. Use the raw body
directly to construct the Pydantic model (after stripping NOT_GIVENs).
This properly fixes the ValidationError where required fields were missing.
The streaming code path (_call_streaming) had the same issue as non-streaming:
it called _convert_body() which returned empty dict for Pydantic params.
Applied the same fix as commit 7476c0ae:
- Detect Pydantic model parameters before body conversion
- Skip _convert_body() for Pydantic params
- Construct Pydantic model directly from raw body (after stripping NOT_GIVENs)
This fixes streaming endpoints like openai_chat_completion with stream=True.
The streaming code path (_call_streaming) had the same issue as non-streaming:
it called _convert_body() which returned empty dict for Pydantic params.
Applied the same fix as commit 7476c0ae:
- Detect Pydantic model parameters before body conversion
- Skip _convert_body() for Pydantic params
- Construct Pydantic model directly from raw body (after stripping NOT_GIVENs)
This fixes streaming endpoints like openai_chat_completion with stream=True.
Propagate test IDs from client to server via HTTP headers to maintain
proper test isolation when running with server-based stack configs.
Without
this, recorded/replayed inference requests in server mode would leak
across
tests.
Changes:
- Patch client _prepare_request to inject test ID into provider data
header
- Sync test context from provider data on server side before storage
operations
- Set LLAMA_STACK_TEST_STACK_CONFIG_TYPE env var based on stack config
- Configure console width for cleaner log output in CI
- Add SQLITE_STORE_DIR temp directory for test data isolation
Uses test_id in request hashes and test-scoped subdirectories to prevent
cross-test contamination. Model list endpoints exclude test_id to enable
merging recordings from different servers.
Additionally, this PR adds a `record-if-missing` mode (which we will use
instead of `record` which records everything) which is very useful.
🤖 Co-authored with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
# What does this PR do?
unpublish (make unavailable to users) the following apis -
- `/v1/inference/completion`, replaced by `/v1/openai/v1/completions`
- `/v1/inference/chat-completion`, replaced by
`/v1/openai/v1/chat/completions`
- `/v1/inference/embeddings`, replaced by `/v1/openai/v1/embeddings`
- `/v1/inference/batch-completion`, replaced by `/v1/openai/v1/batches`
- `/v1/inference/batch-chat-completion`, replaced by
`/v1/openai/v1/batches`
note: the implementations are still available for internal use, e.g.
agents uses chat-completion.
# What does this PR do?
I found a few issues while adding new metrics for various APIs:
currently metrics are only propagated in `chat_completion` and
`completion`
since most providers use the `openai_..` routes as the default in
`llama-stack-client inference chat-completion`, metrics are currently
not working as expected.
in order to get them working the following had to be done:
1. get the completion as usual
2. use new `openai_` versions of the metric gathering functions which
use `.usage` from the `OpenAI..` response types to gather the metrics
which are already populated.
3. define a `stream_generator` which counts the tokens and computes the
metrics (only for stream=True)
5. add metrics to response
NOTE: I could not add metrics to `openai_completion` where stream=True
because that ONLY returns an `OpenAICompletion` not an AsyncGenerator
that we can manipulate.
acquire the lock, and add event to the span as the other `_log_...`
methods do
some new output:
`llama-stack-client inference chat-completion --message hi`
<img width="2416" height="425" alt="Screenshot 2025-07-16 at 8 28 20 AM"
src="https://github.com/user-attachments/assets/ccdf1643-a184-4ddd-9641-d426c4d51326"
/>
and in the client:
<img width="763" height="319" alt="Screenshot 2025-07-16 at 8 28 32 AM"
src="https://github.com/user-attachments/assets/6bceb811-5201-47e9-9e16-8130f0d60007"
/>
these were not previously being recorded nor were they being printed to
the server due to the improper console sink handling
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR fixes flaky telemetry tests
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
See https://github.com/meta-llama/llama-stack/pull/2814
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
# What does this PR do?
## Test Plan
ENABLE_OLLAMA=ollama LLAMA_STACK_CONFIG=starter uv run pytest
tests/integration/telemetry
--text-model="ollama/llama3.2:3b-instruct-fp16"
# What does this PR do?
https://github.com/meta-llama/llama-stack/pull/1828 removed
__root_span__ attribute which is still needed
## Test Plan
added telemetry integration test
LLAMA_STACK_CONFIG=http://localhost:5001 pytest -s -v
tests/integration/telemetry --safety-shield meta-llama/Llama-Guard-3-8B
--text-model accounts/fireworks/models/llama-v3p3-70b-instruct
