# What does this PR do?
Add rerank API for NVIDIA Inference Provider.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#3278
## Test Plan
Unit test:
```
pytest tests/unit/providers/nvidia/test_rerank_inference.py
```
Integration test:
```
pytest -s -v tests/integration/inference/test_rerank.py --stack-config="inference=nvidia" --rerank-model=nvidia/nvidia/nv-rerankqa-mistral-4b-v3 --env NVIDIA_API_KEY="" --env NVIDIA_BASE_URL="https://integrate.api.nvidia.com"
```
`mypy` became very slow for the common path. This can make local
pre-commit runs very slow. Let's restore that.
- restore fast mirrors-mypy hook for local runs
- add optional mypy-full hook and docs so devs can match CI
- run full mypy in CI with a hint when failures occur
### Test Plan
- uv run pre-commit run mypy --all-files
- uv run pre-commit run mypy-full --hook-stage manual --all-files
- uv run --group dev --group type_checking mypy
# What does this PR do?
chunk_id in the Chunk class executes actual logic to compute a chunk ID.
This sort of logic should not live in the API spec.
Instead, the providers should be in charge of calling generate_chunk_id,
and pass it to `Chunk`.
this removes the incorrect dependency between Provider impl and API impl
Signed-off-by: Charlie Doern <cdoern@redhat.com>
## Summary
When users provide API keys via `X-LlamaStack-Provider-Data` header,
`models.list()` now returns models they can access from those providers,
not just pre-registered models from the registry.
This complements the routing fix from f88416ef8 which enabled inference
calls with `provider_id/model_id` format for unregistered models. Users
can now discover which models are available to them before making
inference requests.
The implementation reuses
`NeedsRequestProviderData.get_request_provider_data()` to validate
credentials, then dynamically fetches models from providers without
caching them since they're user-specific. Registry models take
precedence to respect any pre-configured aliases.
## Test Script
```python
#!/usr/bin/env python3
import json
import os
from openai import OpenAI
# Test 1: Without provider_data header
client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="dummy")
models = client.models.list()
anthropic_without = [m.id for m in models.data if m.id and "anthropic" in m.id]
print(f"Without header: {len(models.data)} models, {len(anthropic_without)} anthropic")
# Test 2: With provider_data header containing Anthropic API key
anthropic_api_key = os.environ["ANTHROPIC_API_KEY"]
client_with_key = OpenAI(
base_url="http://localhost:8321/v1/openai/v1",
api_key="dummy",
default_headers={
"X-LlamaStack-Provider-Data": json.dumps({"anthropic_api_key": anthropic_api_key})
}
)
models_with_key = client_with_key.models.list()
anthropic_with = [m.id for m in models_with_key.data if m.id and "anthropic" in m.id]
print(f"With header: {len(models_with_key.data)} models, {len(anthropic_with)} anthropic")
print(f"Anthropic models: {anthropic_with}")
assert len(anthropic_with) > len(anthropic_without), "Should have more anthropic models with API key"
print("\n✓ Test passed!")
```
Run with a stack that has Anthropic provider configured (but without API
key in config):
```bash
ANTHROPIC_API_KEY=sk-ant-... python test_provider_data_models.py
```
## Summary
Fixes all mypy type errors in `providers/inline/agents/meta_reference/`
and removes exclusions from pyproject.toml.
## Changes
- Fix type annotations for Safety API message parameters
(OpenAIMessageParam)
- Add Action enum usage in access control checks
- Correct method signatures to match API supertype (parameter ordering)
- Handle optional return types with proper None checks
- Remove 3 meta_reference exclusions from mypy config
**Files fixed:** 25 errors across 3 files (safety.py, persistence.py,
agents.py)
## Summary
Resolves all mypy errors in meta reference agent OpenAI responses
implementation by adding proper type narrowing, None checks, and
Sequence type support.
## Changes
- Fixed streaming.py, openai_responses.py, utils.py, tool_executor.py,
agent_instance.py
- Added Sequence type support to schema generator (ensures correct JSON
schema generation)
- Applied union type narrowing and None checks throughout
## Test plan
- All modified files pass mypy type checking (0 errors)
- Schema generator produces correct `type: array` for Sequence types
---------
Co-authored-by: Claude <noreply@anthropic.com>
Error fixes in Agents implementation (`meta-reference` provider) --
adding proper type annotations and using type narrowing for optional
attributes. Essentially a bunch of `if x and x_foo := getattr(x, "foo")`
instead of `x.foo` directly
Part of ongoing mypy remediation effort.
---------
Co-authored-by: Claude <noreply@anthropic.com>
# What does this PR do?
- Adds OpenAI files provider
- Note that file content retrieval is pretty limited by `purpose`
https://community.openai.com/t/file-uploads-error-why-can-t-i-download-files-with-purpose-user-data/1357013?utm_source=chatgpt.com
## Test Plan
Modify run yaml to use openai files provider:
```
files:
- provider_id: openai
provider_type: remote::openai
config:
api_key: ${env.OPENAI_API_KEY:=}
metadata_store:
backend: sql_default
table_name: openai_files_metadata
# Then run files tests
❯ uv run --no-sync ./scripts/integration-tests.sh --stack-config server:ci-tests --inference-mode replay --setup ollama --suite base --pattern test_files
```
This PR enables routing of fully qualified model IDs of the form
`provider_id/model_id` even when the models are not registered with the
Stack.
Here's the situation: assume a remote inference provider which works
only when users provide their own API keys via
`X-LlamaStack-Provider-Data` header. By definition, we cannot list
models and hence update our routing registry. But because we _require_ a
provider ID in the models now, we can identify which provider to route
to and let that provider decide.
Note that we still try to look up our registry since it may have a
pre-registered alias. Just that we don't outright fail when we are not
able to look it up.
Also, updated inference router so that the responses have the _exact_
model that the request had.
## Test Plan
Added an integration test
Closes#3929
---------
Co-authored-by: ehhuang <ehhuang@users.noreply.github.com>
Adds type stubs and fixes mypy errors for better type coverage.
Changes:
- Added type_checking dependency group with type stubs (torchtune, trl,
etc.)
- Added lm-format-enforcer to pre-commit hook
- Created HFAutoModel Protocol for type-safe HuggingFace model handling
- Added mypy.overrides for untyped libraries (torchtune, fairscale,
etc.)
- Fixed type issues in post-training providers, databricks, and
api_recorder
Note: ~1,200 errors remain in excluded files (see pyproject.toml exclude
list).
---------
Co-authored-by: Claude <noreply@anthropic.com>
## Summary
- Fix OpenAI SDK NotGiven/Omit type mismatches in embeddings calls
- Fix incorrect OpenAIChatCompletionChunk import in vllm provider
- Refactor to avoid type:ignore comments by using conditional kwargs
## Changes
**openai_mixin.py (9 errors fixed):**
- Build kwargs conditionally for embeddings.create() to avoid
NotGiven/Omit mismatch
- Only include parameters when they have actual values (not None)
**gemini.py (9 errors fixed):**
- Apply same conditional kwargs pattern
- Add missing Any import
**vllm.py (2 errors fixed):**
- Use correct OpenAIChatCompletionChunk from llama_stack.apis.inference
- Remove incorrect alias from openai package
## Technical Notes
The OpenAI SDK has a type system quirk where `NOT_GIVEN` has type
`NotGiven` but parameter signatures expect `Omit`. By only passing
parameters with actual values, we avoid this mismatch entirely without
needing `# type: ignore` comments.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude <noreply@anthropic.com>
Fixes mypy type errors in provider utilities and testing infrastructure:
- `mcp.py`: Cast incompatible client types, wrap image data properly
- `batches.py`: Rename walrus variable to avoid shadowing
- `api_recorder.py`: Use cast for Pydantic field annotation
No functional changes.
---------
Co-authored-by: Claude <noreply@anthropic.com>
Fixes mypy type errors in OpenTelemetry integration:
- Add type aliases for AttributeValue and Attributes
- Add helper to filter None values from attributes (OpenTelemetry
doesn't accept None)
- Cast metric and tracer objects to proper types
- Update imports after refactoring
No functional changes.
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR is responsible for making changes to Responses API scheme to
introduce OpenAI compatible prompts there. Change to the API only,
therefore currently no implementation at all. However, the follow up PR
with actual implementation will be submitted after current PR lands.
The need of this functionality was initiated in #3514.
> Note, #3514 is divided on three separate PRs. Current PR is the second
of three.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## 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.* -->
CI
# What does this PR do?
add provider-data key passing support to Cerebras, Databricks, NVIDIA
and RunPod
also, added missing tests for Fireworks, Anthropic, Gemini, SambaNova,
and vLLM
addresses #3517
## Test Plan
ci w/ new tests
---------
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
Migrates package structure to src/ layout following Python packaging
best practices.
All code moved from `llama_stack/` to `src/llama_stack/`. Public API
unchanged - imports remain `import llama_stack.*`.
Updated build configs, pre-commit hooks, scripts, and GitHub workflows
accordingly. All hooks pass, package builds cleanly.
**Developer note**: Reinstall after pulling: `pip install -e .`
