## 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
```
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>
# 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 .`
2025-10-27 12:02:21 -07:00
Renamed from llama_stack/providers/remote/inference/databricks/databricks.py (Browse further)
