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0e0d311dea
Add a new remote provider that integrates MLflow's Prompt Registry with Llama Stack's prompts API, enabling centralized prompt management and versioning using MLflow as the backend. Features: - Full implementation of Llama Stack Prompts protocol - Support for prompt versioning and default version management - Automatic variable extraction from Jinja2-style templates - MLflow tag-based metadata for efficient prompt filtering - Flexible authentication (config, environment variables, per-request) - Bidirectional ID mapping (pmpt_<hex> ↔ llama_prompt_<hex>) - Comprehensive error handling and validation Implementation: - Remote provider: src/llama_stack/providers/remote/prompts/mlflow/ - Inline reference provider: src/llama_stack/providers/inline/prompts/reference/ - MLflow 3.4+ required for Prompt Registry API support - Deterministic ID mapping ensures consistency across conversions Testing: - 15 comprehensive unit tests (config validation, ID mapping) - 18 end-to-end integration tests (full CRUD workflows) - GitHub Actions workflow for automated CI testing with MLflow server - Integration test fixtures with automatic server setup Documentation: - Complete provider configuration reference - Setup and usage examples with code samples - Authentication options and security best practices Signed-off-by: William Caban <william.caban@gmail.com> Co-Authored-By: Claude <noreply@anthropic.com>
133 lines
4.2 KiB
Python
133 lines
4.2 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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"""Fixtures for MLflow integration tests.
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These tests require a running MLflow server. Set the MLFLOW_TRACKING_URI
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environment variable to point to your MLflow server, or the tests will
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attempt to use http://localhost:5555.
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To run tests:
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# Start MLflow server (in separate terminal)
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mlflow server --host 127.0.0.1 --port 5555
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# Run integration tests
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MLFLOW_TRACKING_URI=http://localhost:5555 \
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uv run --group test pytest -sv tests/integration/providers/remote/prompts/mlflow/
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"""
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import os
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import pytest
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from llama_stack.providers.remote.prompts.mlflow import MLflowPromptsAdapter
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from llama_stack.providers.remote.prompts.mlflow.config import MLflowPromptsConfig
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@pytest.fixture(scope="session")
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def mlflow_tracking_uri():
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"""Get MLflow tracking URI from environment or use default."""
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return os.environ.get("MLFLOW_TRACKING_URI", "http://localhost:5555")
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@pytest.fixture(scope="session")
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def mlflow_server_available(mlflow_tracking_uri):
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"""Verify MLflow server is running and accessible.
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Skips all tests if server is not available.
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"""
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try:
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import requests
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response = requests.get(f"{mlflow_tracking_uri}/health", timeout=5)
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if response.status_code != 200:
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pytest.skip(f"MLflow server at {mlflow_tracking_uri} returned status {response.status_code}")
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except ImportError:
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pytest.skip("requests package not installed - install with: pip install requests")
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except requests.exceptions.ConnectionError:
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pytest.skip(
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f"MLflow server not available at {mlflow_tracking_uri}. "
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"Start with: mlflow server --host 127.0.0.1 --port 5555"
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)
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except requests.exceptions.Timeout:
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pytest.skip(f"MLflow server at {mlflow_tracking_uri} timed out")
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except Exception as e:
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pytest.skip(f"Failed to check MLflow server availability: {e}")
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return True
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@pytest.fixture
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async def mlflow_config(mlflow_tracking_uri, mlflow_server_available):
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"""Create MLflow configuration for testing."""
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return MLflowPromptsConfig(
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mlflow_tracking_uri=mlflow_tracking_uri,
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experiment_name="test-llama-stack-prompts",
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timeout_seconds=30,
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)
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@pytest.fixture
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async def mlflow_adapter(mlflow_config):
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"""Create and initialize MLflow adapter for testing.
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This fixture creates a new adapter instance for each test.
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The adapter connects to the MLflow server specified in the config.
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"""
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adapter = MLflowPromptsAdapter(config=mlflow_config)
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await adapter.initialize()
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yield adapter
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# Cleanup: shutdown adapter
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await adapter.shutdown()
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@pytest.fixture
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async def mlflow_adapter_with_cleanup(mlflow_config):
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"""Create MLflow adapter with automatic cleanup after test.
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This fixture is useful for tests that create prompts and want them
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automatically cleaned up (though MLflow doesn't support deletion,
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so cleanup is best-effort).
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"""
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adapter = MLflowPromptsAdapter(config=mlflow_config)
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await adapter.initialize()
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created_prompt_ids = []
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# Provide adapter and tracking list
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class AdapterWithTracking:
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def __init__(self, adapter_instance):
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self.adapter = adapter_instance
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self.created_ids = created_prompt_ids
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async def create_prompt(self, *args, **kwargs):
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prompt = await self.adapter.create_prompt(*args, **kwargs)
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self.created_ids.append(prompt.prompt_id)
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return prompt
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def __getattr__(self, name):
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return getattr(self.adapter, name)
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tracked_adapter = AdapterWithTracking(adapter)
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yield tracked_adapter
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# Cleanup: attempt to delete created prompts
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# Note: MLflow doesn't support deletion, so this is a no-op
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# but we keep it for future compatibility
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for prompt_id in created_prompt_ids:
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try:
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await adapter.delete_prompt(prompt_id)
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except NotImplementedError:
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# Expected - MLflow doesn't support deletion
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pass
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except Exception:
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# Ignore cleanup errors
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pass
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await adapter.shutdown()
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