feat: Add MLflow Prompt Registry provider (squashed commit)

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>

tests/integration/providers/remote/prompts/mlflow/conftest.py

@@ -0,0 +1,133 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+"""Fixtures for MLflow integration tests.
+
+These tests require a running MLflow server. Set the MLFLOW_TRACKING_URI
+environment variable to point to your MLflow server, or the tests will
+attempt to use http://localhost:5555.
+
+To run tests:
+    # Start MLflow server (in separate terminal)
+    mlflow server --host 127.0.0.1 --port 5555
+
+    # Run integration tests
+    MLFLOW_TRACKING_URI=http://localhost:5555 \
+        uv run --group test pytest -sv tests/integration/providers/remote/prompts/mlflow/
+"""
+
+import os
+
+import pytest
+
+from llama_stack.providers.remote.prompts.mlflow import MLflowPromptsAdapter
+from llama_stack.providers.remote.prompts.mlflow.config import MLflowPromptsConfig
+
+
+@pytest.fixture(scope="session")
+def mlflow_tracking_uri():
+    """Get MLflow tracking URI from environment or use default."""
+    return os.environ.get("MLFLOW_TRACKING_URI", "http://localhost:5555")
+
+
+@pytest.fixture(scope="session")
+def mlflow_server_available(mlflow_tracking_uri):
+    """Verify MLflow server is running and accessible.
+
+    Skips all tests if server is not available.
+    """
+    try:
+        import requests
+
+        response = requests.get(f"{mlflow_tracking_uri}/health", timeout=5)
+        if response.status_code != 200:
+            pytest.skip(f"MLflow server at {mlflow_tracking_uri} returned status {response.status_code}")
+    except ImportError:
+        pytest.skip("requests package not installed - install with: pip install requests")
+    except requests.exceptions.ConnectionError:
+        pytest.skip(
+            f"MLflow server not available at {mlflow_tracking_uri}. "
+            "Start with: mlflow server --host 127.0.0.1 --port 5555"
+        )
+    except requests.exceptions.Timeout:
+        pytest.skip(f"MLflow server at {mlflow_tracking_uri} timed out")
+    except Exception as e:
+        pytest.skip(f"Failed to check MLflow server availability: {e}")
+
+    return True
+
+
+@pytest.fixture
+async def mlflow_config(mlflow_tracking_uri, mlflow_server_available):
+    """Create MLflow configuration for testing."""
+    return MLflowPromptsConfig(
+        mlflow_tracking_uri=mlflow_tracking_uri,
+        experiment_name="test-llama-stack-prompts",
+        timeout_seconds=30,
+    )
+
+
+@pytest.fixture
+async def mlflow_adapter(mlflow_config):
+    """Create and initialize MLflow adapter for testing.
+
+    This fixture creates a new adapter instance for each test.
+    The adapter connects to the MLflow server specified in the config.
+    """
+    adapter = MLflowPromptsAdapter(config=mlflow_config)
+    await adapter.initialize()
+
+    yield adapter
+
+    # Cleanup: shutdown adapter
+    await adapter.shutdown()
+
+
+@pytest.fixture
+async def mlflow_adapter_with_cleanup(mlflow_config):
+    """Create MLflow adapter with automatic cleanup after test.
+
+    This fixture is useful for tests that create prompts and want them
+    automatically cleaned up (though MLflow doesn't support deletion,
+    so cleanup is best-effort).
+    """
+    adapter = MLflowPromptsAdapter(config=mlflow_config)
+    await adapter.initialize()
+
+    created_prompt_ids = []
+
+    # Provide adapter and tracking list
+    class AdapterWithTracking:
+        def __init__(self, adapter_instance):
+            self.adapter = adapter_instance
+            self.created_ids = created_prompt_ids
+
+        async def create_prompt(self, *args, **kwargs):
+            prompt = await self.adapter.create_prompt(*args, **kwargs)
+            self.created_ids.append(prompt.prompt_id)
+            return prompt
+
+        def __getattr__(self, name):
+            return getattr(self.adapter, name)
+
+    tracked_adapter = AdapterWithTracking(adapter)
+
+    yield tracked_adapter
+
+    # Cleanup: attempt to delete created prompts
+    # Note: MLflow doesn't support deletion, so this is a no-op
+    # but we keep it for future compatibility
+    for prompt_id in created_prompt_ids:
+        try:
+            await adapter.delete_prompt(prompt_id)
+        except NotImplementedError:
+            # Expected - MLflow doesn't support deletion
+            pass
+        except Exception:
+            # Ignore cleanup errors
+            pass
+
+    await adapter.shutdown()