Merge branch 'main' into fix-2619

.github/workflows/integration-tests.yml

@@ -23,6 +23,7 @@ on:
       - '.github/actions/setup-test-environment/action.yml'
       - '.github/actions/run-and-record-tests/action.yml'
       - 'scripts/integration-tests.sh'
+      - 'scripts/generate_ci_matrix.py'
   schedule:
     # If changing the cron schedule, update the provider in the test-matrix job
     - cron: '0 0 * * *'  # (test latest client) Daily at 12 AM UTC
@@ -44,8 +45,27 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+  generate-matrix:
+    runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+
+      - name: Generate test matrix
+        id: set-matrix
+        run: |
+          # Generate matrix from CI_MATRIX in tests/integration/suites.py
+          # Supports schedule-based and manual input overrides
+          MATRIX=$(PYTHONPATH=. python3 scripts/generate_ci_matrix.py \
+            --schedule "${{ github.event.schedule }}" \
+            --test-setup "${{ github.event.inputs.test-setup }}")
+          echo "matrix=$MATRIX" >> $GITHUB_OUTPUT
+          echo "Generated matrix: $MATRIX"
 
   run-replay-mode-tests:
+    needs: generate-matrix
     runs-on: ubuntu-latest
     name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, {4})', matrix.client-type, matrix.config.setup, matrix.python-version, matrix.client-version, matrix.config.suite) }}
 
@@ -56,18 +76,9 @@ jobs:
         # Use Python 3.13 only on nightly schedule (daily latest client test), otherwise use 3.12
         python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }}
         client-version: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }}
-        # Define (setup, suite) pairs - they are always matched and cannot be independent
-        # Weekly schedule (Sun 1 AM): vllm+base
-        # Input test-setup=ollama-vision: ollama-vision+vision
-        # Default (including test-setup=ollama): ollama+base, ollama-vision+vision, gpt+responses
-        config: >-
-          ${{
-            github.event.schedule == '1 0 * * 0'
-              && fromJSON('[{"setup": "vllm", "suite": "base"}]')
-            || github.event.inputs.test-setup == 'ollama-vision'
-              && fromJSON('[{"setup": "ollama-vision", "suite": "vision"}]')
-            || fromJSON('[{"setup": "ollama", "suite": "base"}, {"setup": "ollama-vision", "suite": "vision"}, {"setup": "gpt", "suite": "responses"}]')
-          }}
+        # Test configurations: Generated from CI_MATRIX in tests/integration/suites.py
+        # See scripts/generate_ci_matrix.py for generation logic
+        config: ${{ fromJSON(needs.generate-matrix.outputs.matrix).include }}
 
     steps:
       - name: Checkout repository

.github/workflows/pre-commit.yml

@@ -165,3 +165,14 @@ jobs:
             echo "::error::Full mypy failed. Reproduce locally with 'uv run pre-commit run mypy-full --hook-stage manual --all-files'."
           fi
           exit $status
+
+      - name: Check if any unused recordings
+        run: |
+          set -e
+          PYTHONPATH=$PWD uv run ./scripts/cleanup_recordings.py --delete
+          changes=$(git status --short tests/integration | grep 'recordings' || true)
+          if [ -n "$changes" ]; then
+            echo "::error::Unused integration recordings detected. Run 'PYTHONPATH=$(pwd) uv run ./scripts/cleanup_recordings.py --delete' locally and commit the deletions."
+            echo "$changes"
+            exit 1
+          fi

scripts/cleanup_recordings.py

@@ -0,0 +1,272 @@
+#!/usr/bin/env python3
+# 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.
+
+"""
+Clean up unused test recordings based on CI test collection.
+
+This script:
+1. Reads CI matrix definitions from tests/integration/ci_matrix.json (default + scheduled overrides)
+2. Uses pytest --collect-only with --json-report to gather all test IDs that run in CI
+3. Compares against existing recordings to identify unused ones
+4. Optionally deletes unused recordings
+
+Usage:
+    # Dry run - see what would be deleted
+    ./scripts/cleanup_recordings.py
+
+    # Save manifest of CI test IDs for inspection
+    ./scripts/cleanup_recordings.py --manifest ci_tests.txt
+
+    # Actually delete unused recordings
+    ./scripts/cleanup_recordings.py --delete
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import tempfile
+from collections import defaultdict
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).parent.parent
+
+# Load CI matrix from JSON file
+CI_MATRIX_FILE = REPO_ROOT / "tests/integration/ci_matrix.json"
+with open(CI_MATRIX_FILE) as f:
+    _matrix_config = json.load(f)
+
+DEFAULT_CI_MATRIX: list[dict[str, str]] = _matrix_config["default"]
+SCHEDULED_MATRICES: dict[str, list[dict[str, str]]] = _matrix_config.get("schedules", {})
+
+
+def _unique_configs(entries):
+    seen: set[tuple[str, str]] = set()
+    for entry in entries:
+        suite = entry["suite"]
+        setup = entry["setup"]
+        key = (suite, setup)
+        if key in seen:
+            continue
+        seen.add(key)
+        yield {"suite": suite, "setup": setup}
+
+
+def iter_all_ci_configs() -> list[dict[str, str]]:
+    """Return unique CI configs across default and scheduled matrices."""
+    combined = list(DEFAULT_CI_MATRIX)
+    for configs in SCHEDULED_MATRICES.values():
+        combined.extend(configs)
+    return list(_unique_configs(combined))
+
+
+def collect_ci_tests():
+    """Collect all test IDs that would run in CI using --collect-only with JSON output."""
+
+    all_test_ids = set()
+    configs = iter_all_ci_configs()
+
+    for config in configs:
+        print(f"Collecting tests for suite={config['suite']}, setup={config['setup']}...")
+
+        # Create a temporary file for JSON report
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
+            json_report_file = f.name
+
+        try:
+            # Configure environment for collection run
+            env = os.environ.copy()
+            env["PYTEST_ADDOPTS"] = f"--json-report --json-report-file={json_report_file}"
+            repo_path = str(REPO_ROOT)
+            existing_path = env.get("PYTHONPATH", "")
+            env["PYTHONPATH"] = f"{repo_path}{os.pathsep}{existing_path}" if existing_path else repo_path
+
+            result = subprocess.run(
+                [
+                    "./scripts/integration-tests.sh",
+                    "--collect-only",
+                    "--suite",
+                    config["suite"],
+                    "--setup",
+                    config["setup"],
+                ],
+                capture_output=True,
+                text=True,
+                cwd=REPO_ROOT,
+                env=env,
+            )
+
+            if result.returncode != 0:
+                raise RuntimeError(
+                    "Test collection failed.\n"
+                    f"Command: {' '.join(result.args)}\n"
+                    f"stdout:\n{result.stdout}\n"
+                    f"stderr:\n{result.stderr}"
+                )
+
+            # Parse JSON report to extract test IDs
+            try:
+                with open(json_report_file) as f:
+                    report = json.load(f)
+
+                # The "collectors" field contains collected test items
+                # Each collector has a "result" array with test node IDs
+                for collector in report.get("collectors", []):
+                    for item in collector.get("result", []):
+                        # The "nodeid" field is the test ID
+                        if "nodeid" in item:
+                            all_test_ids.add(item["nodeid"])
+
+                print(f"  Collected {len(all_test_ids)} test IDs so far")
+
+            except (json.JSONDecodeError, FileNotFoundError) as e:
+                print(f"  Warning: Failed to parse JSON report: {e}")
+                continue
+
+        finally:
+            # Clean up temp file
+            if os.path.exists(json_report_file):
+                os.unlink(json_report_file)
+
+    print(f"\nTotal unique test IDs collected: {len(all_test_ids)}")
+    return all_test_ids, configs
+
+
+def get_base_test_id(test_id: str) -> str:
+    """Extract base test ID without parameterization.
+
+    Example:
+      'tests/integration/inference/test_foo.py::test_bar[param1-param2]'
+      -> 'tests/integration/inference/test_foo.py::test_bar'
+    """
+    return test_id.split("[")[0] if "[" in test_id else test_id
+
+
+def find_all_recordings():
+    """Find all recording JSON files."""
+    return list((REPO_ROOT / "tests/integration").rglob("recordings/*.json"))
+
+
+def analyze_recordings(ci_test_ids, dry_run=True):
+    """Analyze recordings and identify unused ones."""
+
+    # Use full test IDs with parameterization for exact matching
+    all_recordings = find_all_recordings()
+    print(f"\nTotal recording files: {len(all_recordings)}")
+
+    # Categorize recordings
+    used_recordings = []
+    unused_recordings = []
+    shared_recordings = []  # model-list endpoints without test_id
+    parse_errors = []
+
+    for json_file in all_recordings:
+        try:
+            with open(json_file) as f:
+                data = json.load(f)
+
+            test_id = data.get("test_id", "")
+
+            if not test_id:
+                # Shared/infrastructure recordings (model lists, etc)
+                shared_recordings.append(json_file)
+                continue
+
+            # Match exact test_id (with full parameterization)
+            if test_id in ci_test_ids:
+                used_recordings.append(json_file)
+            else:
+                unused_recordings.append((json_file, test_id))
+
+        except Exception as e:
+            parse_errors.append((json_file, str(e)))
+
+    # Print summary
+    print("\nRecording Analysis:")
+    print(f"  Used in CI:     {len(used_recordings)}")
+    print(f"  Shared (no ID): {len(shared_recordings)}")
+    print(f"  UNUSED:         {len(unused_recordings)}")
+    print(f"  Parse errors:   {len(parse_errors)}")
+
+    if unused_recordings:
+        print("\nUnused recordings by test:")
+
+        # Group by base test ID
+        by_test = defaultdict(list)
+        for file, test_id in unused_recordings:
+            base = get_base_test_id(test_id)
+            by_test[base].append(file)
+
+        for base_test, files in sorted(by_test.items()):
+            print(f"\n  {base_test}")
+            print(f"    ({len(files)} recording(s))")
+            for f in files[:3]:
+                print(f"      - {f.relative_to(REPO_ROOT / 'tests/integration')}")
+            if len(files) > 3:
+                print(f"      ... and {len(files) - 3} more")
+
+    if parse_errors:
+        print("\nParse errors:")
+        for file, error in parse_errors[:5]:
+            print(f"  {file.relative_to(REPO_ROOT)}: {error}")
+        if len(parse_errors) > 5:
+            print(f"  ... and {len(parse_errors) - 5} more")
+
+    # Perform cleanup
+    if not dry_run:
+        print(f"\nDeleting {len(unused_recordings)} unused recordings...")
+        for file, _ in unused_recordings:
+            file.unlink()
+            print(f"  Deleted: {file.relative_to(REPO_ROOT / 'tests/integration')}")
+        print("✅ Cleanup complete")
+    else:
+        print("\n(Dry run - no files deleted)")
+        print("\nTo delete these files, run with --delete")
+
+    return len(unused_recordings)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Clean up unused test recordings based on CI test collection",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument("--delete", action="store_true", help="Actually delete unused recordings (default is dry-run)")
+    parser.add_argument("--manifest", help="Save collected test IDs to file (optional)")
+
+    args = parser.parse_args()
+
+    print("=" * 60)
+    print("Recording Cleanup Utility")
+    print("=" * 60)
+
+    ci_configs = iter_all_ci_configs()
+
+    print(f"\nDetected CI configurations: {len(ci_configs)}")
+    for config in ci_configs:
+        print(f"  - suite={config['suite']}, setup={config['setup']}")
+
+    # Collect test IDs from CI configurations
+    ci_test_ids, _ = collect_ci_tests()
+
+    if args.manifest:
+        with open(args.manifest, "w") as f:
+            for test_id in sorted(ci_test_ids):
+                f.write(f"{test_id}\n")
+        print(f"\nSaved test IDs to: {args.manifest}")
+
+    # Analyze and cleanup
+    unused_count = analyze_recordings(ci_test_ids, dry_run=not args.delete)
+
+    print("\n" + "=" * 60)
+    if unused_count > 0 and not args.delete:
+        print("Run with --delete to remove unused recordings")
+
+
+if __name__ == "__main__":
+    main()

scripts/generate_ci_matrix.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+# 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.
+
+"""
+Generate CI test matrix from ci_matrix.json with schedule/input overrides.
+
+This script is used by .github/workflows/integration-tests.yml to generate
+the test matrix dynamically based on the CI_MATRIX definition.
+"""
+
+import json
+from pathlib import Path
+
+CI_MATRIX_FILE = Path(__file__).parent.parent / "tests/integration/ci_matrix.json"
+
+with open(CI_MATRIX_FILE) as f:
+    matrix_config = json.load(f)
+
+DEFAULT_MATRIX = matrix_config["default"]
+SCHEDULE_MATRICES: dict[str, list[dict[str, str]]] = matrix_config.get("schedules", {})
+
+
+def generate_matrix(schedule="", test_setup=""):
+    """
+    Generate test matrix based on schedule or manual input.
+
+    Args:
+        schedule: GitHub cron schedule string (e.g., "1 0 * * 0" for weekly)
+        test_setup: Manual test setup input (e.g., "ollama-vision")
+
+    Returns:
+        Matrix configuration as JSON string
+    """
+    # Weekly scheduled test matrices
+    if schedule and schedule in SCHEDULE_MATRICES:
+        matrix = SCHEDULE_MATRICES[schedule]
+    # Manual input for specific setup
+    elif test_setup == "ollama-vision":
+        matrix = [{"suite": "vision", "setup": "ollama-vision"}]
+    # Default: use JSON-defined matrix
+    else:
+        matrix = DEFAULT_MATRIX
+
+    # GitHub Actions expects {"include": [...]} format
+    return json.dumps({"include": matrix})
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Generate CI test matrix")
+    parser.add_argument("--schedule", default="", help="GitHub schedule cron string")
+    parser.add_argument("--test-setup", default="", help="Manual test setup input")
+
+    args = parser.parse_args()
+
+    print(generate_matrix(args.schedule, args.test_setup))

scripts/integration-tests.sh

@@ -235,6 +235,7 @@ if [[ "$STACK_CONFIG" == *"server:"* && "$COLLECT_ONLY" == false ]]; then
         export OTEL_EXPORTER_OTLP_PROTOCOL="http/protobuf"
         export OTEL_BSP_SCHEDULE_DELAY="200"
         export OTEL_BSP_EXPORT_TIMEOUT="2000"
+        export OTEL_METRIC_EXPORT_INTERVAL="200"
 
     # remove "server:" from STACK_CONFIG
     stack_config=$(echo "$STACK_CONFIG" | sed 's/^server://')
@@ -337,6 +338,9 @@ if [[ "$STACK_CONFIG" == *"docker:"* && "$COLLECT_ONLY" == false ]]; then
     DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e LLAMA_STACK_TEST_INFERENCE_MODE=$INFERENCE_MODE"
     DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e LLAMA_STACK_TEST_STACK_CONFIG_TYPE=server"
     DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:${COLLECTOR_PORT}"
+    DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OTEL_METRIC_EXPORT_INTERVAL=200"
+    DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OTEL_BSP_SCHEDULE_DELAY=200"
+    DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e OTEL_BSP_EXPORT_TIMEOUT=2000"
 
     # Pass through API keys if they exist
     [ -n "${TOGETHER_API_KEY:-}" ] && DOCKER_ENV_VARS="$DOCKER_ENV_VARS -e TOGETHER_API_KEY=$TOGETHER_API_KEY"

src/llama_stack/apis/conversations/__init__.py

@@ -6,26 +6,22 @@
 
 from .conversations import (
     Conversation,
-    ConversationCreateRequest,
     ConversationDeletedResource,
     ConversationItem,
     ConversationItemCreateRequest,
     ConversationItemDeletedResource,
     ConversationItemList,
     Conversations,
-    ConversationUpdateRequest,
     Metadata,
 )
 
 __all__ = [
     "Conversation",
-    "ConversationCreateRequest",
     "ConversationDeletedResource",
     "ConversationItem",
     "ConversationItemCreateRequest",
     "ConversationItemDeletedResource",
     "ConversationItemList",
     "Conversations",
-    "ConversationUpdateRequest",
     "Metadata",
 ]

src/llama_stack/apis/conversations/conversations.py

@@ -102,32 +102,6 @@ register_schema(ConversationItem, name="ConversationItem")
 # ]
 
 
-@json_schema_type
-class ConversationCreateRequest(BaseModel):
-    """Request body for creating a conversation."""
-
-    items: list[ConversationItem] | None = Field(
-        default=[],
-        description="Initial items to include in the conversation context. You may add up to 20 items at a time.",
-        max_length=20,
-    )
-    metadata: Metadata | None = Field(
-        default={},
-        description="Set of 16 key-value pairs that can be attached to an object. Useful for storing additional information",
-        max_length=16,
-    )
-
-
-@json_schema_type
-class ConversationUpdateRequest(BaseModel):
-    """Request body for updating a conversation."""
-
-    metadata: Metadata = Field(
-        ...,
-        description="Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard. Keys are strings with a maximum length of 64 characters. Values are strings with a maximum length of 512 characters.",
-    )
-
-
 @json_schema_type
 class ConversationDeletedResource(BaseModel):
     """Response for deleted conversation."""

src/llama_stack/cli/stack/list_deps.py

@@ -46,6 +46,10 @@ class StackListDeps(Subcommand):
     def _run_stack_list_deps_command(self, args: argparse.Namespace) -> None:
         # always keep implementation completely silo-ed away from CLI so CLI
         # can be fast to load and reduces dependencies
+        if not args.config and not args.providers:
+            self.parser.print_help()
+            self.parser.exit()
+
         from ._list_deps import run_stack_list_deps_command
 
         return run_stack_list_deps_command(args)

src/llama_stack/cli/stack/list_stacks.py

@@ -9,46 +9,67 @@ from pathlib import Path
 
 from llama_stack.cli.subcommand import Subcommand
 from llama_stack.cli.table import print_table
+from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
 
 
 class StackListBuilds(Subcommand):
-    """List built stacks in .llama/distributions directory"""
+    """List available distributions (both built-in and custom)"""
 
     def __init__(self, subparsers: argparse._SubParsersAction):
         super().__init__()
         self.parser = subparsers.add_parser(
             "list",
             prog="llama stack list",
-            description="list the build stacks",
+            description="list available distributions",
             formatter_class=argparse.ArgumentDefaultsHelpFormatter,
         )
         self._add_arguments()
         self.parser.set_defaults(func=self._list_stack_command)
 
-    def _get_distribution_dirs(self) -> dict[str, Path]:
-        """Return a dictionary of distribution names and their paths"""
-        distributions = {}
-        dist_dir = Path.home() / ".llama" / "distributions"
+    def _get_distribution_dirs(self) -> dict[str, tuple[Path, str]]:
+        """Return a dictionary of distribution names and their paths with source type
+
+        Returns:
+            dict mapping distro name to (path, source_type) where source_type is 'built-in' or 'custom'
+        """
+        distributions = {}
+
+        # Get built-in distributions from source code
+        distro_dir = Path(__file__).parent.parent.parent / "distributions"
+        if distro_dir.exists():
+            for stack_dir in distro_dir.iterdir():
+                if stack_dir.is_dir() and not stack_dir.name.startswith(".") and not stack_dir.name.startswith("__"):
+                    distributions[stack_dir.name] = (stack_dir, "built-in")
+
+        # Get custom/run distributions from ~/.llama/distributions
+        # These override built-in ones if they have the same name
+        if DISTRIBS_BASE_DIR.exists():
+            for stack_dir in DISTRIBS_BASE_DIR.iterdir():
+                if stack_dir.is_dir() and not stack_dir.name.startswith("."):
+                    # Clean up the name (remove llamastack- prefix if present)
+                    name = stack_dir.name.replace("llamastack-", "")
+                    distributions[name] = (stack_dir, "custom")
 
-        if dist_dir.exists():
-            for stack_dir in dist_dir.iterdir():
-                if stack_dir.is_dir():
-                    distributions[stack_dir.name] = stack_dir
         return distributions
 
     def _list_stack_command(self, args: argparse.Namespace) -> None:
         distributions = self._get_distribution_dirs()
 
         if not distributions:
-            print("No stacks found in ~/.llama/distributions")
+            print("No distributions found")
             return
 
-        headers = ["Stack Name", "Path"]
-        headers.extend(["Build Config", "Run Config"])
+        headers = ["Stack Name", "Source", "Path", "Build Config", "Run Config"]
         rows = []
-        for name, path in distributions.items():
-            row = [name, str(path)]
+        for name, (path, source_type) in sorted(distributions.items()):
+            row = [name, source_type, str(path)]
             # Check for build and run config files
+            # For built-in distributions, configs are named build.yaml and run.yaml
+            # For custom distributions, configs are named {name}-build.yaml and {name}-run.yaml
+            if source_type == "built-in":
+                build_config = "Yes" if (path / "build.yaml").exists() else "No"
+                run_config = "Yes" if (path / "run.yaml").exists() else "No"
+            else:
                 build_config = "Yes" if (path / f"{name}-build.yaml").exists() else "No"
                 run_config = "Yes" if (path / f"{name}-run.yaml").exists() else "No"
             row.extend([build_config, run_config])

src/llama_stack/core/telemetry/telemetry.py

@@ -427,6 +427,7 @@ _GLOBAL_STORAGE: dict[str, dict[str | int, Any]] = {
     "counters": {},
     "gauges": {},
     "up_down_counters": {},
+    "histograms": {},
 }
 _global_lock = threading.Lock()
 _TRACER_PROVIDER = None
@@ -540,6 +541,16 @@ class Telemetry:
             )
         return cast(metrics.ObservableGauge, _GLOBAL_STORAGE["gauges"][name])
 
+    def _get_or_create_histogram(self, name: str, unit: str) -> metrics.Histogram:
+        assert self.meter is not None
+        if name not in _GLOBAL_STORAGE["histograms"]:
+            _GLOBAL_STORAGE["histograms"][name] = self.meter.create_histogram(
+                name=name,
+                unit=unit,
+                description=f"Histogram for {name}",
+            )
+        return cast(metrics.Histogram, _GLOBAL_STORAGE["histograms"][name])
+
     def _log_metric(self, event: MetricEvent) -> None:
         # Add metric as an event to the current span
         try:
@@ -571,7 +582,16 @@ class Telemetry:
         # Log to OpenTelemetry meter if available
         if self.meter is None:
             return
-        if isinstance(event.value, int):
+
+        # Use histograms for token-related metrics (per-request measurements)
+        # Use counters for other cumulative metrics
+        token_metrics = {"prompt_tokens", "completion_tokens", "total_tokens"}
+
+        if event.metric in token_metrics:
+            # Token metrics are per-request measurements, use histogram
+            histogram = self._get_or_create_histogram(event.metric, event.unit)
+            histogram.record(event.value, attributes=_clean_attributes(event.attributes))
+        elif isinstance(event.value, int):
             counter = self._get_or_create_counter(event.metric, event.unit)
             counter.add(event.value, attributes=_clean_attributes(event.attributes))
         elif isinstance(event.value, float):

src/llama_stack/providers/remote/inference/watsonx/watsonx.py

@@ -283,8 +283,8 @@ class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
             # ...
             provider_resource_id = f"{self.__provider_id__}/{model_spec['model_id']}"
             if "embedding" in functions:
-                embedding_dimension = model_spec["model_limits"]["embedding_dimension"]
-                context_length = model_spec["model_limits"]["max_sequence_length"]
+                embedding_dimension = model_spec.get("model_limits", {}).get("embedding_dimension", 0)
+                context_length = model_spec.get("model_limits", {}).get("max_sequence_length", 0)
                 embedding_metadata = {
                     "embedding_dimension": embedding_dimension,
                     "context_length": context_length,
@@ -306,10 +306,6 @@ class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
                     metadata={},
                     model_type=ModelType.llm,
                 )
-                # In theory, I guess it is possible that a model could be both an embedding model and a text chat model.
-                # In that case, the cache will record the generator Model object, and the list which we return will have
-                # both the generator Model object and the text chat Model object.  That's fine because the cache is
-                # only used for check_model_availability() anyway.
                 self._model_cache[provider_resource_id] = model
                 models.append(model)
         return models

tests/integration/batches/recordings/37b8d062efc39eaa4ac3438f5102044a29fd1e9b844ead2f066a1fb25c85672c.json

@@ -1,422 +0,0 @@
-{
-  "test_id": "tests/integration/batches/test_batches.py::TestBatchesIntegration::test_batch_e2e_embeddings[emb=ollama/all-minilm:l6-v2]",
-  "request": {
-    "method": "POST",
-    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
-    "headers": {},
-    "body": {
-      "model": "all-minilm:l6-v2",
-      "input": "Hello world",
-      "encoding_format": "float"
-    },
-    "endpoint": "/v1/embeddings",
-    "model": "all-minilm:l6-v2"
-  },
-  "response": {
-    "body": {
-      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
-      "__data__": {
-        "data": [
-          {
-            "embedding": [
-              -0.034477483,
-              0.030899182,
-              0.0066526434,
-              0.026075281,
-              -0.039411988,
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-              0.00039833272,
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-              0.017779494,
-              0.09106629,
-              0.11033428,
-              0.00065298256,
-              0.051472265,
-              -0.01461242,
-              0.033237122,
-              0.023671487,
-              -0.022980422,
-              0.038988944,
-              0.030206418
-            ],
-            "index": 0,
-            "object": "embedding"
-          }
-        ],
-        "model": "all-minilm:l6-v2",
-        "object": "list",
-        "usage": {
-          "prompt_tokens": 2,
-          "total_tokens": 2
-        }
-      }
-    },
-    "is_streaming": false
-  },
-  "id_normalization_mapping": {}
-}

tests/integration/ci_matrix.json

@@ -0,0 +1,12 @@
+{
+  "default": [
+    {"suite": "base", "setup": "ollama"},
+    {"suite": "vision", "setup": "ollama-vision"},
+    {"suite": "responses", "setup": "gpt"}
+  ],
+  "schedules": {
+    "1 0 * * 0": [
+      {"suite": "base", "setup": "vllm"}
+    ]
+  }
+}

tests/integration/common/recordings/models-64a2277c90f0f42576f60c1030e3a020403d34a95f56931b792d5939f4cebc57-826d44c3.json

@@ -0,0 +1,89 @@
+{
+  "test_id": null,
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/models",
+    "headers": {},
+    "body": {},
+    "endpoint": "/v1/models",
+    "model": ""
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.2:3b-instruct-fp16",
+          "created": 1760453641,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "qwen3:4b",
+          "created": 1757615302,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "gpt-oss:latest",
+          "created": 1756395223,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "nomic-embed-text:latest",
+          "created": 1756318548,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.2:3b",
+          "created": 1755191039,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "all-minilm:l6-v2",
+          "created": 1753968177,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.2:1b",
+          "created": 1746124735,
+          "object": "model",
+          "owned_by": "library"
+        }
+      },
+      {
+        "__type__": "openai.types.model.Model",
+        "__data__": {
+          "id": "llama3.2:latest",
+          "created": 1746044170,
+          "object": "model",
+          "owned_by": "library"
+        }
+      }
+    ],
+    "is_streaming": false
+  },
+  "id_normalization_mapping": {}
+}

tests/integration/inference/recordings/02bc4eaa12503edd2af031c7f243b280703e4ed17024e21641119530cc78bd0e.json

@@ -1,75 +0,0 @@
-{
-  "test_id": "tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_encoding_format_base64[openai_client-emb=ollama/all-minilm:l6-v2]",
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/ps",
-    "headers": {},
-    "body": {},
-    "endpoint": "/api/ps",
-    "model": ""
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.ProcessResponse",
-      "__data__": {
-        "models": [
-          {
-            "model": "llama3.2:3b",
-            "name": "llama3.2:3b",
-            "digest": "a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72",
-            "expires_at": "2025-10-08T16:14:05.423042-07:00",
-            "size": 3367856128,
-            "size_vram": 3367856128,
-            "details": {
-              "parent_model": "",
-              "format": "gguf",
-              "family": "llama",
-              "families": [
-                "llama"
-              ],
-              "parameter_size": "3.2B",
-              "quantization_level": "Q4_K_M"
-            }
-          },
-          {
-            "model": "all-minilm:l6-v2",
-            "name": "all-minilm:l6-v2",
-            "digest": "1b226e2802dbb772b5fc32a58f103ca1804ef7501331012de126ab22f67475ef",
-            "expires_at": "2025-10-08T11:35:06.037921-07:00",
-            "size": 585846784,
-            "size_vram": 585846784,
-            "details": {
-              "parent_model": "",
-              "format": "gguf",
-              "family": "bert",
-              "families": [
-                "bert"
-              ],
-              "parameter_size": "23M",
-              "quantization_level": "F16"
-            }
-          },
-          {
-            "model": "llama3.2:3b-instruct-fp16",
-            "name": "llama3.2:3b-instruct-fp16",
-            "digest": "195a8c01d91ec3cb1e0aad4624a51f2602c51fa7d96110f8ab5a20c84081804d",
-            "expires_at": "2025-10-08T11:35:04.346635-07:00",
-            "size": 7919570944,
-            "size_vram": 7919570944,
-            "details": {
-              "parent_model": "",
-              "format": "gguf",
-              "family": "llama",
-              "families": [
-                "llama"
-              ],
-              "parameter_size": "3.2B",
-              "quantization_level": "F16"
-            }
-          }
-        ]
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/inference/recordings/0483665572c6b72adda44aa0b31c105c4a5ac4da26d514c61fbd319910e1ce05.json

@@ -1,422 +0,0 @@
-{
-  "test_id": "tests/integration/inference/test_openai_embeddings.py::test_openai_embeddings_with_user_parameter[llama_stack_client-emb=ollama/all-minilm:l6-v2]",
-  "request": {
-    "method": "POST",
-    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
-    "headers": {},
-    "body": {
-      "model": "all-minilm:l6-v2",
-      "input": "Test user parameter",
-      "encoding_format": "float",
-      "user": "test-user-123"
-    },
-    "endpoint": "/v1/embeddings",
-    "model": "all-minilm:l6-v2"
-  },
-  "response": {
-    "body": {
-      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
-      "__data__": {
-        "data": [
-          {
-            "embedding": [
-              0.043779343,
-              0.021533398,
-              -0.081306435,
-              0.010584965,
-              -0.079082854,
-              -0.03219143,
-              0.13092613,
-              0.04234389,
-              -0.11600539,
-              -0.07588513,
-              0.04182356,
-              -0.08061255,
-              0.038127176,
-              -0.010701234,
-              0.015768763,
-              -0.04193689,
-              0.04310592,
-              -0.033361685,
-              0.013566423,
-              -0.010392366,
-              0.015551022,
-              -0.037858423,
-              -0.050305344,
-              -0.025666261,
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tests/integration/inference/recordings/e780faa88ebc38e9a74c5d5fa942921d291f73653719e73301196c0258732bf7.json

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tests/integration/inference/recordings/eb7da4d664ddae8fb27ddefb18dae5008c35c09ca3bf67d3bfa521e7a7a55275.json

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tests/integration/inference/recordings/f02f1bfd75adaea87b91dedc59430b99015b5ed0e2bbf24418a31146ffcbca9b.json

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tests/integration/inference/recordings/f8f023b96c521ba3f3f3f06156012dc518c00dbcb1bff697ba2bb7e18b7476ac.json

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tests/integration/responses/recordings/07b6475c42139e6e3f06323eeef10cba663a61796f4667367aa5084f2e2c2eb6.json

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tests/integration/responses/recordings/0f79646fcf8aaf90bd27658c7c038a495eba448dfde29b58d887281ab0ffe972.json

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tests/integration/responses/recordings/0fd12925d27e6824a956559c79b4c90723b11660134454284bddc28e2275f770.json

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tests/integration/responses/recordings/112ca0696d9265f3301cfe6b263f62aac7bc31f6f2c1191f15030f176e32c855.json

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-              "text": "knowledge_search tool found 2 chunks:\nBEGIN of knowledge_search tool results.\n"
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-              "text": "[1] document_id: file-728796232029, score: 1.4945131220963286, attributes: {'filename': 'llama_stack_and_models.pdf', 'document_id': 'file-728796232029', 'token_count': 98.0, 'metadata_token_count': 11.0} (cite as <|file-728796232029|>)\n,  \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n  \nLlama  4  Maverick  \n Llama  4  Maverick  is  a  Mixture-of-Experts  (MoE)  model  with  17  billion  active  parameters  and  128  experts.   \n"
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tests/integration/responses/recordings/16499abd50909c547f4a980a69dec75cb2931838dc1a18a16cf83bc299c056e0.json

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tests/integration/responses/recordings/1b0c005eb4b93c22ec7211904cab5145194ec832d8e436fee11599c1c2d80fd8.json

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tests/integration/responses/recordings/1cc394167676abff7fb4bcda8322806f6adaa14139f0c610113de8fba24347c2.json

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tests/integration/responses/recordings/4eb6e076a5b400260dc58a211bf90a9bd9bb6c940c4d88aba912ae87a8f66816.json

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tests/integration/responses/recordings/50209c4013650ac6b04e2ac8a5dc865d7e6b17a175e6674499c870797be5951e.json

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tests/integration/responses/recordings/84f3ac4d8f92d4fc884b329f0b516b18e13a02230eb82befeac5bba5b936a99e.json

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