chore(ci): remove unused recordings (#4074)

Added a script to cleanup recordings. While doing this, moved the CI
matrix generation to a separate script so there is a single source of
truth for the matrix.

Ran the cleanup script as:
```
PYTHONPATH=. python scripts/cleanup_recordings.py
```

Also added this as part of the pre-commit workflow to ensure that the
recordings are always up to date and that no stale recordings are left
in the repo.

.github/workflows/integration-tests.yml

@@ -22,6 +22,8 @@ on:
       - '.github/actions/setup-ollama/action.yml'
       - '.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
@@ -43,8 +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) }}
 
@@ -55,18 +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
+        # Test configurations: Generated from CI_MATRIX in tests/integration/suites.py
-        # Weekly schedule (Sun 1 AM): vllm+base
+        # See scripts/generate_ci_matrix.py for generation logic
-        # Input test-setup=ollama-vision: ollama-vision+vision
+        config: ${{ fromJSON(needs.generate-matrix.outputs.matrix).include }}
-        # 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"}]')
-          }}
 
     steps:
       - name: Checkout repository

.github/workflows/pre-commit.yml

@@ -166,3 +166,14 @@ 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))

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,
-              -0.16037956,
-              0.06692074,
-              -0.006511468,
-              -0.047467157,
-              0.014774274,
-              0.07094562,
-              0.055527706,
-              0.019183245,
-              -0.026297163,
-              -0.010018651,
-              -0.02694715,
-              0.0223884,
-              -0.02220693,
-              -0.14977267,
-              -0.017530814,
-              0.0075938613,
-              0.054253556,
-              0.0032258728,
-              0.031724673,
-              -0.08466085,
-              -0.029342307,
-              0.05155048,
-              0.048105717,
-              -0.0032670307,
-              -0.05822795,
-              0.041971523,
-              0.022229431,
-              0.1281518,
-              -0.022270948,
-              -0.011725874,
-              0.06294936,
-              -0.032847952,
-              -0.09124354,
-              -0.031128692,
-              0.05274829,
-              0.047067728,
-              -0.08414196,
-              -0.029979317,
-              -0.020692566,
-              0.00949804,
-              -0.0035992558,
-              0.0074442336,
-              0.03928378,
-              0.09326073,
-              -0.0037437282,
-              -0.052663893,
-              -0.058101393,
-              -0.006925679,
-              0.0052269334,
-              0.08290669,
-              0.019312402,
-              0.0062818974,
-              -0.010331665,
-              0.008930684,
-              -0.037712026,
-              -0.045175705,
-              0.023950849,
-              -0.006926045,
-              0.013429504,
-              0.100098,
-              -0.0715888,
-              -0.021700105,
-              0.031693522,
-              -0.05161389,
-              -0.08224763,
-              -0.06577986,
-              -0.009853981,
-              0.005808086,
-              0.07364217,
-              -0.034008067,
-              0.024907362,
-              0.014441484,
-              0.02645124,
-              0.009659713,
-              0.030284341,
-              0.052878983,
-              -0.07536944,
-              0.009890014,
-              0.029907802,
-              0.017498897,
-              0.02313779,
-              0.0018918256,
-              0.0013156217,
-              -0.047173936,
-              -0.011251131,
-              -0.11422648,
-              -0.019960148,
-              0.040278148,
-              0.0022633963,
-              -0.07986738,
-              -0.025357265,
-              0.094500035,
-              -0.029062947,
-              -0.14495483,
-              0.2309815,
-              0.027703581,
-              0.03208736,
-              0.031073036,
-              0.042917974,
-              0.064246915,
-              0.032118786,
-              -0.004844535,
-              0.055775862,
-              -0.03756279,
-              -0.021487191,
-              -0.028432492,
-              -0.028887685,
-              0.03842892,
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-              -0.020587107,
-              0.013077965,
-              0.028824588,
-              -3.1978743e-33,
-              0.06475607,
-              -0.018065408,
-              0.05190019,
-              0.12193858,
-              0.028755108,
-              0.008794777,
-              -0.07044016,
-              -0.016856866,
-              0.040675826,
-              0.04222898,
-              0.025450956,
-              0.035772353,
-              -0.049134083,
-              0.0021395232,
-              -0.015527445,
-              0.05065655,
-              -0.04814189,
-              0.03586998,
-              -0.004134139,
-              0.10165314,
-              -0.055980552,
-              -0.010677752,
-              0.011231545,
-              0.09068785,
-              0.004311188,
-              0.035094332,
-              -0.009658399,
-              -0.09383056,
-              0.092755266,
-              0.00799794,
-              -0.0077075018,
-              -0.052119244,
-              -0.01259255,
-              0.0032277475,
-              0.005989667,
-              0.0075889886,
-              0.010571857,
-              -0.08629758,
-              -0.06985891,
-              -0.002511263,
-              -0.091053724,
-              0.0468712,
-              0.05203361,
-              0.0072902967,
-              0.010906411,
-              -0.0052922186,
-              0.013883815,
-              0.021929385,
-              0.0341257,
-              0.060227357,
-              0.00018942523,
-              0.0146624865,
-              -0.07000342,
-              0.028425341,
-              -0.027542787,
-              0.01082086,
-              0.03491755,
-              -0.022430921,
-              0.0096813915,
-              0.07725412,
-              0.021618832,
-              0.114911504,
-              -0.06805403,
-              0.023872944,
-              -0.015999107,
-              -0.017794114,
-              0.06442477,
-              0.03206309,
-              0.050293576,
-              -0.005988605,
-              -0.03376946,
-              0.017821673,
-              0.016567992,
-              0.063335925,
-              0.034753703,
-              0.046586752,
-              0.09789875,
-              -0.006560692,
-              0.025039855,
-              -0.07780643,
-              0.016878096,
-              -0.0010056288,
-              0.02257608,
-              -0.0382721,
-              0.09572481,
-              -0.005296001,
-              0.010567662,
-              -0.11538674,
-              -0.013233586,
-              -0.010786205,
-              -0.083147496,
-              0.073254965,
-              0.049377624,
-              -0.009025328,
-              -0.0957893,
-              3.3687185e-33,
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tests/integration/ci_matrix.json

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-{
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tests/integration/inference/recordings/d2ba309413e85d6166f7543a879b890b4e65a5f9917a2d75c5795782ab7cbfff.json

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tests/integration/inference/recordings/d5b0723b5cea73527f33a5f3c0508ca5ff9df6fb940311653cfab6d01a56d155.json

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tests/integration/inference/recordings/e3727f6c749ab8bdee2f581300092002485023b937d72b7aa8d4c15c9204fc5c.json

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tests/integration/inference/recordings/e780faa88ebc38e9a74c5d5fa942921d291f73653719e73301196c0258732bf7.json

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-            "size_vram": 2350966784,
-            "details": {
-              "parent_model": "",
-              "format": "gguf",
-              "family": "llama",
-              "families": [
-                "llama"
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-              "parameter_size": "1.5B",
-              "quantization_level": "Q8_0"
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-    "is_streaming": false
-  }
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tests/integration/responses/recordings/07b6475c42139e6e3f06323eeef10cba663a61796f4667367aa5084f2e2c2eb6.json

@@ -1,372 +0,0 @@
-{
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-          "content": "How many experts does the Llama 4 Maverick model have?"
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-        {
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-          "content": "",
-          "tool_calls": [
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-              "id": "call_aPe1vS1v5bIwPgl789D5bfmW",
-              "type": "function",
-              "function": {
-                "name": "knowledge_search",
-                "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}"
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-          ]
-        },
-        {
-          "role": "tool",
-          "tool_call_id": "call_aPe1vS1v5bIwPgl789D5bfmW",
-          "content": [
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-              "type": "text",
-              "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n"
-            },
-            {
-              "type": "text",
-              "text": "[1] document_id: file-690481987689, score: 2.5781234969335522, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-690481987689', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-690481987689|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n"
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-            },
-            {
-              "type": "text",
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-        {
-          "role": "assistant",
-          "content": "The Llama 4 Maverick model has 128 experts in its mixture of experts architecture <|file-690481987689|>."
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-          "role": "user",
-          "content": "Can you tell me more about the architecture?"
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-          "tool_call_id": "call_UqyVZyvMh30eQuiKg7lJIUhQ",
-          "content": [
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-              "text": "END of knowledge_search tool results.\n"
-            },
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-              "type": "text",
-              "text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model architecture\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n"
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tests/integration/responses/recordings/08f3fbf0d55f3e5a0c98b89e144663cbc5b0ea707de71feaa4c47a585d4e0073.json

@@ -1,501 +0,0 @@
-{
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-  "request": {
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-    "headers": {},
-    "body": {
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-      "messages": [
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-          "role": "user",
-          "content": [
-            {
-              "type": "text",
-              "text": "what teams are playing in this image?"
-            }
-          ]
-        },
-        {
-          "role": "user",
-          "content": [
-            {
-              "type": "image_url",
-              "image_url": {
-                "url": "https://upload.wikimedia.org/wikipedia/commons/3/3b/LeBron_James_Layup_%28Cleveland_vs_Brooklyn_2018%29.jpg",
-                "detail": "auto"
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-    "endpoint": "/v1/chat/completions",
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tests/integration/responses/recordings/0fd12925d27e6824a956559c79b4c90723b11660134454284bddc28e2275f770.json

@@ -1,219 +0,0 @@
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-          "content": "How many experts does the Llama 4 Maverick model have?"
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tests/integration/responses/recordings/112ca0696d9265f3301cfe6b263f62aac7bc31f6f2c1191f15030f176e32c855.json

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tests/integration/responses/recordings/2f3f766a9601f4ffcde5522361da752354dbf42d982c0a98f4f1113f9cc03769.json

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-{
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-          "content": "How many experts does the Llama 4 Maverick model have?"
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-        {
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-          "tool_call_id": "call_90pCu8l9ITbz463ZJxhGGKm3",
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-              "text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of experts\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n"
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tests/integration/responses/recordings/323c7637263f3623d58c73a3584af9d9f3d3190b2a2beec72bf5c34d8c196d51.json

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-          "content": "How many experts does the Llama 4 Maverick model have?"
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tests/integration/responses/recordings/50209c4013650ac6b04e2ac8a5dc865d7e6b17a175e6674499c870797be5951e.json

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tests/integration/responses/recordings/53ee2f8e45e7a4aa0c39a3f2691d544dfeefbc3678e08d4add8140f9127f50a1.json

@@ -1,732 +0,0 @@
-{
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tests/integration/responses/recordings/635f9971c8d6855fb581204bf37e5d0577d3d38e286c5796aed6f72ebdbbca53.json

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tests/integration/responses/recordings/6bc34054dd782e9c2100053a55bad1b5973ae19f8e75c847bef36d71fccd804e.json

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-              "text": "[1] document_id: file-433408948870, score: 1.4947232325305748, attributes: {'filename': 'llama_stack_and_models.pdf', 'document_id': 'file-433408948870', 'token_count': 98.0, 'metadata_token_count': 11.0} (cite as <|file-433408948870|>)\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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-              "text": "[2] document_id: file-433408948870, score: 1.1417523389560924, attributes: {'filename': 'llama_stack_and_models.pdf', 'document_id': 'file-433408948870', 'token_count': 498.0, 'metadata_token_count': 11.0} (cite as <|file-433408948870|>)\nLlama  Stack  \nLlama  Stack  Overview  \nLlama  Stack  standardizes  the  core  building  blocks  that  simplify  AI  application  development.  It  codifies  best  \npractices\n \nacross\n \nthe\n \nLlama\n \necosystem.\n \nMore\n \nspecifically,\n \nit\n \nprovides\n  \u25cf  Unified  API  layer  for  Inference,  RAG,  Agents,  Tools,  Safety,  Evals,  and  Telemetry.  \u25cf  Plugin  architecture  to  support  the  rich  ecosystem  of  different  API  implementations  in  various  \nenvironments,\n \nincluding\n \nlocal\n \ndevelopment,\n \non-premises,\n \ncloud,\n \nand\n \nmobile.\n \u25cf  Prepackaged  verified  distributions  which  offer  a  one-stop  solution  for  developers  to  get  started  quickly  \nand\n \nreliably\n \nin\n \nany\n \nenvironment.\n \u25cf  Multiple  developer  interfaces  like  CLI  and  SDKs  for  Python,  Typescript,  iOS,  and  Android.  \u25cf  Standalone  applications  as  examples  for  how  to  build  production-grade  AI  applications  with  Llama  \nStack.\n \nLlama  Stack  Benefits  \n\u25cf  Flexible  Options:  Developers  can  choose  their  preferred  infrastructure  without  changing  APIs  and  enjoy  \nflexible\n \ndeployment\n \nchoices.\n \u25cf  Consistent  Experience:  With  its  unified  APIs,  Llama  Stack  makes  it  easier  to  build,  test,  and  deploy  AI  \napplications\n \nwith\n \nconsistent\n \napplication\n \nbehavior.\n \u25cf  Robust  Ecosystem:  Llama  Stack  is  already  integrated  with  distribution  partners  (cloud  providers,  \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/78f9446cb2483639d2d49d73e5774b17078adbf2f522081ad08befd8a79eb96d.json

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tests/integration/responses/recordings/813ac454f8df150f6a9918ddd8a68279f2e21c8948779350810cdf51987ed559.json

@@ -1,219 +0,0 @@
-{
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-          "role": "user",
-          "content": "How many experts does the Llama 4 Maverick model have?"
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-        {
-          "role": "assistant",
-          "content": "",
-          "tool_calls": [
-            {
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-              "id": "call_zS2WxgXWetjnlPt2MzH9Asrc",
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-              "function": {
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-                "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}"
-              }
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-          ]
-        },
-        {
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-            {
-              "type": "text",
-              "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n"
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-              "type": "text",
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-            },
-            {
-              "type": "text",
-              "text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of experts\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n"
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-      "stream": true,
-      "tools": [
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-              "required": [
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tests/integration/responses/recordings/818fcb5332bf6025c6550a3c5acffc75d16dbe1fe0d904783b52c844593463ab.json

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tests/integration/responses/recordings/8ce76cb00986647bbb2175d73915eee068824175e094bf70e5d0fe4711550632.json

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tests/integration/responses/recordings/9303128e4af555495750ef8bf042b6f0940445a217e011c6204a183f51eba35c.json

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