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Oct 7, 2025
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Ensure loader closes Qdrant client between runs #146

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155 changes: 79 additions & 76 deletions mcp_plex/loader/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -364,86 +364,89 @@ async def run(
https=qdrant_https,
prefer_grpc=qdrant_prefer_grpc,
)
collection_name = "media-items"
await _ensure_collection(
client,
collection_name,
dense_size=dense_size,
dense_distance=dense_distance,
)

items: list[AggregatedItem]
if sample_dir is not None:
logger.info("Loading sample data from %s", sample_dir)
sample_items = samples._load_from_sample(sample_dir)
orchestrator, items, qdrant_retry_queue = _build_loader_orchestrator(
client=client,
collection_name=collection_name,
dense_model_name=dense_model_name,
sparse_model_name=sparse_model_name,
tmdb_api_key=None,
sample_items=sample_items,
plex_server=None,
plex_chunk_size=plex_chunk_size,
enrichment_batch_size=enrichment_batch_size,
enrichment_workers=enrichment_workers,
upsert_buffer_size=upsert_buffer_size,
max_concurrent_upserts=max_concurrent_upserts,
imdb_config=IMDbRuntimeConfig(
cache=imdb_config.cache,
max_retries=imdb_config.max_retries,
backoff=imdb_config.backoff,
retry_queue=IMDbRetryQueue(),
requests_per_window=imdb_config.requests_per_window,
window_seconds=imdb_config.window_seconds,
),
qdrant_config=qdrant_config,
try:
collection_name = "media-items"
await _ensure_collection(
client,
collection_name,
dense_size=dense_size,
dense_distance=dense_distance,
)
logger.info("Starting staged loader (sample mode)")
await orchestrator.run()
else:
if PlexServer is None:
raise RuntimeError("plexapi is required for live loading")
if not plex_url or not plex_token:
raise RuntimeError("PLEX_URL and PLEX_TOKEN must be provided")
if not tmdb_api_key:
raise RuntimeError("TMDB_API_KEY must be provided")
logger.info("Loading data from Plex server %s", plex_url)
server = PlexServer(plex_url, plex_token)
orchestrator, items, qdrant_retry_queue = _build_loader_orchestrator(
client=client,
collection_name=collection_name,
dense_model_name=dense_model_name,
sparse_model_name=sparse_model_name,
tmdb_api_key=tmdb_api_key,
sample_items=None,
plex_server=server,
plex_chunk_size=plex_chunk_size,
enrichment_batch_size=enrichment_batch_size,
enrichment_workers=enrichment_workers,
upsert_buffer_size=upsert_buffer_size,
max_concurrent_upserts=max_concurrent_upserts,
imdb_config=imdb_config,
qdrant_config=qdrant_config,
)
logger.info("Starting staged loader (Plex mode)")
await orchestrator.run()
logger.info("Loaded %d items", len(items))
if not items:
logger.info("No points to upsert")

await _process_qdrant_retry_queue(
client,
collection_name,
qdrant_retry_queue,
config=qdrant_config,
)
items: list[AggregatedItem]
if sample_dir is not None:
logger.info("Loading sample data from %s", sample_dir)
sample_items = samples._load_from_sample(sample_dir)
orchestrator, items, qdrant_retry_queue = _build_loader_orchestrator(
client=client,
collection_name=collection_name,
dense_model_name=dense_model_name,
sparse_model_name=sparse_model_name,
tmdb_api_key=None,
sample_items=sample_items,
plex_server=None,
plex_chunk_size=plex_chunk_size,
enrichment_batch_size=enrichment_batch_size,
enrichment_workers=enrichment_workers,
upsert_buffer_size=upsert_buffer_size,
max_concurrent_upserts=max_concurrent_upserts,
imdb_config=IMDbRuntimeConfig(
cache=imdb_config.cache,
max_retries=imdb_config.max_retries,
backoff=imdb_config.backoff,
retry_queue=IMDbRetryQueue(),
requests_per_window=imdb_config.requests_per_window,
window_seconds=imdb_config.window_seconds,
),
qdrant_config=qdrant_config,
)
logger.info("Starting staged loader (sample mode)")
await orchestrator.run()
else:
if PlexServer is None:
raise RuntimeError("plexapi is required for live loading")
if not plex_url or not plex_token:
raise RuntimeError("PLEX_URL and PLEX_TOKEN must be provided")
if not tmdb_api_key:
raise RuntimeError("TMDB_API_KEY must be provided")
logger.info("Loading data from Plex server %s", plex_url)
server = PlexServer(plex_url, plex_token)
orchestrator, items, qdrant_retry_queue = _build_loader_orchestrator(
client=client,
collection_name=collection_name,
dense_model_name=dense_model_name,
sparse_model_name=sparse_model_name,
tmdb_api_key=tmdb_api_key,
sample_items=None,
plex_server=server,
plex_chunk_size=plex_chunk_size,
enrichment_batch_size=enrichment_batch_size,
enrichment_workers=enrichment_workers,
upsert_buffer_size=upsert_buffer_size,
max_concurrent_upserts=max_concurrent_upserts,
imdb_config=imdb_config,
qdrant_config=qdrant_config,
)
logger.info("Starting staged loader (Plex mode)")
await orchestrator.run()
logger.info("Loaded %d items", len(items))
if not items:
logger.info("No points to upsert")

await _process_qdrant_retry_queue(
client,
collection_name,
qdrant_retry_queue,
config=qdrant_config,
)

if imdb_queue_path:
_persist_imdb_retry_queue(imdb_queue_path, imdb_config.retry_queue)
if imdb_queue_path:
_persist_imdb_retry_queue(imdb_queue_path, imdb_config.retry_queue)

json.dump([item.model_dump(mode="json") for item in items], fp=sys.stdout, indent=2)
sys.stdout.write("\n")
json.dump([item.model_dump(mode="json") for item in items], fp=sys.stdout, indent=2)
sys.stdout.write("\n")
finally:
await client.close()


async def load_media(
Expand Down
27 changes: 22 additions & 5 deletions tests/test_loader_integration.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@ class CaptureClient(AsyncQdrantClient):
captured_points: list[models.PointStruct] = []
upsert_calls: int = 0
created_indexes: list[tuple[str, Any]] = []
close_calls: int = 0

def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
Expand Down Expand Up @@ -44,6 +45,10 @@ async def create_payload_index(
wait=wait,
)

async def close(self) -> None:
CaptureClient.close_calls += 1
await super().close()


async def _run_loader(sample_dir: Path, **kwargs) -> None:
await loader.run(
Expand All @@ -62,6 +67,7 @@ def test_run_writes_points(monkeypatch):
CaptureClient.captured_points = []
CaptureClient.upsert_calls = 0
CaptureClient.created_indexes = []
CaptureClient.close_calls = 0
sample_dir = Path(__file__).resolve().parents[1] / "sample-data"
asyncio.run(_run_loader(sample_dir))
client = CaptureClient.instance
Expand All @@ -70,9 +76,6 @@ def test_run_writes_points(monkeypatch):
assert index_map.get("show_title") == models.PayloadSchemaType.KEYWORD
assert index_map.get("season_number") == models.PayloadSchemaType.INTEGER
assert index_map.get("episode_number") == models.PayloadSchemaType.INTEGER
points, _ = asyncio.run(client.scroll("media-items", limit=10, with_payload=True))
assert len(points) == 2
assert all("title" in p.payload and "type" in p.payload for p in points)
captured = CaptureClient.captured_points
assert len(captured) == 2
assert all(isinstance(p.vector["dense"], models.Document) for p in captured)
Expand All @@ -85,7 +88,7 @@ def test_run_writes_points(monkeypatch):
texts = [p.vector["dense"].text for p in captured]
assert any("Directed by" in t for t in texts)
assert any("Starring" in t for t in texts)
movie_point = next(p for p in points if p.payload["type"] == "movie")
movie_point = next(p for p in captured if p.payload["type"] == "movie")
assert "directors" in movie_point.payload and "Guy Ritchie" in movie_point.payload["directors"]
assert "writers" in movie_point.payload and movie_point.payload["writers"]
assert "genres" in movie_point.payload and movie_point.payload["genres"]
Expand All @@ -94,7 +97,7 @@ def test_run_writes_points(monkeypatch):
assert movie_point.payload.get("plot")
assert movie_point.payload.get("tagline")
assert movie_point.payload.get("reviews")
episode_point = next(p for p in points if p.payload["type"] == "episode")
episode_point = next(p for p in captured if p.payload["type"] == "episode")
assert episode_point.payload.get("show_title") == "Alien: Earth"
assert episode_point.payload.get("season_title") == "Season 1"
assert episode_point.payload.get("season_number") == 1
Expand All @@ -110,6 +113,7 @@ def test_run_processes_imdb_queue(monkeypatch, tmp_path):
monkeypatch.setattr(loader, "AsyncQdrantClient", CaptureClient)
CaptureClient.captured_points = []
CaptureClient.upsert_calls = 0
CaptureClient.close_calls = 0
queue_file = tmp_path / "queue.json"
queue_file.write_text(json.dumps(["tt0111161"]))
sample_dir = Path(__file__).resolve().parents[1] / "sample-data"
Expand Down Expand Up @@ -138,12 +142,25 @@ def test_run_upserts_in_batches(monkeypatch):
monkeypatch.setattr(loader, "AsyncQdrantClient", CaptureClient)
CaptureClient.captured_points = []
CaptureClient.upsert_calls = 0
CaptureClient.close_calls = 0
sample_dir = Path(__file__).resolve().parents[1] / "sample-data"
asyncio.run(_run_loader(sample_dir, qdrant_batch_size=1))
assert CaptureClient.upsert_calls == 2
assert len(CaptureClient.captured_points) == 2


def test_run_closes_client_once(monkeypatch):
monkeypatch.setattr(loader, "AsyncQdrantClient", CaptureClient)
CaptureClient.close_calls = 0
sample_dir = Path(__file__).resolve().parents[1] / "sample-data"

asyncio.run(_run_loader(sample_dir))
assert CaptureClient.close_calls == 1

asyncio.run(_run_loader(sample_dir))
assert CaptureClient.close_calls == 2


def test_run_raises_for_unknown_dense_model():
sample_dir = Path(__file__).resolve().parents[1] / "sample-data"

Expand Down