Qdrant VectorSearchPort adapter for muscles-data
Project description
muscles-data-qdrant
Qdrant adapter package for muscles-data.
This package is intentionally separate from muscles-data: the core package
owns typed ports, resource runtime and diagnostics, while this package owns the
Qdrant-backed VectorSearchPort implementation.
Related packages
- Core runtime and port contracts:
muscles-data - Elasticsearch search adapter:
muscles-data-elasticsearch - OpenSearch search adapter:
muscles-data-opensearch - Redis key-value/lock/stream adapter:
muscles-data-redis - MongoDB document-store adapter:
muscles-data-mongodb - S3 object-store adapter:
muscles-data-s3 - SQLAlchemy direct SQL resource adapter:
muscles-data-sqlalchemy - Executable example:
example_data_qdrant_1
Usage
Register the factory in the project composition root:
from muscles_data.catalog import DataAdapterCatalog
from muscles_data.ports import VectorSearchPort
from muscles_data.runtime import DataRuntime
from muscles_data_qdrant import QdrantVectorFactory
catalog = DataAdapterCatalog.with_defaults()
catalog.register(QdrantVectorFactory())
runtime = DataRuntime(config=config, catalog=catalog)
vector = runtime.require_port("vector.docs", VectorSearchPort)
Resource config stays in the project:
data:
resources:
vector.docs:
type: qdrant
url_env: QDRANT_URL
api_key: ${QDRANT_API_KEY}
collection: docs
vector_size: 1536
distance: cosine
timeout: 3
prefer_grpc: false
The adapter creates the Qdrant client and collection lazily on vector search/write/delete,
explicit native access or data.doctor. Application code should use
VectorSearchPort; direct client access is only an advanced escape hatch with
native_client: true.
vector_size is required when a missing collection must be created. Request
dimensions are validated against the configured/existing collection, and
keyword payload indexes are created for standard AssetForge filter fields.
Embeddings are not generated by this adapter.
See muscular-example/example_data_qdrant_1 for an executable example.
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