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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

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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