pyqql-edge 0.1.1

Local QQL execution for Python with qdrant-edge and FastEmbed

pyqql-edge

Local QQL execution for Python — qdrant-edge + fastembed, zero network.

import pyqql_edge

# Parser (same API as pyqql)
stmt = pyqql_edge.parse("QUERY 'hello' FROM docs LIMIT 10")[0]
tokens = pyqql_edge.tokenize("QUERY 'test' FROM docs")

# Discover local ONNX models
models = pyqql_edge.list_embedding_models()
# [{'name': 'BGESmallENV15', 'model_code': 'Xenova/bge-small-en-v1.5', 'dim': 384, ...}, ...]

# Edge execution — pick model (default BGESmallENV15 / 384-d)
exec = pyqql_edge.local_executor(
    "./qdrant_data",
    on_disk_payload=False,
    model="bge-small-en-v1.5",        # enum name, HF code, or short alias
    cache_dir="/var/cache/fastembed", # optional
)

# Schema-aware text auto-embed (dense-only, sparse-only, and hybrid)
exec.execute("CREATE COLLECTION docs HYBRID")
exec.execute(
    'UPSERT INTO docs VALUES {id: "550e8400-e29b-41d4-a716-446655440001", text: "hello"}'
)
result = exec.execute("QUERY 'hello' FROM docs USING dense LIMIT 10", on_error="stop")

execute() and execute_async() return the same ExecutionReport dict as pyqql: ok, ordered results, succeeded, and failed.

Prebuilt wheels are provided for Linux x64, Windows x64, and Apple Silicon macOS. ONNX Runtime does not provide the required macOS Intel artifact, so pyqql-edge does not publish a Darwin x64 wheel.

Edge gotchas

Gotcha	Reality
Point IDs	Integers or UUIDs only — "doc-1" is rejected
Text UPSERT into an existing collection	Auto-embedding follows the schema: dense-only gets dense, sparse-only gets sparse, hybrid gets both
QUERY 'text' on HYBRID	Dense+sparse topology is ambiguous, so specify the target with USING <vector_name>
GROUP BY / shard keys	Rejected clearly; never silently ignored in edge mode
Model locked at local_executor()	USING MODEL 'other' mismatches fail
Client lifetime	Call close() (or use Python with Client) before deleting data_dir