anti
f2b3393669
Replaces LICENSE (GPLv3 -> AGPLv3) and prepends `SPDX-License-Identifier: AGPL-3.0-or-later` to every source file across decnet/, decnet_web/, tests/, scripts/, and tools/. Rationale: closes the GPLv3 ASP loophole so any party operating a modified DECNET as a network service must offer their modified source. Personal copyright (Samuel Paschuan) + inbound=outbound contributions make a future unilateral relicense infeasible. - LICENSE: full AGPL-3.0 text (gnu.org/licenses/agpl-3.0.txt) - COPYRIGHT: project copyright notice - tools/add_spdx_headers.py: idempotent header injector (shebang- and PEP 263-aware) Touches 1565 source files (.py, .ts, .tsx, .js, .jsx, .css, .sh). No behavior change; comments only.
133 lines
4.1 KiB
Python
133 lines
4.1 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-or-later
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"""In-memory vector store backend.
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Two flavors:
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* :class:`FakeVectorStore` — a real, working in-memory store. Used by
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tests and by dev environments that want similarity search without
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any native extension on the box. KNN is brute-force L2 — fine up to
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a few thousand vectors per kind.
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* :class:`NullVectorStore` — a no-op store returned by the factory
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when ``DECNET_VECTORSTORE_ENABLED=false``. Every method succeeds
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trivially; ``get`` and ``knn`` return None / [] respectively. Lets
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workers run unaffected when the operator hasn't opted into vector
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features yet.
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"""
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from __future__ import annotations
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import math
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from typing import Optional, Sequence
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from decnet.vectorstore.base import BaseVectorStore, Neighbor, VectorRecord
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class FakeVectorStore(BaseVectorStore):
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"""Pure-python in-memory vector store, brute-force KNN.
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Suitable for tests and small-scale dev (≤ a few thousand vectors
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per kind). Not persistent — every process restart drops state.
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"""
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def __init__(self) -> None:
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# {kind: {id: VectorRecord}}
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self._store: dict[str, dict[str, VectorRecord]] = {}
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# {kind: dim} — locked the first time a kind is written.
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self._dims: dict[str, int] = {}
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async def initialize(self) -> None:
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return None
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async def close(self) -> None:
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return None
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async def health(self) -> dict:
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total = sum(len(by_id) for by_id in self._store.values())
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return {
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"ok": True,
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"backend": "fake",
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"kinds": len(self._store),
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"vectors": total,
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}
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async def insert(
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self,
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kind: str,
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id: str,
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vector: Sequence[float],
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*,
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extractor_version: int = 1,
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) -> None:
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dim = len(vector)
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existing_dim = self._dims.get(kind)
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if existing_dim is None:
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self._dims[kind] = dim
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elif existing_dim != dim:
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raise ValueError(
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f"vector dim mismatch for kind={kind!r}: "
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f"expected {existing_dim}, got {dim}"
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)
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rec = VectorRecord(
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kind=kind, id=id, vector=tuple(float(x) for x in vector),
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dim=dim, extractor_version=int(extractor_version),
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)
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self._store.setdefault(kind, {})[id] = rec
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async def get(self, kind: str, id: str) -> Optional[VectorRecord]:
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return self._store.get(kind, {}).get(id)
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async def delete(self, kind: str, id: str) -> bool:
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bucket = self._store.get(kind)
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if bucket is None or id not in bucket:
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return False
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del bucket[id]
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return True
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async def knn(
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self, kind: str, vector: Sequence[float], k: int = 10
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) -> list[Neighbor]:
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bucket = self._store.get(kind)
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if not bucket:
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return []
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q = tuple(float(x) for x in vector)
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if len(q) != self._dims.get(kind, len(q)):
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raise ValueError(
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f"query dim {len(q)} != stored dim {self._dims[kind]} "
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f"for kind={kind!r}"
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)
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scored: list[Neighbor] = []
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for rid, rec in bucket.items():
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d = math.sqrt(sum((a - b) ** 2 for a, b in zip(q, rec.vector)))
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scored.append(Neighbor(kind=kind, id=rid, distance=d))
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scored.sort(key=lambda n: n.distance)
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return scored[: max(0, int(k))]
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class NullVectorStore(BaseVectorStore):
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"""No-op vector store. Returned when vectorstore is disabled."""
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async def initialize(self) -> None:
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return None
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async def close(self) -> None:
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return None
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async def health(self) -> dict:
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return {"ok": True, "backend": "null", "kinds": 0, "vectors": 0}
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async def insert(
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self, kind: str, id: str, vector: Sequence[float],
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*, extractor_version: int = 1,
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) -> None:
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return None
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async def get(self, kind: str, id: str) -> Optional[VectorRecord]:
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return None
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async def delete(self, kind: str, id: str) -> bool:
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return False
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async def knn(
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self, kind: str, vector: Sequence[float], k: int = 10
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) -> list[Neighbor]:
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return []
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