87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
"""VectorStore 统一接口与 sqlite-vec 实现。
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vec0 虚拟表返回的 distance 是欧氏距离（非平方）。入库前向量已做 L2 归一化，
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因此 distance² = 2(1-cos)，余弦相似度 = 1 - distance² / 2。
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Protocol, runtime_checkable
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import sqlite_vec
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from app.database.db import connect, transaction
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@dataclass
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class VectorRecord:
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id: str
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vector: list[float]
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@dataclass
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class VectorHit:
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id: str
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score: float # 余弦相似度 [0,1]
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@runtime_checkable
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class VectorStore(Protocol):
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"""统一向量存储接口（与文档一致）。上层只依赖此抽象，不读 vec0 内部表。"""
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async def upsert(self, records: list[VectorRecord]) -> None: ...
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async def delete(self, ids: list[str]) -> None: ...
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async def search(self, vector: list[float], *, top_k: int) -> list[VectorHit]: ...
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class SqliteVecStore:
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"""sqlite-vec 默认实现。"""
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async def upsert(self, records: list[VectorRecord]) -> None:
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if not records:
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return
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conn = connect()
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try:
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with transaction(conn):
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for record in records:
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conn.execute(
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"INSERT INTO vec_blocks (block_id, embedding) VALUES (?, ?)",
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(record.id, sqlite_vec.serialize_float32(record.vector)),
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)
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finally:
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conn.close()
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async def delete(self, ids: list[str]) -> None:
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if not ids:
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return
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conn = connect()
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try:
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with transaction(conn):
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for bid in ids:
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conn.execute("DELETE FROM vec_blocks WHERE block_id = ?", (bid,))
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finally:
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conn.close()
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async def search(self, vector: list[float], *, top_k: int) -> list[VectorHit]:
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conn = connect()
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try:
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rows = conn.execute(
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"SELECT block_id, distance FROM vec_blocks WHERE embedding MATCH ? AND k = ?",
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(sqlite_vec.serialize_float32(vector), top_k),
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).fetchall()
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return [
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VectorHit(id=row["block_id"], score=max(0.0, 1.0 - row["distance"] ** 2 / 2.0))
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for row in rows
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]
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finally:
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conn.close()
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async def clear(self) -> None:
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conn = connect()
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try:
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with transaction(conn):
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conn.execute("DELETE FROM vec_blocks")
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finally:
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conn.close()
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