mirror of
https://github.com/lllin000/PaperForge.git
synced 2026-07-22 06:50:53 +00:00
40326d6759
embed status now runs a zero-vector k-NN query on each vec0 table that has companion meta rows. If the probe fails (table dropped, extension unavailable, index corrupted), healthy=false and corrupted=true are reported even when meta row counts > 0. Includes integration test that drops vec_fulltext and verifies healthy=false.
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
from __future__ import annotations
|
|
|
|
import json as _json
|
|
import logging
|
|
from pathlib import Path
|
|
|
|
from paperforge.embedding._config import get_api_model
|
|
from paperforge.memory.db import ensure_vec_extension, get_connection, get_memory_db_path
|
|
from paperforge.memory.schema import VEC_EMBEDDING_DIM, ensure_schema
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def get_embed_status(vault: Path) -> dict:
|
|
"""Get vector DB status from sqlite-vec companion tables."""
|
|
db_path = get_memory_db_path(vault)
|
|
exists = db_path.exists()
|
|
chunk_count = 0
|
|
object_chunk_count = 0
|
|
body_chunk_count = 0
|
|
error = ""
|
|
healthy = True
|
|
if exists:
|
|
conn = None
|
|
try:
|
|
conn = get_connection(db_path, read_only=True)
|
|
try:
|
|
ensure_vec_extension(conn)
|
|
except Exception:
|
|
pass
|
|
row_ft = conn.execute("SELECT COUNT(*) AS cnt FROM vec_fulltext_meta").fetchone()
|
|
chunk_count = row_ft["cnt"] if row_ft else 0
|
|
row_body = conn.execute("SELECT COUNT(*) AS cnt FROM vec_body_meta").fetchone()
|
|
body_chunk_count = row_body["cnt"] if row_body else 0
|
|
row_obj = conn.execute("SELECT COUNT(*) AS cnt FROM vec_objects_meta").fetchone()
|
|
object_chunk_count = row_obj["cnt"] if row_obj else 0
|
|
|
|
# -- vec0 k-NN health probe --
|
|
total_meta = chunk_count + body_chunk_count + object_chunk_count
|
|
if total_meta > 0:
|
|
zero_vec = [0.0] * VEC_EMBEDDING_DIM
|
|
zero_json = _json.dumps(zero_vec)
|
|
for vec_table, meta_count in [
|
|
("vec_fulltext", chunk_count),
|
|
("vec_body", body_chunk_count),
|
|
("vec_objects", object_chunk_count),
|
|
]:
|
|
if meta_count > 0:
|
|
conn.execute(
|
|
f"SELECT 1 FROM {vec_table} WHERE embedding MATCH ? AND k = 1",
|
|
(zero_json,),
|
|
)
|
|
except Exception as exc:
|
|
healthy = False
|
|
error = str(exc)
|
|
finally:
|
|
if conn is not None:
|
|
try:
|
|
conn.close()
|
|
except Exception:
|
|
pass
|
|
|
|
model = get_api_model(vault)
|
|
|
|
return {
|
|
"db_exists": exists,
|
|
"chunk_count": chunk_count,
|
|
"body_chunk_count": body_chunk_count,
|
|
"object_chunk_count": object_chunk_count,
|
|
"total_chunks": chunk_count + body_chunk_count + object_chunk_count,
|
|
"model": model,
|
|
"mode": "api",
|
|
"healthy": healthy,
|
|
"corrupted": not healthy,
|
|
"error": error,
|
|
}
|