ten31-database/backend/ingest/sync.py

#!/usr/bin/env python3
"""Phase-0 Workstream B4 — incremental, idempotent CRM -> Qdrant sync.

One command that keeps the index fresh:
  1. Re-run deterministic entity resolution (cheap, idempotent, respects durable
     fuzzy merges). Optionally re-run the local-Qwen fuzzy tier (--fuzzy).
  2. Re-embed ONLY the source rows changed since the last sync (by updated_at);
     the first run (or --recreate) is a full backfill.
  3. Upsert with deterministic point ids (overwrite in place) and advance the
     watermark. Logged to interaction_log.

Idempotent: re-running with no CRM changes embeds nothing. Watermark lives in an
`ingest_sync_state` table the pipeline owns.

    python3 backend/ingest/sync.py --db data/crm_dev.db            # incremental (full on first run)
    python3 backend/ingest/sync.py --db data/crm_dev.db --recreate # force full rebuild
    python3 backend/ingest/sync.py --db data/crm_dev.db --fuzzy    # also run the Qwen fuzzy tier

LIMITATION: the CRM hard-deletes today, so a removed row's chunk is not pruned
incrementally (no tombstone). Until the DELETE handlers honor `deleted_at`, run a
periodic `--recreate` (or `backfill.py --recreate`) to drop orphans. Structural
entity-id changes (merges) are likewise best followed by a periodic full rebuild.
"""
import argparse
import json
import sqlite3
import uuid
from datetime import datetime, timezone

import backfill
import chunking
import config
import entity_resolution as er
import qdrant_io

_CHANGE_TABLES = [("communications", "communications"), ("contacts", "contacts"),
                  ("lp_profiles", "lp_profiles"), ("opportunities", "opportunities"),
                  ("organizations", "organizations"), ("fundraising_investors", "fundraising_investors")]


def _now():
    # Match the CRM's updated_at format ("...Z") so the watermark compares
    # correctly against source-row updated_at (server.now() in server.py).
    return datetime.now(timezone.utc).replace(tzinfo=None).isoformat() + "Z"


def _ensure_state(conn):
    conn.execute("""CREATE TABLE IF NOT EXISTS ingest_sync_state (
        key TEXT PRIMARY KEY, value TEXT, updated_at TEXT DEFAULT (datetime('now')))""")


def _state_get(conn, key):
    r = conn.execute("SELECT value FROM ingest_sync_state WHERE key=?", (key,)).fetchone()
    return r[0] if r else None


def _state_set(conn, key, value):
    conn.execute("""INSERT INTO ingest_sync_state (key, value, updated_at) VALUES (?,?,?)
                    ON CONFLICT(key) DO UPDATE SET value=excluded.value, updated_at=excluded.updated_at""",
                 (key, value, _now()))


def _changed_source_ids(conn, since):
    changed = set()
    for tbl, model in _CHANGE_TABLES:
        for r in conn.execute(f"SELECT id FROM {tbl} WHERE updated_at > ?", (since,)):
            changed.add((model, r["id"]))
    if chunking._has_table(conn, "emails"):
        for r in conn.execute("SELECT id FROM emails WHERE updated_at > ? AND is_matched=1", (since,)):
            changed.add(("emails", r["id"]))
    return changed


def run(db, recreate=False, fuzzy=False, batch=32):
    # 1. refresh the canonical layer (deterministic always; fuzzy on request)
    er.run(db)
    if fuzzy:
        import fuzzy_resolve
        fuzzy_resolve.run(db)

    conn = sqlite3.connect(db)
    conn.row_factory = sqlite3.Row
    _ensure_state(conn)
    last = _state_get(conn, "last_sync_ts")
    run_start = _now()

    qdrant_io.create_collection(recreate=recreate)
    qdrant_io.ensure_indexes()

    all_chunks = chunking.build_chunks(conn)
    if last is None or recreate:
        mode, target = "full", all_chunks
    else:
        changed = _changed_source_ids(conn, last)
        mode, target = "incremental", [c for c in all_chunks
                                       if (c["source_model"], c["source_id"]) in changed]

    written = backfill.embed_and_upsert(target, batch=batch, progress=False)
    _state_set(conn, "last_sync_ts", run_start)

    summary = {"mode": mode, "rows_embedded": written, "total_chunks": len(all_chunks),
               "qdrant_points": qdrant_io.count()}
    conn.execute("""INSERT INTO interaction_log
        (id, ts, actor_type, actor_id, action, target_type, payload, source, created_at)
        VALUES (?,?,?,?,?,?,?,?,?)""",
        (str(uuid.uuid4()), _now(), "system", "ingest_sync", "ingest.sync", "crm_chunks",
         json.dumps(summary), "ingest", _now()))
    conn.commit()
    conn.close()
    return summary


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--db", default=config.DEFAULT_DB)
    ap.add_argument("--recreate", action="store_true")
    ap.add_argument("--fuzzy", action="store_true")
    ap.add_argument("--batch", type=int, default=32)
    args = ap.parse_args()
    s = run(args.db, recreate=args.recreate, fuzzy=args.fuzzy, batch=args.batch)
    print(f"Sync ({s['mode']}): embedded {s['rows_embedded']} chunk(s); "
          f"{s['total_chunks']} total; Qdrant now holds {s['qdrant_points']} points.")


if __name__ == "__main__":
    main()