"""Retry attempt #2 — for sources that timed out after MAX_QUEUED_QUERIES bump."""
import json, os, time
from pathlib import Path
import psycopg2, requests
from dotenv import load_dotenv
load_dotenv(Path.home() / "aaronai" / ".env")

GRAPHITI_URL = "http://localhost:8001"
PG_DSN = os.environ["PG_DSN"]
BATCH_SIZE = 3  # smaller batches given timeouts

PRIOR = Path.home() / "aaronai" / "experiments" / "graphiti_bulk_retry.json"
OUT = Path.home() / "aaronai" / "experiments" / "graphiti_bulk_retry2.json"


def fetch_doc(cur, source):
    cur.execute("SELECT STRING_AGG(document, E'\\n\\n' ORDER BY id) FROM embeddings WHERE source = %s", (source,))
    row = cur.fetchone()
    return row[0] if row else None


def submit_batch(batch):
    payload = {"episodes": [
        {"name": s, "content": d[:12000],
         "source_description": "pgvector_migration_bulk_retry2",
         "timestamp": "2026-04-28T00:00:00"}
        for s, d in batch
    ]}
    t0 = time.time()
    try:
        r = requests.post(f"{GRAPHITI_URL}/episodes/bulk", json=payload, timeout=900)
        return {
            "batch_size": len(batch),
            "status_code": r.status_code,
            "elapsed_s": round(time.time() - t0, 2),
            "error": None if r.ok else r.text[:500],
            "sources": [s for s, _ in batch],
        }
    except Exception as e:
        return {
            "batch_size": len(batch),
            "status_code": None,
            "elapsed_s": round(time.time() - t0, 2),
            "error": str(e)[:500],
            "sources": [s for s, _ in batch],
        }


def main():
    prior = json.loads(PRIOR.read_text())
    failed = []
    for r in prior["results"]:
        if r["error"] is not None:
            failed.extend(r["sources"])
    print(f"Retry #2: {len(failed)} sources still failing")

    conn = psycopg2.connect(PG_DSN); cur = conn.cursor()
    sources = []
    for s in failed:
        d = fetch_doc(cur, s)
        if d: sources.append((s, d))
    cur.close(); conn.close()

    batches = [sources[i:i+BATCH_SIZE] for i in range(0, len(sources), BATCH_SIZE)]
    print(f"Submitting {len(batches)} batches of up to {BATCH_SIZE}\n")

    results = []
    for i, batch in enumerate(batches, 1):
        avg = int(sum(len(d) for _, d in batch) / len(batch))
        print(f"[batch {i}/{len(batches)}] n={len(batch)} avg_chars={avg:6d}", end=" ", flush=True)
        r = submit_batch(batch)
        results.append(r)
        if r["error"]: print(f" ERROR: {r['error'][:80]}")
        else: print(f" {r['status_code']} {r['elapsed_s']}s")

    succ = [r for r in results if r["error"] is None]
    fail = [r for r in results if r["error"] is not None]
    summary = {
        "n_sources": len(sources),
        "successful_batches": len(succ),
        "failed_batches": len(fail),
        "successful_episodes": sum(r["batch_size"] for r in succ),
        "failed_episodes": sum(r["batch_size"] for r in fail),
        "results": results,
    }
    OUT.write_text(json.dumps(summary, indent=2))
    print()
    print(f"Episodes: {summary['successful_episodes']}/{len(sources)} succeeded")
    print(f"Full results: {OUT}")


if __name__ == "__main__":
    main()