"""
Retest just the previously-failed batches after raising MAX_QUEUED_QUERIES.
Reads failed sources from graphiti_bulk_cost_test.json and resubmits.
"""
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 = 5

PRIOR_RESULTS = Path.home() / "aaronai" / "experiments" / "graphiti_bulk_cost_test.json"
OUT = Path.home() / "aaronai" / "experiments" / "graphiti_bulk_retry.json"


def fetch_doc_for_source(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_bulk_batch(batch):
    payload = {"episodes": [
        {"name": s, "content": d[:12000],
         "source_description": "pgvector_migration_bulk_retry",
         "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),
            "elapsed_per_episode_s": round((time.time() - t0) / len(batch), 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),
            "elapsed_per_episode_s": None,
            "error": str(e)[:500],
            "sources": [s for s, _ in batch],
        }


def main():
    prior = json.loads(PRIOR_RESULTS.read_text())
    failed_sources = []
    for batch_result in prior["results"]:
        if batch_result["error"] is not None:
            failed_sources.extend(batch_result["sources"])
    print(f"Retrying {len(failed_sources)} previously-failed sources")

    conn = psycopg2.connect(PG_DSN)
    cur = conn.cursor()
    sources_with_docs = []
    for s in failed_sources:
        doc = fetch_doc_for_source(cur, s)
        if doc:
            sources_with_docs.append((s, doc))
        else:
            print(f"  WARN: could not find doc for source {s}")
    cur.close(); conn.close()
    print(f"Loaded {len(sources_with_docs)} source docs")
    print()

    batches = [sources_with_docs[i:i+BATCH_SIZE]
               for i in range(0, len(sources_with_docs), BATCH_SIZE)]

    results = []
    total_start = time.time()
    for i, batch in enumerate(batches, start=1):
        avg = int(sum(len(d) for _, d in batch) / len(batch))
        print(f"[batch {i:2d}/{len(batches)}] n={len(batch)} avg_chars={avg:6d}",
              end=" ", flush=True)
        result = submit_bulk_batch(batch)
        results.append(result)
        if result["error"]:
            print(f" ERROR: {result['error'][:80]}")
        else:
            print(f" {result['status_code']} {result['elapsed_s']}s")
    total_elapsed = time.time() - total_start

    successful = [r for r in results if r["error"] is None]
    failed = [r for r in results if r["error"] is not None]
    summary = {
        "n_retry_sources": len(sources_with_docs),
        "n_batches": len(batches),
        "successful_batches": len(successful),
        "failed_batches": len(failed),
        "successful_episodes": sum(r["batch_size"] for r in successful),
        "failed_episodes": sum(r["batch_size"] for r in failed),
        "total_elapsed_s": round(total_elapsed, 1),
        "results": results,
    }
    OUT.write_text(json.dumps(summary, indent=2))

    print()
    print("=" * 60)
    print("RETRY RESULTS")
    print("=" * 60)
    print(f"Episodes: {summary['successful_episodes']}/{len(sources_with_docs)} succeeded")
    print(f"Batches:  {summary['successful_batches']}/{summary['n_batches']} succeeded")
    print(f"Total elapsed: {summary['total_elapsed_s']}s")
    print()
    print(f"Full results: {OUT}")


if __name__ == "__main__":
    main()