281 lines
9.3 KiB
Python
281 lines
9.3 KiB
Python
#!/usr/bin/env python3
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"""
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Migrate RAG data from Redis to Supabase rag_vectors table.
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Reads all documents from 10 Redis collections and upserts into pgvector.
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Handles mixed embedding dimensions by padding to 640.
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Processes in batches of 100. Idempotent (skips already-migrated docs).
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"""
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import asyncio
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import json
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import logging
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import os
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import sys
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import time
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import httpx
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import redis.asyncio as aioredis
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from dotenv import load_dotenv
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load_dotenv("/app/.env", override=True)
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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)
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logger = logging.getLogger(__name__)
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# Target dimension — max across all collections is 640 (token_analysis)
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TARGET_DIM = 640
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COLLECTIONS = [
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"wallet_profiles",
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"token_analysis",
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"known_scams",
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"scam_patterns",
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"forensic_reports",
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"market_intel",
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"contract_audits",
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"news_articles",
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"transaction_patterns",
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"general",
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]
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BATCH_SIZE = 100
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def _get_url():
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return os.environ.get("SUPABASE_URL", "")
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def _get_key():
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return os.environ.get("SUPABASE_SERVICE_KEY", "") or os.environ.get("SUPABASE_SERVICE_ROLE_KEY", "")
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def _get_headers():
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key = _get_key()
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return {
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"apikey": key,
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"Authorization": f"Bearer {key}",
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"Content-Type": "application/json",
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"Prefer": "resolution=merge-duplicates",
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}
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def _get_redis_password():
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return os.environ.get("REDIS_PASSWORD", "")
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def pad_vector(vec: list[float], target_dim: int) -> list[float]:
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"""Pad or truncate embedding vector to target dimension."""
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if len(vec) == target_dim:
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return vec
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if len(vec) > target_dim:
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return vec[:target_dim]
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return vec + [0.0] * (target_dim - len(vec))
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async def get_existing_ids(client: httpx.AsyncClient) -> set:
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"""Fetch all existing doc IDs from rag_vectors to skip already-migrated docs."""
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existing = set()
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offset = 0
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limit = 1000
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while True:
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url = f"{_get_url()}/rest/v1/rag_vectors"
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params = {"select": "id", "limit": str(limit), "offset": str(offset), "order": "id"}
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try:
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resp = await client.get(url, params=params, headers=_get_headers(), timeout=30)
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if resp.status_code != 200:
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logger.warning(f"Could not fetch existing IDs: {resp.status_code} {resp.text[:300]}")
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break
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rows = resp.json() if resp.text else []
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if not rows:
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break
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for row in rows:
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existing.add(row["id"])
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if len(rows) < limit:
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break
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offset += limit
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except Exception as e:
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logger.warning(f"Error fetching existing IDs: {e}")
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break
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logger.info(f"Found {len(existing)} existing docs in rag_vectors")
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return existing
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async def insert_batch(client: httpx.AsyncClient, rows: list[dict]) -> int:
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"""Insert a batch of rows into rag_vectors via Supabase REST API."""
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url = f"{_get_url()}/rest/v1/rag_vectors"
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params = {"on_conflict": "id"}
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try:
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resp = await client.post(url, json=rows, params=params, headers=_get_headers(), timeout=120)
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if resp.status_code in (200, 201):
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return len(rows)
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else:
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logger.error(f"Batch insert failed: {resp.status_code} {resp.text[:500]}")
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# Fallback: try one by one
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success = 0
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for row in rows:
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try:
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r2 = await client.post(url, json=[row], params=params, headers=_get_headers(), timeout=60)
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if r2.status_code in (200, 201):
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success += 1
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else:
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if r2.status_code == 400 and "embedding" in (r2.text or ""):
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logger.warning(f"Skipping {row['id']} due to embedding error")
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else:
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logger.warning(f"Single insert failed for {row['id']}: {r2.status_code} {r2.text[:200]}")
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except Exception as e:
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logger.warning(f"Single insert error for {row['id']}: {e}")
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return success
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except Exception as e:
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logger.error(f"Batch insert exception: {e}")
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return 0
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async def migrate_collection(r: aioredis.Redis, client: httpx.AsyncClient, collection: str, existing_ids: set) -> int:
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"""Migrate all docs from one Redis collection to Supabase."""
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idx_key = f"rag:idx:{collection}"
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doc_ids = await r.smembers(idx_key)
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if not doc_ids:
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logger.info(f"Collection {collection}: empty, skipping")
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return 0
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total_docs = len(doc_ids)
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logger.info(f"Collection {collection}: {total_docs} docs to process")
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# Filter out already-migrated docs
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to_migrate = [did for did in doc_ids if did not in existing_ids]
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skipped = total_docs - len(to_migrate)
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if skipped > 0:
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logger.info(f" Skipping {skipped} already-migrated docs")
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migrated = 0
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batch_rows = []
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batch_count = 0
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errors = 0
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for i, doc_id in enumerate(to_migrate):
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doc_key = f"rag:{collection}:{doc_id}"
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try:
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data = await r.get(doc_key)
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if not data:
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logger.debug(f" Doc {doc_id}: not found in Redis")
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continue
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doc = json.loads(data)
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# Extract fields
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vector = doc.get("vector", [])
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content = doc.get("content", "") or ""
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metadata = doc.get("metadata", {}) or {}
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source = metadata.get("source", "") or doc.get("source", "") or ""
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severity = metadata.get("severity", "") or doc.get("severity", "") or "medium"
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chain = metadata.get("chain", "") or doc.get("chain", "") or ""
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# Pad vector to target dimension
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if vector:
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padded = pad_vector(vector, TARGET_DIM)
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else:
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padded = [0.0] * TARGET_DIM
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logger.debug(f" Doc {doc_id}: no embedding, using zero vector")
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row = {
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"id": str(doc_id),
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"collection": collection,
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"content": content[:10000],
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"embedding": padded,
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"metadata": json.dumps(metadata) if isinstance(metadata, dict) else str(metadata),
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"source": source[:200] if source else "",
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"severity": severity[:50] if severity else "medium",
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"chain": chain[:50] if chain else "",
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}
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batch_rows.append(row)
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if len(batch_rows) >= BATCH_SIZE:
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count = await insert_batch(client, batch_rows)
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migrated += count
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batch_count += 1
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logger.info(
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f" Batch {batch_count}: inserted {count}/{len(batch_rows)} ({migrated}/{len(to_migrate)} total)"
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)
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batch_rows = []
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await asyncio.sleep(0.05)
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except json.JSONDecodeError as e:
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errors += 1
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if errors <= 5:
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logger.warning(f" Doc {doc_id}: JSON decode error: {e}")
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except Exception as e:
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errors += 1
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if errors <= 5:
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logger.warning(f" Doc {doc_id}: error: {e}")
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# Progress every 500 docs
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if (i + 1) % 500 == 0:
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logger.info(f" Progress: processed {i + 1}/{len(to_migrate)}, migrated {migrated}, errors {errors}")
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# Flush remaining batch
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if batch_rows:
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count = await insert_batch(client, batch_rows)
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migrated += count
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batch_count += 1
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logger.info(f" Final batch {batch_count}: inserted {count}/{len(batch_rows)}")
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logger.info(
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f"Collection {collection}: migrated {migrated}/{len(to_migrate)} docs ({skipped} skipped, {errors} errors)"
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)
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return migrated
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async def main():
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logger.info("Starting Redis -> Supabase rag_vectors migration")
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logger.info(f"Supabase URL: {_get_url()}")
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logger.info(f"Redis: {os.environ.get('REDIS_HOST', 'rmi-redis')}:{os.environ.get('REDIS_PORT', '6379')}")
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logger.info(f"Target vector dimension: {TARGET_DIM}")
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logger.info(f"Collections: {COLLECTIONS}")
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if not _get_url() or not _get_key():
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logger.error("SUPABASE_URL and SUPABASE_SERVICE_KEY must be set")
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sys.exit(1)
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redis_password = _get_redis_password()
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redis_host = os.environ.get("REDIS_HOST", "rmi-redis")
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redis_port = int(os.environ.get("REDIS_PORT", "6379"))
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# Connect to Redis
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r = aioredis.Redis(
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host=redis_host,
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port=redis_port,
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password=redis_password,
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decode_responses=True,
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)
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await r.ping()
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logger.info("Redis connected")
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# HTTP client for Supabase
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async with httpx.AsyncClient(timeout=120) as client:
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# Get existing IDs for idempotency
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existing_ids = await get_existing_ids(client)
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total_migrated = 0
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start_time = time.time()
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for collection in COLLECTIONS:
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try:
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count = await migrate_collection(r, client, collection, existing_ids)
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total_migrated += count
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except Exception as e:
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logger.error(f"Collection {collection} failed: {e}")
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elapsed = time.time() - start_time
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logger.info("=" * 60)
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logger.info(f"Migration complete: {total_migrated} docs migrated in {elapsed:.1f}s")
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await r.aclose()
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if __name__ == "__main__":
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asyncio.run(main())
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