"""Backfill embeddings for existing memory_facts that lack them.

Run this once after enabling pgvector / embedding backend:
    .venv/bin/python navi/memory/backfill_embeddings.py

What it does:
1. Connects to PostgreSQL via DATABASE_URL
2. Finds facts with embedding IS NULL
3. Generates embeddings in small batches via Ollama (nomic-embed-text)
4. Updates each row with its vector

Safe to run multiple times — only touches rows without embeddings.
"""

import asyncio
import sys

from navi.config import settings
from navi.llm.ollama import OllamaBackend
from navi.memory.store import MemoryStore


async def main() -> None:
    if not settings.database_url:
        print("No DATABASE_URL set — nothing to backfill.")
        sys.exit(0)

    # Build a dedicated embedding backend (may point to a local CPU server
    # while the main chat backend uses Ollama Cloud).
    host = settings.embedding_ollama_host or settings.ollama_host
    api_key = settings.embedding_ollama_api_key or settings.ollama_api_key
    backend = OllamaBackend(
        model=settings.embedding_model,
        host=host,
        api_key=api_key,
    )

    store = MemoryStore(settings.database_url)
    store.set_embedding_backend(backend)

    print("Checking for facts without embeddings...")
    count = await store.backfill_embeddings(batch_size=8)
    print(f"Backfilled {count} embeddings.")


if __name__ == "__main__":
    asyncio.run(main())
