"""
agent.py — Vault Tagger + Embeddings Agent

Job:
    Run the vault-tagger LLM (Qwen2.5-7B via Ollama) over new or changed vault documents,
    update their frontmatter with corrected tags/type/entities, then re-embed them into
    Qdrant via the existing bge-m3 TEI service.

Pipeline per document:
    1. Read doc + current frontmatter
    2. Load vocab: topic_categories from config.yaml + entity lexicon from vocab.json
    3. Call vault-tagger via Ollama /api/generate (JSON mode, temp 0.1)
    4. Parse JSON response; validate fields against vocabulary
    5. If confidence >= threshold: apply tags/type to frontmatter (auto_apply)
       Else: flag in changelog, do not modify file
    6. Re-embed via TEI bge-m3 and upsert into Qdrant (collection: vault_docs)
    7. Update related wikilinks in frontmatter.related if embedding similarity > 0.85
    8. Append changelog entry (file, old tags, new tags, confidence, timestamp)

State tracking:
    Maintains engine/.last_sweep (ISO timestamp) to process only docs modified since last run.
    Pass --full to reprocess all docs.

Implemented in: Step 5
"""

# TODO (Step 5): imports — pathlib, json, yaml, httpx or requests, datetime, argparse, logging


def load_config(config_path: str) -> dict:
    """Load and return parsed config.yaml."""
    raise NotImplementedError("implemented in step 5")


def load_vocab(engine_dir: str) -> dict:
    """Load topic_categories from config + entity lexicon from vocab.json."""
    raise NotImplementedError("implemented in step 5")


def get_changed_docs(vault_dir: str, since: str) -> list:
    """Return list of .md paths modified after `since` (ISO timestamp)."""
    raise NotImplementedError("implemented in step 5")


def call_tagger(doc_text: str, vocab: dict, config: dict) -> dict:
    """
    POST to Ollama /api/generate with vault-tagger model.
    Returns parsed JSON response dict.
    """
    raise NotImplementedError("implemented in step 5")


def embed_document(doc_text: str, tei_endpoint: str) -> list[float]:
    """POST to TEI bge-m3 endpoint; return embedding vector."""
    raise NotImplementedError("implemented in step 5")


def upsert_qdrant(doc_id: str, vector: list[float], payload: dict, config: dict) -> None:
    """Upsert a document vector + metadata into Qdrant vault_docs collection."""
    raise NotImplementedError("implemented in step 5")


def find_related(doc_id: str, vector: list[float], config: dict, threshold: float = 0.85) -> list[str]:
    """Query Qdrant for nearest neighbours above threshold; return doc ids."""
    raise NotImplementedError("implemented in step 5")


def apply_tagger_result(path, result: dict, config: dict) -> dict:
    """
    Write tagger output back to doc frontmatter if confidence >= threshold.
    Returns summary of changes made.
    """
    raise NotImplementedError("implemented in step 5")


def main() -> None:
    """Entry point. Parse args, load state, process changed docs, update state."""
    raise NotImplementedError("implemented in step 5")


if __name__ == "__main__":
    main()