"""
Phase 5 — Memory Search (ChromaDB)
Appended to agents.py functions for Memory Search.
"""
import chromadb


def _get_chroma_client():
    """Get or create the shared ChromaDB HTTP client (thread-safe singleton)."""
    if not hasattr(_get_chroma_client, "_client"):
        _get_chroma_client._client = chromadb.HttpClient(host="127.0.0.1", port=8000)
    return _get_chroma_client._client


def _search_agent_memory(agent_id: str, query: str, limit: int = 10) -> list:
    """
    Search memory for a specific agent.
    Searches the rose_memory collection filtered by topic matching agent_id.
    """
    try:
        client = _get_chroma_client()
        coll = client.get_collection(name="rose_memory")
        results = coll.query(
            query_texts=[query],
            n_results=limit,
            include=["metadatas", "documents"],
        )
        matches = []
        for i, doc in enumerate(results.get("documents", [[]])[0] or []):
            meta = (results.get("metadatas", [[{}]])[0] or [{}])[i] or {}
            topic = meta.get("topic", "")
            # Filter: only docs that belong to this agent (topic starts with agent_id)
            if not topic.startswith(agent_id):
                continue
            matches.append({
                "id": (results.get("ids", [["?"]])[0] or ["?"])[i],
                "topic": topic,
                "content": doc,
                "confidence": float(meta.get("confidence", 0.0)),
                "tags": meta.get("tags", ""),
                "vault_path": meta.get("vault_path", ""),
            })
        return matches
    except Exception:
        return []


def _search_all_agents_memory(query: str, limit: int = 20) -> list:
    """
    Search across all agent memories in ChromaDB.
    Returns matches with agent attribution from topic.
    Topic format: "agent-name/fact-name" or flat topic name.
    """
    try:
        client = _get_chroma_client()
        coll = client.get_collection(name="rose_memory")
        results = coll.query(
            query_texts=[query],
            n_results=limit,
            include=["metadatas", "documents"],
        )
        matches = []
        for i, doc in enumerate(results.get("documents", [[]])[0] or []):
            meta = (results.get("metadatas", [[{}]])[0] or [{}])[i] or {}
            topic = meta.get("topic", "")
            # Extract agent from topic: "agent-name/ffact" -> agent
            parts = topic.split("/")
            agent = parts[0] if len(parts) > 1 else topic
            matches.append({
                "id": (results.get("ids", [["?"]])[0] or ["?"])[i],
                "topic": topic,
                "agent": agent,
                "content": doc,
                "confidence": float(meta.get("confidence", 0.0)),
                "tags": meta.get("tags", ""),
                "vault_path": meta.get("vault_path", ""),
            })
        return matches
    except Exception:
        return []