"""Echo Core memory search — semantic search over memory/*.md files.

Uses Ollama all-minilm embeddings stored in SQLite for cosine similarity search.
"""

import logging
import math
import re
import sqlite3
import struct
from datetime import datetime, timezone
from pathlib import Path

import httpx

log = logging.getLogger(__name__)

PROJECT_ROOT = Path(__file__).resolve().parent.parent
DB_PATH = PROJECT_ROOT / "memory" / "echo.sqlite"
MEMORY_DIR = PROJECT_ROOT / "memory"

# Defaults — overridable via config.json ollama/memory sections
_OLLAMA_BASE_URL = "http://localhost:11434"
_OLLAMA_MODEL = "all-minilm"
_EMBEDDING_DIM = 384
_CHUNK_TARGET = 500
_CHUNK_MAX = 1000
_CHUNK_MIN = 100

# Runtime config (populated by init_config)
OLLAMA_URL = f"{_OLLAMA_BASE_URL}/api/embeddings"
OLLAMA_MODEL = _OLLAMA_MODEL
EMBEDDING_DIM = _EMBEDDING_DIM


def init_config(config=None) -> None:
    """Load settings from config object. Call once at startup."""
    global OLLAMA_URL, OLLAMA_MODEL, EMBEDDING_DIM
    if config is None:
        # Try loading from config.json directly
        config_file = PROJECT_ROOT / "config.json"
        if config_file.exists():
            import json
            try:
                with open(config_file, encoding="utf-8") as f:
                    data = json.load(f)
                base_url = data.get("ollama", {}).get("url", _OLLAMA_BASE_URL)
                OLLAMA_URL = f"{base_url.rstrip('/')}/api/embeddings"
                OLLAMA_MODEL = data.get("ollama", {}).get("model", _OLLAMA_MODEL)
                EMBEDDING_DIM = data.get("ollama", {}).get("embedding_dim", _EMBEDDING_DIM)
            except (json.JSONDecodeError, OSError):
                pass
        return

    # Config object with .get() method
    base_url = config.get("ollama.url", _OLLAMA_BASE_URL)
    OLLAMA_URL = f"{base_url.rstrip('/')}/api/embeddings"
    OLLAMA_MODEL = config.get("ollama.model", _OLLAMA_MODEL)
    EMBEDDING_DIM = config.get("ollama.embedding_dim", _EMBEDDING_DIM)


# Auto-init from config.json on import
init_config()


def _is_indexable(md_file: Path) -> bool:
    """Skip generated navigation files so they aren't embedded as if they were notes."""
    return md_file.name != "index.md"


def get_db() -> sqlite3.Connection:
    """Get SQLite connection, create table if needed."""
    DB_PATH.parent.mkdir(parents=True, exist_ok=True)
    conn = sqlite3.connect(str(DB_PATH))
    conn.execute(
        """CREATE TABLE IF NOT EXISTS chunks (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            file_path TEXT NOT NULL,
            chunk_index INTEGER NOT NULL,
            chunk_text TEXT NOT NULL,
            embedding BLOB NOT NULL,
            updated_at TEXT NOT NULL,
            UNIQUE(file_path, chunk_index)
        )"""
    )
    conn.execute(
        "CREATE INDEX IF NOT EXISTS idx_file_path ON chunks(file_path)"
    )
    conn.commit()
    return conn


def get_embedding(text: str) -> list[float]:
    """Get embedding vector from Ollama. Returns list of 384 floats."""
    try:
        resp = httpx.post(
            OLLAMA_URL,
            json={"model": OLLAMA_MODEL, "prompt": text},
            timeout=30.0,
        )
        resp.raise_for_status()
        embedding = resp.json()["embedding"]
        if len(embedding) != EMBEDDING_DIM:
            raise ValueError(
                f"Expected {EMBEDDING_DIM} dimensions, got {len(embedding)}"
            )
        return embedding
    except httpx.ConnectError:
        raise ConnectionError(
            f"Cannot connect to Ollama at {OLLAMA_URL}. Is Ollama running?"
        )
    except httpx.HTTPStatusError as e:
        raise ConnectionError(f"Ollama API error: {e.response.status_code}")


def serialize_embedding(embedding: list[float]) -> bytes:
    """Pack floats to bytes for SQLite storage."""
    return struct.pack(f"{len(embedding)}f", *embedding)


def deserialize_embedding(data: bytes) -> list[float]:
    """Unpack bytes to floats."""
    n = len(data) // 4
    return list(struct.unpack(f"{n}f", data))


def cosine_similarity(a: list[float], b: list[float]) -> float:
    """Compute cosine similarity between two vectors."""
    dot = sum(x * y for x, y in zip(a, b))
    norm_a = math.sqrt(sum(x * x for x in a))
    norm_b = math.sqrt(sum(x * x for x in b))
    if norm_a == 0 or norm_b == 0:
        return 0.0
    return dot / (norm_a * norm_b)


def chunk_file(file_path: Path) -> list[str]:
    """Split .md file into chunks of ~500 chars."""
    text = file_path.read_text(encoding="utf-8")
    if not text.strip():
        return []

    # Split by double newlines or headers
    raw_parts: list[str] = []
    current = ""
    for line in text.split("\n"):
        # Split on headers or empty lines (paragraph boundaries)
        if line.startswith("#") and current.strip():
            raw_parts.append(current.strip())
            current = line + "\n"
        elif line.strip() == "" and current.strip():
            raw_parts.append(current.strip())
            current = ""
        else:
            current += line + "\n"
    if current.strip():
        raw_parts.append(current.strip())

    # Merge small chunks with next, split large ones
    chunks: list[str] = []
    buffer = ""
    for part in raw_parts:
        if buffer and len(buffer) + len(part) + 1 > _CHUNK_MAX:
            chunks.append(buffer)
            buffer = part
        elif buffer:
            buffer = buffer + "\n\n" + part
        else:
            buffer = part

        # If buffer exceeds max, flush
        if len(buffer) > _CHUNK_MAX:
            chunks.append(buffer)
            buffer = ""

    if buffer:
        # Merge tiny trailing chunk with previous
        if len(buffer) < _CHUNK_MIN and chunks:
            chunks[-1] = chunks[-1] + "\n\n" + buffer
        else:
            chunks.append(buffer)

    return chunks


def index_file(file_path: Path) -> int:
    """Index a single file. Returns number of chunks created."""
    rel_path = str(file_path.relative_to(MEMORY_DIR))
    chunks = chunk_file(file_path)
    if not chunks:
        return 0

    now = datetime.now(timezone.utc).isoformat()
    conn = get_db()
    try:
        conn.execute("DELETE FROM chunks WHERE file_path = ?", (rel_path,))
        for i, chunk_text in enumerate(chunks):
            embedding = get_embedding(chunk_text)
            conn.execute(
                """INSERT INTO chunks (file_path, chunk_index, chunk_text, embedding, updated_at)
                   VALUES (?, ?, ?, ?, ?)""",
                (rel_path, i, chunk_text, serialize_embedding(embedding), now),
            )
        conn.commit()
        return len(chunks)
    finally:
        conn.close()


def reindex() -> dict:
    """Rebuild entire index. Returns {"files": N, "chunks": M}."""
    conn = get_db()
    conn.execute("DELETE FROM chunks")
    conn.commit()
    conn.close()

    files_count = 0
    chunks_count = 0
    for md_file in sorted(MEMORY_DIR.rglob("*.md")):
        if not _is_indexable(md_file):
            continue
        try:
            n = index_file(md_file)
            files_count += 1
            chunks_count += n
            log.info("Indexed %s (%d chunks)", md_file.name, n)
        except Exception as e:
            log.warning("Failed to index %s: %s", md_file, e)

    return {"files": files_count, "chunks": chunks_count}


def incremental_index() -> dict:
    """Index only new or modified .md files. Returns {"indexed": N, "chunks": M}."""
    conn = get_db()
    try:
        # Get latest updated_at per file from DB
        rows = conn.execute(
            "SELECT file_path, MAX(updated_at) FROM chunks GROUP BY file_path"
        ).fetchall()
        db_times = {}
        for rel_path, updated_at in rows:
            try:
                db_times[rel_path] = datetime.fromisoformat(updated_at)
            except (ValueError, TypeError):
                pass
    finally:
        conn.close()

    files_indexed = 0
    chunks_total = 0
    for md_file in sorted(MEMORY_DIR.rglob("*.md")):
        if not _is_indexable(md_file):
            continue
        rel_path = str(md_file.relative_to(MEMORY_DIR))
        file_mtime = datetime.fromtimestamp(
            md_file.stat().st_mtime, tz=timezone.utc
        )
        db_time = db_times.get(rel_path)
        if db_time is not None:
            # Ensure both are offset-aware for comparison
            if db_time.tzinfo is None:
                db_time = db_time.replace(tzinfo=timezone.utc)
            if file_mtime <= db_time:
                continue
        try:
            n = index_file(md_file)
            files_indexed += 1
            chunks_total += n
            log.info("Incremental indexed %s (%d chunks)", md_file.name, n)
        except Exception as e:
            log.warning("Failed to index %s: %s", md_file, e)

    return {"indexed": files_indexed, "chunks": chunks_total}


def _keyword_fallback(query: str, top_k: int = 5) -> list[dict]:
    """Keyword search over indexed chunks. Used when the embedding backend is down.

    Returns the same shape as search() plus "degraded": True so callers can
    tell the user that semantic recall was unavailable. Ranks best-chunk-per-file
    by raw term-occurrence count.
    """
    terms = [t for t in re.findall(r"\w+", query.lower()) if len(t) > 2]

    conn = get_db()
    try:
        rows = conn.execute("SELECT file_path, chunk_text FROM chunks").fetchall()
    finally:
        conn.close()

    best: dict[str, dict] = {}
    for file_path, chunk_text in rows:
        low = chunk_text.lower()
        hits = sum(low.count(t) for t in terms) if terms else 0
        if hits == 0:
            continue
        cur = best.get(file_path)
        if cur is None or hits > cur["score"]:
            best[file_path] = {
                "file": file_path,
                "chunk": chunk_text,
                "score": float(hits),
                "degraded": True,
            }

    scored = sorted(best.values(), key=lambda x: x["score"], reverse=True)
    return scored[:top_k]


def search(query: str, top_k: int = 5) -> list[dict]:
    """Search for query. Returns list of {"file": str, "chunk": str, "score": float}.

    Results are deduped to the best-scoring chunk per file, so a relevant note
    can't be buried by another file contributing several chunks. If the embedding
    backend (Ollama) is unreachable, falls back to keyword search and tags each
    result with "degraded": True instead of raising.
    """
    try:
        query_embedding = get_embedding(query)
    except ConnectionError as e:
        log.warning(
            "Embedding backend unavailable (%s); falling back to keyword search", e
        )
        return _keyword_fallback(query, top_k)

    conn = get_db()
    try:
        rows = conn.execute(
            "SELECT file_path, chunk_text, embedding FROM chunks"
        ).fetchall()
    finally:
        conn.close()

    if not rows:
        return []

    best: dict[str, dict] = {}
    for file_path, chunk_text, emb_blob in rows:
        emb = deserialize_embedding(emb_blob)
        score = cosine_similarity(query_embedding, emb)
        cur = best.get(file_path)
        if cur is None or score > cur["score"]:
            best[file_path] = {"file": file_path, "chunk": chunk_text, "score": score}

    scored = sorted(best.values(), key=lambda x: x["score"], reverse=True)
    return scored[:top_k]