import os
import sqlite3
import json
from pymilvus import MilvusClient, FieldSchema, CollectionSchema, DataType
import google.generativeai as genai

from tools.tool_registry import get_all_tools

# --- Configuration for persistence paths ---
DATA_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "data"))
SQLITE_DB_PATH = os.path.join(DATA_DIR, "tools.metadata.db")
MILVUS_DATA_PATH = os.path.join(DATA_DIR, "milvus_lite.db")

# --- Model and DB Configuration ---
EMBEDDING_DIM = 3072
EMBEDDING_MODEL_NAME = "gemini-embedding-exp-03-07"
MILVUS_COLLECTION_NAME = "tool_embeddings"


def initialize_system():
    """
    The main system initialization function.
    It creates directories, sets up the database and vector store, and loads tools.
    This function is designed to be idempotent.
    """
    print("--- Starting System Initialization (Final Version) ---")
    os.makedirs(DATA_DIR, exist_ok=True)

    # --- Correct Initialization Order ---

    # 1. Initialize SQLite and sync tool metadata
    # Ensures SQLite always has the latest tool information
    _init_sqlite_db()
    all_tools_definitions = get_all_tools()
    _sync_tools_to_sqlite(all_tools_definitions)

    # 2. Initialize Milvus and sync vector embeddings
    # It reads data from the already populated SQLite DB
    milvus_client = _init_milvus_and_sync_embeddings()

    # 3. Create the tool recommender instance
    from core.tool_recommender import DirectToolRecommender

    tool_recommender = DirectToolRecommender(
        milvus_client=milvus_client, sqlite_db_path=SQLITE_DB_PATH
    )

    print("--- System Initialization Complete ---")
    return all_tools_definitions, tool_recommender


def _init_sqlite_db():
    """Initializes the SQLite database and creates the tools table if it doesn't exist."""
    print(f"SQLite DB Path: {SQLITE_DB_PATH}")
    with sqlite3.connect(SQLITE_DB_PATH) as conn:
        cursor = conn.cursor()
        cursor.execute(
            """
            CREATE TABLE IF NOT EXISTS tools (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                name TEXT UNIQUE NOT NULL,
                description TEXT NOT NULL,
                parameters TEXT NOT NULL
            )
        """
        )
        conn.commit()
    print("SQLite DB table verified.")


def _sync_tools_to_sqlite(tools_definitions):
    """Syncs tool definitions into the SQLite database."""
    print("Syncing tool metadata to SQLite...")
    with sqlite3.connect(SQLITE_DB_PATH) as conn:
        cursor = conn.cursor()
        for tool in tools_definitions:
            cursor.execute("SELECT id FROM tools WHERE name = ?", (tool.name,))
            if cursor.fetchone() is None:
                cursor.execute(
                    "INSERT INTO tools (name, description, parameters) VALUES (?, ?, ?)",
                    (tool.name, tool.description, json.dumps(tool.args)),
                )
                print(f"  - Added new tool to SQLite: {tool.name}")
        conn.commit()
    print("SQLite sync complete.")


def _init_milvus_and_sync_embeddings():
    """Initializes Milvus Lite, rebuilds the collection, and syncs embeddings."""
    print(f"Milvus Lite Data Path: {MILVUS_DATA_PATH}")
    client = MilvusClient(uri=MILVUS_DATA_PATH)

    # Recreate the collection on every startup to ensure correct dimensionality and fresh data for the demo.
    if client.has_collection(collection_name=MILVUS_COLLECTION_NAME):
        client.drop_collection(collection_name=MILVUS_COLLECTION_NAME)
        print("Found old Milvus collection. Dropped it to rebuild.")

    print(
        f"Creating Milvus collection '{MILVUS_COLLECTION_NAME}' with dimension {EMBEDDING_DIM}..."
    )
    fields = [
        FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
        FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=EMBEDDING_DIM),
    ]
    schema = CollectionSchema(fields)
    client.create_collection(collection_name=MILVUS_COLLECTION_NAME, schema=schema)

    index_params = client.prepare_index_params()
    index_params.add_index(
        field_name="embedding", index_type="AUTOINDEX", metric_type="L2"
    )
    client.create_index(
        collection_name=MILVUS_COLLECTION_NAME, index_params=index_params
    )
    print("Milvus collection and index created successfully.")

    # Critical Step: Now we sync the embeddings to the newly created collection
    _sync_tool_embeddings_to_milvus(client)

    client.load_collection(collection_name=MILVUS_COLLECTION_NAME)
    return client


def _sync_tool_embeddings_to_milvus(milvus_client):
    """Generates and syncs tool description embeddings to Milvus Lite."""
    print("Syncing tool embeddings to Milvus...")
    api_key = os.environ.get("GEMINI_API_KEY")
    if not api_key:
        print("Error: GEMINI_API_KEY not found.")
        return
    genai.configure(api_key=api_key)

    with sqlite3.connect(SQLITE_DB_PATH) as conn:
        cursor = conn.cursor()
        cursor.execute("SELECT id, description FROM tools")
        all_tools_in_db = cursor.fetchall()

    if not all_tools_in_db:
        print("Error: No tools found in SQLite to sync.")
        return

    print(f"Found {len(all_tools_in_db)} tools from SQLite, generating embeddings...")
    docs_to_embed = [tool[1] for tool in all_tools_in_db]

    print(f"Using embedding model: {EMBEDDING_MODEL_NAME}")
    result = genai.embed_content(
        model=EMBEDDING_MODEL_NAME,
        content=docs_to_embed,
        task_type="retrieval_document",
    )

    embeddings = result["embedding"]
    tool_ids_to_insert = [tool[0] for tool in all_tools_in_db]

    data_to_insert = [
        {"id": tool_id, "embedding": embedding}
        for tool_id, embedding in zip(tool_ids_to_insert, embeddings)
    ]

    milvus_client.insert(collection_name=MILVUS_COLLECTION_NAME, data=data_to_insert)
    print(f"Successfully inserted {len(data_to_insert)} new embeddings into Milvus.")