# database/setup.py

import os
import sqlite3
import json
from pymilvus import MilvusClient, FieldSchema, CollectionSchema, DataType
from llama_index.embeddings.google import GooglePairedEmbeddings

# 导入你的工具注册表
from tools.tool_registry import get_all_tools

# --- 配置持久化路径 ---
DATA_DIR = 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_data.db"
)  # Milvus Lite 将数据存在一个文件中

# --- Milvus Lite 配置 ---
MILVUS_COLLECTION_NAME = "tool_embeddings"
EMBEDDING_DIM = 768  # Google's text-embedding-004 model dimension

# --- 全局变量，避免重复初始化 ---
_db_initialized = False
_milvus_initialized = False


def initialize_system():
    """
    系统的主初始化函数。
    它会创建目录、设置数据库和向量库，并加载工具。
    这个函数是幂等的，即多次调用不会产生副作用。
    """
    global _db_initialized, _milvus_initialized

    print("--- 开始系统初始化 ---")

    # 1. 创建数据目录
    os.makedirs(DATA_DIR, exist_ok=True)

    # 2. 初始化SQLite数据库
    if not _db_initialized:
        _init_sqlite_db()
        _db_initialized = True

    # 3. 初始化Milvus Lite向量数据库
    if not _milvus_initialized:
        milvus_client = _init_milvus_lite()
        _milvus_initialized = True
    else:
        milvus_client = MilvusClient(uri=MILVUS_DATA_PATH)

    # 4. 获取所有工具定义
    all_tools_definitions = get_all_tools()

    # 5. 将工具元数据同步到SQLite
    _sync_tools_to_sqlite(all_tools_definitions)

    # 6. 将工具描述的嵌入同步到Milvus Lite
    _sync_tool_embeddings_to_milvus(milvus_client)

    # 7. 从LlamaIndex创建工具推荐器 (在这里创建并返回)
    from core.tool_recommender import LlamaIndexToolRecommender

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

    print("--- 系统初始化完成 ---")
    return all_tools_definitions, tool_recommender


def _init_sqlite_db():
    """初始化SQLite数据库并创建表。"""
    print(f"SQLite DB 路径: {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 -- 存储JSON字符串
            )
        """
        )
        conn.commit()
    print("SQLite DB 表已确认存在。")


def _init_milvus_lite():
    """初始化Milvus Lite并创建集合和索引。"""
    print(f"Milvus Lite 数据路径: {MILVUS_DATA_PATH}")
    client = MilvusClient(uri=MILVUS_DATA_PATH)

    if not client.has_collection(collection_name=MILVUS_COLLECTION_NAME):
        print(f"Milvus集合 '{MILVUS_COLLECTION_NAME}' 不存在，正在创建...")
        fields = [
            FieldSchema(name="id", dtype=DataType.INT64, is_primary=True),
            FieldSchema(
                name="embedding", dtype=DataType.FLOAT_VECTOR, dim=EMBEDDING_DIM
            ),
        ]
        schema = CollectionSchema(fields, description="Tool embedding collection")
        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",  # 让Milvus自动选择最佳索引
            metric_type="L2",
        )
        client.create_index(
            collection_name=MILVUS_COLLECTION_NAME, index_params=index_params
        )
        print("Milvus集合和索引创建完成。")
    else:
        print(f"Milvus集合 '{MILVUS_COLLECTION_NAME}' 已存在。")
        # 确保集合已加载到内存中以供搜索
        client.load_collection(collection_name=MILVUS_COLLECTION_NAME)

    return client


def _sync_tools_to_sqlite(tools_definitions):
    """将工具定义同步到SQLite数据库。"""
    print("正在同步工具元数据到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:
                # 工具不存在，插入新工具
                cursor.execute(
                    "INSERT INTO tools (name, description, parameters) VALUES (?, ?, ?)",
                    (tool.name, tool.description, json.dumps(tool.args)),
                )
                print(f"  - 新增工具到SQLite: {tool.name}")
        conn.commit()
    print("SQLite同步完成。")


def _sync_tool_embeddings_to_milvus(milvus_client):
    """计算并同步工具描述的嵌入到Milvus Lite。"""
    print("正在同步工具嵌入到Milvus...")

    # 1. 从SQLite获取所有工具
    with sqlite3.connect(SQLITE_DB_PATH) as conn:
        cursor = conn.cursor()
        cursor.execute("SELECT id, description FROM tools")
        all_tools_in_db = cursor.fetchall()

    # 2. 获取Milvus中已存在的工具ID
    try:
        existing_milvus_ids_raw = milvus_client.query(
            collection_name=MILVUS_COLLECTION_NAME,
            filter="id > 0",
            output_fields=["id"],
        )
        existing_milvus_ids = {item["id"] for item in existing_milvus_ids_raw}
    except Exception:
        existing_milvus_ids = set()

    # 3. 找出需要计算嵌入的新工具
    new_tools_to_embed = [
        (tool_id, description)
        for tool_id, description in all_tools_in_db
        if tool_id not in existing_milvus_ids
    ]

    if not new_tools_to_embed:
        print("所有工具嵌入已是最新，无需同步。")
        return

    print(f"发现 {len(new_tools_to_embed)} 个新工具需要生成嵌入...")

    # 4. 初始化嵌入模型
    try:
        # 确保你的API Key已在环境中设置
        embed_model = GooglePairedEmbeddings(
            model_name="models/text-embedding-004",
            task_type="retrieval_document",  # 用于存储的文档嵌入
        )
    except Exception as e:
        print(f"错误：无法初始化Google嵌入模型。请检查API Key。 - {e}")
        return

    # 5. 生成嵌入并准备插入
    tool_ids_to_insert = [tool[0] for tool in new_tools_to_embed]
    descriptions_to_embed = [tool[1] for tool in new_tools_to_embed]

    embeddings = embed_model.get_text_embedding_batch(
        descriptions_to_embed, show_progress=True
    )

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

    # 6. 插入到Milvus
    milvus_client.insert(collection_name=MILVUS_COLLECTION_NAME, data=data_to_insert)
    milvus_client.flush([MILVUS_COLLECTION_NAME])  # 确保数据写入
    print(f"成功将 {len(data_to_insert)} 个新嵌入插入到Milvus。")