# RAG 服务配置文件模板
# ================================
# 请复制为 config.py 并填入你的 API 密钥
# 敏感信息通过环境变量注入，默认值为空字符串

import os

# ==============================================================================
# 一、API 密钥与模型
# ==============================================================================

# OpenAI 兼容 LLM 服务（当前默认使用小米 MiMo）
DASHSCOPE_API_KEY = os.getenv("DASHSCOPE_API_KEY", "")
DASHSCOPE_BASE_URL = os.getenv("DASHSCOPE_BASE_URL", "https://token-plan-cn.xiaomimimo.com/v1")
DASHSCOPE_MODEL = os.getenv("DASHSCOPE_MODEL", "mimo-v2.5")             # 文本生成模型
RAG_CHAT_MODEL = os.getenv("RAG_CHAT_MODEL", "mimo-v2.5")               # RAG 对话模型
INTENT_MODEL = os.getenv("INTENT_MODEL", "mimo-v2.5")                   # 意图分析模型
VLM_MODEL = os.getenv("VLM_MODEL", "mimo-v2.5")                         # 图片理解模型

# 兼容旧变量名（逐步迁移到 DASHSCOPE_* 命名）
API_KEY = DASHSCOPE_API_KEY
BASE_URL = DASHSCOPE_BASE_URL
MODEL = DASHSCOPE_MODEL

# ==============================================================================
# 二、环境与功能开关
# ==============================================================================

APP_ENV = os.getenv("APP_ENV", "dev")   # dev / prod
IS_DEV = APP_ENV == "dev"
IS_PROD = APP_ENV == "prod"
DEV_MODE = os.getenv("DEV_MODE", "true").lower() != "false"

# 开发/生产环境自动切换
ENABLE_SESSION = IS_DEV          # 会话存储（仅开发环境）
ENABLE_FEEDBACK = True           # 反馈系统

# 扩展功能（手动开启）
ENABLE_WEB_SEARCH = False        # 网络搜索（需 SERPER_API_KEY）

# ==============================================================================
# 三、路径配置
# ==============================================================================

PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))
MODELS_DIR = os.path.join(PROJECT_ROOT, "models")
EMBEDDING_MODEL_PATH = os.path.join(MODELS_DIR, "bge-base-zh-v1.5")
RERANK_MODEL_PATH = os.path.join(MODELS_DIR, "bge-reranker-base")
_vector_store_path = os.path.join(PROJECT_ROOT, "knowledge", "vector_store")
CHROMA_DB_PATH = os.path.join(_vector_store_path, "chroma")
DOCUMENTS_PATH = os.path.join(PROJECT_ROOT, "documents")
BM25_INDEXES_PATH = os.path.join(_vector_store_path, "bm25")

# 文件存储类型: local / smb / s3 / http
STORAGE_TYPE = os.getenv("STORAGE_TYPE", "local")

# SMB/CIFS 配置
STORAGE_SMB_HOST = os.getenv("STORAGE_SMB_HOST", "")
STORAGE_SMB_SHARE = os.getenv("STORAGE_SMB_SHARE", "")
STORAGE_SMB_USERNAME = os.getenv("STORAGE_SMB_USERNAME", "")
STORAGE_SMB_PASSWORD = os.getenv("STORAGE_SMB_PASSWORD", "")
STORAGE_SMB_DOMAIN = os.getenv("STORAGE_SMB_DOMAIN", "")
STORAGE_SMB_BASE_PATH = os.getenv("STORAGE_SMB_BASE_PATH", "")

# S3 配置
STORAGE_S3_ENDPOINT = os.getenv("STORAGE_S3_ENDPOINT", "")
STORAGE_S3_BUCKET = os.getenv("STORAGE_S3_BUCKET", "")
STORAGE_S3_ACCESS_KEY = os.getenv("STORAGE_S3_ACCESS_KEY", "")
STORAGE_S3_SECRET_KEY = os.getenv("STORAGE_S3_SECRET_KEY", "")
STORAGE_S3_REGION = os.getenv("STORAGE_S3_REGION", "us-east-1")

# HTTP 文件服务配置
STORAGE_HTTP_BASE_URL = os.getenv("STORAGE_HTTP_BASE_URL", "")
STORAGE_HTTP_TOKEN = os.getenv("STORAGE_HTTP_TOKEN", "")
STORAGE_HTTP_TIMEOUT = int(os.getenv("STORAGE_HTTP_TIMEOUT", "60"))

# ==============================================================================
# 四、设备配置（GPU / CPU）
# ==============================================================================

EMBEDDING_DEVICE = os.getenv("EMBEDDING_DEVICE", os.getenv("DEVICE", "auto"))
RERANK_DEVICE = os.getenv("RERANK_DEVICE", os.getenv("DEVICE", "auto"))

# ==============================================================================
# 五、LLM 参数
# ==============================================================================

# ----- 通用问答 -----
LLM_TEMPERATURE = 0.7            # 生成温度（0=确定性，1=随机性）
LLM_MAX_TOKENS = 3000            # 最大输出 token 数
LLM_DISABLE_THINKING = os.getenv("LLM_DISABLE_THINKING", "true").lower() != "false"

# ----- 意图分析（轻量、确定性高）-----
INTENT_TEMPERATURE = 0.1
INTENT_MAX_TOKENS = 2048
INTENT_HISTORY_WINDOW = 6        # 分析时取最近几条历史消息

# ==============================================================================
# 六、检索参数
# ==============================================================================

# ----- 混合检索 -----
USE_MULTI_KB = True              # 多向量库模式
USE_HYBRID_SEARCH = True         # 向量 + BM25 混合检索
VECTOR_WEIGHT = 0.5              # 向量检索权重
BM25_WEIGHT = 0.5                # BM25 检索权重
RAG_SEARCH_TOP_K = 30            # 最终返回结果数（不小于 MMR_TOP_K，避免截断 MMR 输出）
RAG_SEARCH_CANDIDATES = 100      # 候选池大小（越大召回越全，越慢）
RECALL_MULTIPLIER = 3            # 候选池最小倍数 = top_k * 此值

# ----- 重排序 -----
USE_RERANK = True
RERANK_BACKEND = os.getenv("RERANK_BACKEND", "cloud")   # cloud / local / fallback
RERANK_CLOUD_MODEL = os.getenv("RERANK_CLOUD_MODEL", "xop3qwen8breranker")
RERANK_CLOUD_API_KEY = os.getenv("RERANK_CLOUD_API_KEY", "")
RERANK_CLOUD_BASE_URL = os.getenv("RERANK_CLOUD_BASE_URL", "https://maas-api.cn-huabei-1.xf-yun.com/v2/rerank")
RERANK_CLOUD_TIMEOUT = int(os.getenv("RERANK_CLOUD_TIMEOUT", "15"))
RERANK_CANDIDATES = 20           # 送入重排序的候选数
RERANK_TOP_K = 15                # 重排序后保留数
RERANK_USE_ONNX = os.getenv("RERANK_USE_ONNX", "true").lower() == "true"
RERANK_CONTEXT_MIN_SCORE = 0.05  # Rerank 分数低于此值的切片不送入 LLM

# ----- RRF 融合 -----
RRF_K = 60                       # RRF 常数（越大越平滑）
DYNAMIC_RRF_ENABLED = True       # 根据查询类型动态调整向量/BM25 权重

# ----- MMR 多样性去重 -----
MMR_ENABLED = True
MMR_USE_EMBEDDING = True         # True=语义向量（慢），False=文本相似度（快）
MMR_TOP_K = 30                   # MMR 处理后保留数
MMR_LAMBDA = 0.5                 # 相关性 vs 多样性权衡（0=纯多样，1=纯相关）

# ----- 查询扩展 -----
QUERY_EXPANSION_ENABLED = True
QUERY_EXPANSION_THRESHOLD = 0.8  # 扩展词相似度阈值

# ----- 章节过滤 -----
SECTION_FILTER_ENABLED = True    # 查询提到章节时优先匹配对应切片

# ==============================================================================
# 七、上下文构建
# ==============================================================================

MAX_CONTEXT_CHUNKS = 20          # 送给 LLM 的最大文本切片数
CONTEXT_MAX_CHARS = 8000         # 上下文最大字符数（约 4000 token）
CONTEXT_SOFT_LIMIT = 6000        # 软限制，超过后只接受高分切片组
MAX_SOURCES_RETURNED = 10        # 返回给前端的最大来源数
MAX_HISTORY_ROUNDS = 10          # 对话历史最大轮数
IMAGE_CONTEXT_HISTORY = 4        # 图片上下文取最近几轮历史
DIRECT_CONTEXT_MAX_CHARS = 2000  # 直接回答模式上下文截断字符数

# ==============================================================================
# 八、FAQ 与黑名单
# ==============================================================================

# FAQ 召回与权重
FAQ_RECALL_TOP_K = 3             # FAQ 集合单独召回数
FAQ_BOOST_AMOUNT = 0.1           # FAQ 命中时距离减少量（提升排名）

# FAQ 时间衰减（防止过期 FAQ 长期霸榜）
FAQ_DECAY_MONTHS = 6             # 超过此月数开始衰减
FAQ_DECAY_RATE = 0.01            # 每超一个月的距离惩罚
FAQ_DECAY_MAX = 0.1              # 最大衰减惩罚

# 黑名单（负反馈过滤）
BLACKLIST_MIN_DISLIKES = 3       # 差评达到此数量进入黑名单
BLACKLIST_CACHE_TTL = 300        # 黑名单缓存刷新间隔（秒）

# ==============================================================================
# 九、缓存配置
# ==============================================================================

# 查询结果缓存
QUERY_CACHE_ENABLED = True
QUERY_CACHE_SIZE = 500
QUERY_CACHE_TTL = 3600           # 秒

# Embedding 缓存
EMBEDDING_CACHE_ENABLED = True
EMBEDDING_CACHE_SIZE = 2000
EMBEDDING_CACHE_TTL = 86400

# Rerank 缓存
RERANK_CACHE_ENABLED = True
RERANK_CACHE_SIZE = 1000
RERANK_CACHE_TTL = 3600

# 语义缓存（相似查询复用结果）
SEMANTIC_CACHE_ENABLED = True
SEMANTIC_CACHE_THRESHOLD = 0.92  # 相似度阈值

# 缓存写入最低置信度
# 注意：ChromaDB cosine distance 范围 [0,2]，score = 1 - dist
# 当前 embedding 模型的 cosine similarity 普遍在 0.03-0.06 之间
# 搜索管线已通过 rerank 过滤低质量结果，此处不再额外限制
CACHE_MIN_SCORE = 0.0

# LLM 调用预算
MAX_LLM_CALLS_PER_QUERY = 2
MAX_QUERY_REWRITES = 1

# ==============================================================================
# 十、文档解析
# ==============================================================================

# MinerU 解析器
MINERU_DEVICE_MODE = os.getenv("MINERU_DEVICE_MODE", "cpu")  # cpu / cuda
MINERU_MODEL_VERSION = os.getenv("MINERU_MODEL_VERSION", "vlm")
MINERU_LOCAL_BACKEND = os.getenv("MINERU_LOCAL_BACKEND", "pipeline")
MINERU_ONLINE_TIMEOUT = int(os.getenv("MINERU_ONLINE_TIMEOUT", "300"))

# MinerU 在线 API（作为本地解析失败时的备选）
MINERU_API_TOKEN = os.getenv("MINERU_API_TOKEN", "")  # 在 https://mineru.net/apiManage/token 申请
MINERU_API_URL = os.getenv("MINERU_API_URL", "https://mineru.net/api/v4/extract/task")
MINERU_PREFER_ONLINE = os.getenv("MINERU_PREFER_ONLINE", "true").lower() == "true"
MINERU_PREFER_V2 = os.getenv("MINERU_PREFER_V2", "true").lower() == "true"  # 优先使用 v2 格式（含 style 信息）

# 标题识别规则引擎
# 规则定义见 parsers/heading_rules.py，支持通过 config.py 覆盖
HEADING_RULES_CONFIG = None       # None=使用内置默认规则; dict 列表=自定义规则覆盖
HEADING_SHORT_TEXT_ENABLED = True  # 是否启用短中文文本标题识别（最易误判的规则，可单独关闭）
HEADING_SHORT_TEXT_MAX_LENGTH = 20 # 短文本最大字符数阈值

# 表单类型二次校正
# MinerU 解析 Word 文档时，某些表单被标记为 text，根据内容特征修正为 table
FORM_RECLASSIFY_ENABLED = True    # 是否启用 text->table 表单检测
FORM_RECLASSIFY_MIN_INDICATORS = 2 # 最少命中几个表单特征指标才校正

# 分块参数
CHUNK_SIZE = 1000
CHUNK_OVERLAP = 100
MIN_CHUNK_SIZE = 200
MAX_CHUNK_SIZE = 1200

# 自适应 TopK（根据置信度动态调整返回数）
ADAPTIVE_TOPK_ENABLED = True
ADAPTIVE_LOW_CONFIDENCE = 0.5
ADAPTIVE_HIGH_CONFIDENCE = 0.8
ADAPTIVE_EXPAND_RATIO = 2.0
ADAPTIVE_SHRINK_RATIO = 0.5
ADAPTIVE_MIN_TOPK = 15
ADAPTIVE_MAX_TOPK = 20

# 连续切片完整性保护（枚举/条款/清单类问题）
CONTEXT_EXPANSION_ENABLED = True
CONTEXT_EXPANSION_BEFORE = 1
CONTEXT_EXPANSION_AFTER = 5
CONTEXT_EXPANSION_MAX_CHUNKS = 50
EXPANSION_SCORE_THRESHOLD = 0.3
MAX_EXPANDED_NEIGHBORS = 4
CONFIDENCE_WARN_THRESHOLD = 0.15   # top-3 均分低于此值时，提示 LLM 谨慎回答
CONFIDENCE_CAUTION_THRESHOLD = 0.30 # top-3 均分低于此值时，提示 LLM 优先引用原文
ENUM_QUERY_DISABLE_TOPK_SHRINK = True
ENUM_QUERY_MMR_LAMBDA = 0.85

# 章节聚类提升与 BM25/CrossEncoder 分歧救援
SECTION_CLUSTER_BOOST_ENABLED = True
SECTION_CLUSTER_RESCUE_ENABLED = True
BM25_DIVERGENCE_RESCUE_ENABLED = True
BM25_DIVERGENCE_MAX_RANK = 3
CLUSTER_MIN_MEMBERS = 3
CLUSTER_MIN_TYPES = 2
CLUSTER_SEED_FLOOR = 0.35
CLUSTER_RESCUE_FLOOR = 0.06
CLUSTER_MAX_BOOST_PER_SECTION = 8
CLUSTER_MAX_SECTIONS = 3
CLUSTER_MAX_RESCUE_PER_SECTION = 6
CLUSTER_SECTION_PREFIX_LEVELS = 2

# ==============================================================================
# 十一、可选功能配置
# ==============================================================================

# 网络搜索（需 Serper API）
SERPER_API_KEY = os.getenv("SERPER_API_KEY", "")

# ==============================================================================
# 工具函数
# ==============================================================================

def get_llm_client():
    """获取 LLM 客户端实例"""
    from openai import OpenAI
    return OpenAI(api_key=DASHSCOPE_API_KEY, base_url=DASHSCOPE_BASE_URL)


_intent_client = None


def get_intent_client():
    """获取意图分析专用 LLM 客户端（进程内复用连接池）。"""
    global _intent_client
    if _intent_client is None:
        if not DASHSCOPE_API_KEY:
            raise ValueError("DASHSCOPE_API_KEY 未配置，请在环境变量中设置")
        from openai import OpenAI
        _intent_client = OpenAI(
            api_key=DASHSCOPE_API_KEY,
            base_url=DASHSCOPE_BASE_URL,
        )
    return _intent_client