import os

# LLM Token Settings
MAX_CONTEXT_TOKENS = int(os.getenv("MAX_CONTEXT_TOKENS", 262144))
MAX_OUTPUT_TOKENS = int(os.getenv("MAX_OUTPUT_TOKENS", 8000))
SUMMARIZATION_MAX_TOKENS = MAX_CONTEXT_TOKENS - MAX_OUTPUT_TOKENS - 1000

# Agent Cache Settings
AGENT_CACHE_MAX_SIZE = int(os.getenv("AGENT_CACHE_MAX_SIZE", 20))
AGENT_CACHE_TTL = int(os.getenv("AGENT_CACHE_TTL", 180))
AGENT_CACHE_AUTO_RENEW = os.getenv("AGENT_CACHE_AUTO_RENEW", "true") == "true"



# API Settings
BACKEND_HOST = os.getenv("BACKEND_HOST", "https://api-dev.gptbase.ai")
MASTERKEY = os.getenv("MASTERKEY", "master")
FASTAPI_URL = os.getenv('FASTAPI_URL', 'http://127.0.0.1:8001')

# Project Settings
PROJECT_DATA_DIR = os.getenv("PROJECT_DATA_DIR", "./projects/data")

# Tokenizer Settings
TOKENIZERS_PARALLELISM = os.getenv("TOKENIZERS_PARALLELISM", "true")

# Embedding Model Settings
SENTENCE_TRANSFORMER_MODEL = os.getenv("SENTENCE_TRANSFORMER_MODEL", "TaylorAI/gte-tiny")

# Tool Output Length Control Settings
TOOL_OUTPUT_MAX_LENGTH = int(SUMMARIZATION_MAX_TOKENS/4)
TOOL_OUTPUT_TRUNCATION_STRATEGY = os.getenv("TOOL_OUTPUT_TRUNCATION_STRATEGY", "smart")

# THINKING ENABLE
DEFAULT_THINKING_ENABLE = os.getenv("DEFAULT_THINKING_ENABLE", "true") == "true"

# MCP Tool Timeout Settings
MCP_HTTP_TIMEOUT = int(os.getenv("MCP_HTTP_TIMEOUT", 60))  # HTTP 请求超时（秒）
MCP_SSE_READ_TIMEOUT = int(os.getenv("MCP_SSE_READ_TIMEOUT", 300))  # SSE 读取超时（秒）

# ============================================================
# SQLite Checkpoint Configuration
# ============================================================

# Checkpoint 数据库路径
CHECKPOINT_DB_PATH = os.getenv("CHECKPOINT_DB_PATH", "./projects/memory/checkpoints.db")

# 启用 WAL 模式 (Write-Ahead Logging)
# WAL 模式允许读写并发，大幅提升并发性能
CHECKPOINT_WAL_MODE = os.getenv("CHECKPOINT_WAL_MODE", "true") == "true"

# Busy Timeout (毫秒)
# 当数据库被锁定时，等待的最长时间（毫秒）
CHECKPOINT_BUSY_TIMEOUT = int(os.getenv("CHECKPOINT_BUSY_TIMEOUT", "10000"))


# 连接池大小
# 同时可以持有的最大连接数
CHECKPOINT_POOL_SIZE = int(os.getenv("CHECKPOINT_POOL_SIZE", "30"))