100 lines
4.0 KiB
Python
100 lines
4.0 KiB
Python
import os
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# 必填参数
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# API Settings
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BACKEND_HOST = os.getenv("BACKEND_HOST", "https://api-dev.gptbase.ai")
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MASTERKEY = os.getenv("MASTERKEY", "master")
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FASTAPI_URL = os.getenv('FASTAPI_URL', 'http://127.0.0.1:8001')
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# LLM Token Settings
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MAX_CONTEXT_TOKENS = int(os.getenv("MAX_CONTEXT_TOKENS", 200000))
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MAX_OUTPUT_TOKENS = int(os.getenv("MAX_OUTPUT_TOKENS", 8000))
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# 可选参数
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# Summarization Settings
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SUMMARIZATION_MAX_TOKENS = int(MAX_CONTEXT_TOKENS/3)
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SUMMARIZATION_TOKENS_TO_KEEP = int(SUMMARIZATION_MAX_TOKENS/3)
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DEFAULT_TRIM_TOKEN_LIMIT = SUMMARIZATION_MAX_TOKENS - SUMMARIZATION_TOKENS_TO_KEEP + 5000
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# Agent Cache Settings
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TOOL_CACHE_MAX_SIZE = int(os.getenv("TOOL_CACHE_MAX_SIZE", 20))
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TOOL_CACHE_TTL = int(os.getenv("TOOL_CACHE_TTL", 180))
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TOOL_CACHE_AUTO_RENEW = os.getenv("TOOL_CACHE_AUTO_RENEW", "true") == "true"
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# Project Settings
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PROJECT_DATA_DIR = os.getenv("PROJECT_DATA_DIR", "./projects/data")
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SKILLS_DIR = os.getenv("SKILLS_DIR", "./skills")
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# Tokenizer Settings
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TOKENIZERS_PARALLELISM = os.getenv("TOKENIZERS_PARALLELISM", "true")
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# Embedding Model Settings
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SENTENCE_TRANSFORMER_MODEL = os.getenv("SENTENCE_TRANSFORMER_MODEL", "TaylorAI/gte-tiny")
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# Tool Output Length Control Settings
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TOOL_OUTPUT_MAX_LENGTH = int(SUMMARIZATION_MAX_TOKENS/4)
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TOOL_OUTPUT_TRUNCATION_STRATEGY = os.getenv("TOOL_OUTPUT_TRUNCATION_STRATEGY", "smart")
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# THINKING ENABLE
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DEFAULT_THINKING_ENABLE = os.getenv("DEFAULT_THINKING_ENABLE", "true") == "true"
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# MCP Tool Timeout Settings
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MCP_HTTP_TIMEOUT = int(os.getenv("MCP_HTTP_TIMEOUT", 60)) # HTTP 请求超时（秒）
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MCP_SSE_READ_TIMEOUT = int(os.getenv("MCP_SSE_READ_TIMEOUT", 300)) # SSE 读取超时（秒）
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# ============================================================
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# PostgreSQL Checkpoint Configuration
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# ============================================================
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# PostgreSQL 连接字符串
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# 格式: postgresql://user:password@host:port/database
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CHECKPOINT_DB_URL = os.getenv("CHECKPOINT_DB_URL", "postgresql://postgres:AeEGDB0b7Z5GK0E2tblt@dev-circleo-pg.celp3nik7oaq.ap-northeast-1.rds.amazonaws.com:5432/gptbase")
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#CHECKPOINT_DB_URL = os.getenv("CHECKPOINT_DB_URL", "postgresql://postgres:E5ACJo6zJub4QS@192.168.102.5:5432/agent_db")
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# 连接池大小
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# 同时可以持有的最大连接数
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CHECKPOINT_POOL_SIZE = int(os.getenv("CHECKPOINT_POOL_SIZE", "20"))
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MEM0_POOL_SIZE = int(os.getenv("MEM0_POOL_SIZE", "20"))
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# Checkpoint 自动清理配置
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# 是否启用自动清理旧 session
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CHECKPOINT_CLEANUP_ENABLED = os.getenv("CHECKPOINT_CLEANUP_ENABLED", "true") == "true"
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# 清理多少天前未活动的 thread（天数）
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CHECKPOINT_CLEANUP_INACTIVE_DAYS = int(os.getenv("CHECKPOINT_CLEANUP_INACTIVE_DAYS", "3"))
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# 清理间隔（小时）
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# 每隔多少小时执行一次清理任务
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CHECKPOINT_CLEANUP_INTERVAL_HOURS = int(os.getenv("CHECKPOINT_CLEANUP_INTERVAL_HOURS", "24"))
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# ============================================================
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# Mem0 长期记忆配置
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# ============================================================
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# Mem0 功能开关（全局）
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MEM0_ENABLED = os.getenv("MEM0_ENABLED", "true") == "true"
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# 语义搜索配置
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# 召回记忆数量
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MEM0_SEMANTIC_SEARCH_TOP_K = int(os.getenv("MEM0_SEMANTIC_SEARCH_TOP_K", "20"))
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os.environ["OPENAI_API_KEY"] = "your_api_key"
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# ============================================================
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# RAGFlow Knowledge Base Configuration
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# ============================================================
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# RAGFlow API 配置
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RAGFLOW_API_URL = os.getenv("RAGFLOW_API_URL", "http://100.77.70.35:1080")
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RAGFLOW_API_KEY = os.getenv("RAGFLOW_API_KEY", "ragflow-MRqxnDnYZ1yp5kklDMIlKH4f1qezvXIngSMGPhu1AG8")
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# 文件上传配置
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RAGFLOW_MAX_UPLOAD_SIZE = int(os.getenv("RAGFLOW_MAX_UPLOAD_SIZE", str(100 * 1024 * 1024))) # 100MB
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RAGFLOW_ALLOWED_EXTENSIONS = os.getenv("RAGFLOW_ALLOWED_EXTENSIONS", "pdf,docx,txt,md,csv").split(",")
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# 性能配置
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RAGFLOW_CONNECTION_TIMEOUT = int(os.getenv("RAGFLOW_CONNECTION_TIMEOUT", "30")) # 30秒
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RAGFLOW_MAX_CONCURRENT_UPLOADS = int(os.getenv("RAGFLOW_MAX_CONCURRENT_UPLOADS", "5"))
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