53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import os
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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# App
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APP_TITLE: str = "LightRAG Knowledge Base API"
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APP_VERSION: str = "1.0"
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HOST: str = "0.0.0.0"
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PORT: int = 9600
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# Data
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DATA_DIR: str = "./index_data"
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# LLM (Text) - vLLM
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LLM_BINDING: str = "vllm" # ollama, vllm, openai
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LLM_BINDING_HOST: str = "http://192.168.6.115:8002/v1" # vLLM OpenAI API base
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LLM_MODEL: str = "qwen2.5-7b-awq"
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LLM_KEY: str = "EMPTY" # vLLM default key
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# LLM (Vision) - vLLM
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VL_BINDING: str = "vllm" # ollama, vllm, openai
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VL_BINDING_HOST: str = "http://192.168.6.115:8001/v1"
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VL_MODEL: str = "qwen2.5-vl-3b-awq"
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VL_KEY: str = "EMPTY"
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# Embedding - TEI
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EMBEDDING_BINDING: str = "tei" # ollama, tei, openai
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EMBEDDING_BINDING_HOST: str = "http://192.168.6.115:8003" # TEI usually exposes /embed
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EMBEDDING_MODEL: str = "BAAI/bge-m3" # model id in TEI
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EMBEDDING_KEY: str = "EMPTY"
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# Rerank - TEI
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RERANK_ENABLED: bool = True
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RERANK_BINDING_HOST: str = "http://192.168.6.115:8004"
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RERANK_MODEL: str = "BAAI/bge-reranker-v2-m3"
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RERANK_KEY: str = "EMPTY"
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# RAG Config
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EMBEDDING_DIM: int = 1024
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MAX_TOKEN_SIZE: int = 8192
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MAX_RAG_INSTANCES: int = 3 # 最大活跃 RAG 实例数
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COSINE_THRESHOLD: float = 0.4 # 向量检索相似度阈值
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# Admin & Security
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ADMIN_TOKEN: str = "fzy"
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class Config:
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env_file = ".env"
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env_file_encoding = 'utf-8'
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extra = "ignore" # 忽略多余的环境变量
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settings = Settings()
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