44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
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import os
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from pydantic import BaseModel, Field
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from dotenv import load_dotenv
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load_dotenv()
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PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))
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class LLMConfig(BaseModel):
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api_key: str = Field(default=os.getenv("LLM_API_KEY", ""))
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base_url: str = Field(default=os.getenv("LLM_BASE_URL", "https://api.deepseek.com/v1"))
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model: str = Field(default=os.getenv("LLM_MODEL", "deepseek-chat"))
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max_tokens: int = Field(default=64000)
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temperature: float = Field(default=0.95)
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class EmbeddingConfig(BaseModel):
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api_key: str = Field(default=os.getenv("EMBEDDING_API_KEY", ""))
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api_base: str = Field(default=os.getenv("EMBEDDING_BASE_URL", "https://api.openai.com/v1"))
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model: str = Field(default=os.getenv("EMBEDDING_MODEL", "text-embedding-3-small"))
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class ObsidianConfig(BaseModel):
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vault_path: str = Field(default=os.path.join(PROJECT_ROOT, "obsidian_vault"))
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meetings_dir: str = Field(default="Meetings")
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entities_dir: str = Field(default="Entities")
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graphs_dir: str = Field(default="Graphs")
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raw_dir: str = Field(default="Raw")
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class VectorStoreConfig(BaseModel):
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persist_dir: str = Field(default=os.path.join(PROJECT_ROOT, "vector_store_data"))
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class ProjectConfig(BaseModel):
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llm: LLMConfig = Field(default_factory=LLMConfig)
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embedding: EmbeddingConfig = Field(default_factory=EmbeddingConfig)
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obsidian: ObsidianConfig = Field(default_factory=ObsidianConfig)
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vector_store: VectorStoreConfig = Field(default_factory=VectorStoreConfig)
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state_path: str = Field(default=os.path.join(PROJECT_ROOT, "obsidian_vault", "meeting_state.json"))
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config = ProjectConfig()
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