import os
from pydantic import BaseModel, Field
from dotenv import load_dotenv

load_dotenv()

PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__))


class LLMConfig(BaseModel):
    api_key: str = Field(default=os.getenv("LLM_API_KEY", ""))
    base_url: str = Field(default=os.getenv("LLM_BASE_URL", "https://api.deepseek.com/v1"))
    model: str = Field(default=os.getenv("LLM_MODEL", "deepseek-chat"))
    max_tokens: int = Field(default=64000)
    temperature: float = Field(default=0.95)


class EmbeddingConfig(BaseModel):
    api_key: str = Field(default=os.getenv("EMBEDDING_API_KEY", ""))
    api_base: str = Field(default=os.getenv("EMBEDDING_BASE_URL", "https://api.openai.com/v1"))
    model: str = Field(default=os.getenv("EMBEDDING_MODEL", "text-embedding-3-small"))


class ObsidianConfig(BaseModel):
    vault_path: str = Field(default=os.path.join(PROJECT_ROOT, "obsidian_vault"))
    meetings_dir: str = Field(default="Meetings")
    entities_dir: str = Field(default="Entities")
    graphs_dir: str = Field(default="Graphs")
    raw_dir: str = Field(default="Raw")


class VectorStoreConfig(BaseModel):
    persist_dir: str = Field(default=os.path.join(PROJECT_ROOT, "vector_store_data"))


class ProjectConfig(BaseModel):
    llm: LLMConfig = Field(default_factory=LLMConfig)
    embedding: EmbeddingConfig = Field(default_factory=EmbeddingConfig)
    obsidian: ObsidianConfig = Field(default_factory=ObsidianConfig)
    vector_store: VectorStoreConfig = Field(default_factory=VectorStoreConfig)
    state_path: str = Field(default=os.path.join(PROJECT_ROOT, "obsidian_vault", "meeting_state.json"))


config = ProjectConfig()