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from typing import Optional, Dict, Any
from pydantic_settings import BaseSettings, SettingsConfigDict
from pydantic import Field
from pathlib import Path
class LLMConfig(BaseSettings):
model_name: str = Field(
default="llama3.2",
description="Name of the LLM model to use"
)
model_provider: str = Field(
default="ollama",
description="Model provider (anthropic, openai, ollama, etc)"
)
api_key: Optional[str] = Field(
default=None,
description="API key for the model provider"
)
model_settings: Dict[str, Any] = Field(
default_factory=dict,
description="Additional model settings"
)
embedding_model: str = Field(
default="mxbai-embed-large",
description="Name of the embedding model to use"
)
embedding_provider: str = Field(
default="ollama",
description="Provider for embeddings (ollama, openai, etc)"
)
class DBConfig(BaseSettings):
persist_directory: Path = Field(
default=Path("./chroma_db"),
description="Directory to store ChromaDB files"
)
collection_name: str = Field(
default="code_chunks",
description="Name of the ChromaDB collection"
)
embedding_model: str = Field(
default="sentence-transformers/all-mpnet-base-v2",
description="Embedding model to use"
)
class AppConfig(BaseSettings):
model_config = SettingsConfigDict(
env_file='.env',
env_file_encoding='utf-8',
env_nested_delimiter='__'
)
llm: LLMConfig = Field(default_factory=LLMConfig)
db: DBConfig = Field(default_factory=DBConfig)
chunk_max_size: int = Field(
default=1500,
description="Maximum size of code chunks before splitting"
) |