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"""Application configuration using pydantic-settings."""
from pathlib import Path
from typing import Optional
from pydantic import Field
from pydantic_settings import BaseSettings, SettingsConfigDict
class ModelSettings(BaseSettings):
"""LLM and embedding model configuration."""
model_config = SettingsConfigDict(env_prefix="MODEL_")
# LLM Provider: "local", "openai", "groq", "anthropic", "openrouter"
# Default to "groq" (free tier available, no GPU required)
llm_provider: str = "groq"
# API settings (for remote providers)
llm_api_key: Optional[str] = None
llm_api_base: Optional[str] = None # Custom API base URL
# Model name (local or remote)
llm_name: str = "Qwen/Qwen2.5-Coder-3B-Instruct"
llm_max_new_tokens: int = 1024
llm_temperature: float = 0.1
llm_top_p: float = 0.95
# Local model settings
llm_use_4bit: bool = True
llm_device_map: str = "auto"
embedding_name: str = "nomic-ai/nomic-embed-text-v1.5"
embedding_dimension: int = 768
embedding_batch_size: int = 8 # Reduced for 8GB VRAM GPUs
embedding_device: str = "auto" # "auto" detects CUDA, falls back to CPU
class VectorStoreSettings(BaseSettings):
"""ChromaDB vector store configuration."""
model_config = SettingsConfigDict(env_prefix="VECTORSTORE_")
persist_directory: Path = Path("./data/chroma_db")
collection_name: str = "coderag_chunks"
distance_metric: str = "cosine"
anonymized_telemetry: bool = False
class IngestionSettings(BaseSettings):
"""Repository ingestion configuration."""
model_config = SettingsConfigDict(env_prefix="INGESTION_")
repos_cache_dir: Path = Path("./data/repos")
max_file_size_kb: int = 500
default_branch: str = "main"
chunk_size: int = 1500
chunk_overlap: int = 200
# Large repository handling
max_files_per_repo: int = 5000
max_total_chunks: int = 50000
batch_size: int = 100
stream_processing: bool = True
# Warning thresholds
warn_files_threshold: int = 1000
warn_chunks_threshold: int = 10000
include_patterns: list[str] = Field(
default_factory=lambda: ["*.py", "*.js", "*.ts", "*.java", "*.go", "*.rs", "*.c", "*.cpp", "*.h"]
)
exclude_patterns: list[str] = Field(
default_factory=lambda: [
"**/node_modules/**",
"**/.git/**",
"**/venv/**",
"**/__pycache__/**",
"**/dist/**",
"**/build/**",
"**/*.min.js",
"**/*.min.css",
"**/package-lock.json",
"**/yarn.lock",
"**/poetry.lock",
"**/.env",
"**/.env.*",
"**/credentials*",
"**/*secret*",
"**/*password*",
]
)
class RetrievalSettings(BaseSettings):
"""Retrieval configuration."""
model_config = SettingsConfigDict(env_prefix="RETRIEVAL_")
default_top_k: int = 5
max_top_k: int = 20
similarity_threshold: float = 0.3
class ServerSettings(BaseSettings):
"""Server configuration."""
model_config = SettingsConfigDict(env_prefix="SERVER_")
host: str = "0.0.0.0"
port: int = 8000
reload: bool = False
workers: int = 1
log_level: str = "info"
class Settings(BaseSettings):
"""Main application settings."""
model_config = SettingsConfigDict(
env_file=".env",
env_file_encoding="utf-8",
extra="ignore",
)
app_name: str = "CodeRAG"
app_version: str = "0.1.0"
debug: bool = False
data_dir: Path = Path("./data")
models: ModelSettings = Field(default_factory=ModelSettings)
vectorstore: VectorStoreSettings = Field(default_factory=VectorStoreSettings)
ingestion: IngestionSettings = Field(default_factory=IngestionSettings)
retrieval: RetrievalSettings = Field(default_factory=RetrievalSettings)
server: ServerSettings = Field(default_factory=ServerSettings)
def ensure_directories(self) -> None:
"""Create required directories if they don't exist."""
self.data_dir.mkdir(parents=True, exist_ok=True)
self.vectorstore.persist_directory.mkdir(parents=True, exist_ok=True)
self.ingestion.repos_cache_dir.mkdir(parents=True, exist_ok=True)
_settings: Optional[Settings] = None
def get_settings() -> Settings:
"""Get cached settings instance."""
global _settings
if _settings is None:
_settings = Settings()
_settings.ensure_directories()
return _settings
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