"""
Configuration settings for Codebase RAG.
This module defines all application settings using Pydantic Settings.
Settings can be configured via environment variables or .env file.
"""
from pydantic_settings import BaseSettings
from pydantic import Field
from typing import Optional, Literal, Dict, Any
class Settings(BaseSettings):
# Application Settings
app_name: str = "Code Graph Knowledge Service"
app_version: str = "1.0.0"
debug: bool = False
# Server Settings (Two-Port Architecture)
host: str = Field(default="0.0.0.0", description="Host for all services", alias="HOST")
# Port configuration
port: int = Field(default=8123, description="Legacy port (deprecated)", alias="PORT")
mcp_port: int = Field(default=8000, description="MCP SSE service port (PRIMARY)", alias="MCP_PORT")
web_ui_port: int = Field(default=8080, description="Web UI + REST API port (SECONDARY)", alias="WEB_UI_PORT")
# Vector Search Settings (using Neo4j built-in vector index)
vector_index_name: str = Field(default="knowledge_vectors", description="Neo4j vector index name")
vector_dimension: int = Field(default=384, description="Vector embedding dimension")
# Neo4j Graph Database
neo4j_uri: str = Field(default="bolt://localhost:7687", description="Neo4j connection URI", alias="NEO4J_URI")
neo4j_username: str = Field(default="neo4j", description="Neo4j username", alias="NEO4J_USER")
neo4j_password: str = Field(default="password", description="Neo4j password", alias="NEO4J_PASSWORD")
neo4j_database: str = Field(default="neo4j", description="Neo4j database name")
# LLM Provider Configuration
llm_provider: Literal["ollama", "openai", "gemini", "openrouter"] = Field(
default="ollama",
description="LLM provider to use",
alias="LLM_PROVIDER"
)
# Ollama LLM Service
ollama_base_url: str = Field(default="http://localhost:11434", description="Ollama service URL", alias="OLLAMA_HOST")
ollama_model: str = Field(default="llama2", description="Ollama model name", alias="OLLAMA_MODEL")
# OpenAI Configuration
openai_api_key: Optional[str] = Field(default=None, description="OpenAI API key", alias="OPENAI_API_KEY")
openai_model: str = Field(default="gpt-3.5-turbo", description="OpenAI model name", alias="OPENAI_MODEL")
openai_base_url: Optional[str] = Field(default=None, description="OpenAI API base URL", alias="OPENAI_BASE_URL")
# Google Gemini Configuration
google_api_key: Optional[str] = Field(default=None, description="Google API key", alias="GOOGLE_API_KEY")
gemini_model: str = Field(default="gemini-pro", description="Gemini model name", alias="GEMINI_MODEL")
# OpenRouter Configuration
openrouter_api_key: Optional[str] = Field(default=None, description="OpenRouter API key", alias="OPENROUTER_API_KEY")
openrouter_base_url: str = Field(default="https://openrouter.ai/api/v1", description="OpenRouter API base URL", alias="OPENROUTER_BASE_URL")
openrouter_model: Optional[str] = Field(default="openai/gpt-3.5-turbo", description="OpenRouter model", alias="OPENROUTER_MODEL")
openrouter_max_tokens: int = Field(default=2048, description="OpenRouter max tokens for completion", alias="OPENROUTER_MAX_TOKENS")
# Embedding Provider Configuration
embedding_provider: Literal["ollama", "openai", "gemini", "openrouter"] = Field(
default="ollama",
description="Embedding provider to use",
alias="EMBEDDING_PROVIDER"
)
# Ollama Embedding
ollama_embedding_model: str = Field(default="nomic-embed-text", description="Ollama embedding model", alias="OLLAMA_EMBEDDING_MODEL")
# OpenAI Embedding
openai_embedding_model: str = Field(default="text-embedding-ada-002", description="OpenAI embedding model", alias="OPENAI_EMBEDDING_MODEL")
# Gemini Embedding
gemini_embedding_model: str = Field(default="models/embedding-001", description="Gemini embedding model", alias="GEMINI_EMBEDDING_MODEL")
# OpenRouter Embedding
openrouter_embedding_model: str = Field(default="text-embedding-ada-002", description="OpenRouter embedding model", alias="OPENROUTER_EMBEDDING_MODEL")
# Model Parameters
temperature: float = Field(default=0.1, description="LLM temperature")
max_tokens: int = Field(default=2048, description="Maximum tokens for LLM response")
# RAG Settings
chunk_size: int = Field(default=512, description="Text chunk size for processing")
chunk_overlap: int = Field(default=50, description="Chunk overlap size")
top_k: int = Field(default=5, description="Top K results for retrieval")
# Timeout Settings
connection_timeout: int = Field(default=30, description="Connection timeout in seconds")
operation_timeout: int = Field(default=120, description="Operation timeout in seconds")
large_document_timeout: int = Field(default=300, description="Large document processing timeout in seconds")
# Document Processing Settings
max_document_size: int = Field(default=10 * 1024 * 1024, description="Maximum document size in bytes (10MB)")
max_payload_size: int = Field(default=50 * 1024 * 1024, description="Maximum task payload size for storage (50MB)")
ingestion_pipelines: Dict[str, Dict[str, Any]] = Field(
default_factory=dict,
description="Optional ingestion pipeline overrides",
)
# API Settings
cors_origins: list = Field(default=["*"], description="CORS allowed origins")
api_key: Optional[str] = Field(default=None, description="API authentication key")
# logging
log_file: Optional[str] = Field(default="app.log", description="Log file path")
log_level: str = Field(default="INFO", description="Log level")
class Config:
env_file = ".env"
env_file_encoding = "utf-8"
extra = "ignore" # Ignore extra fields to avoid validation errors
# Global settings instance
settings = Settings()
