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"""Settings and configuration for the Job Search MCP Server."""
import os
from functools import lru_cache
from typing import Optional
from dotenv import load_dotenv
from pydantic import Field
from pydantic_settings import BaseSettings
# Load environment variables from .env file
load_dotenv()
class Settings(BaseSettings):
"""Application settings and configuration."""
# API Keys
openai_api_key: Optional[str] = Field(default=None, env="OPENAI_API_KEY")
anthropic_api_key: Optional[str] = Field(default=None, env="ANTHROPIC_API_KEY")
hf_access_token: Optional[str] = Field(default=None, env="HF_ACCESS_TOKEN")
# Job Search APIs
linkedin_api_key: Optional[str] = Field(default=None, env="LINKEDIN_API_KEY")
indeed_api_key: Optional[str] = Field(default=None, env="INDEED_API_KEY")
remotive_api_url: str = Field(
default="https://remotive.com/api/remote-jobs", env="REMOTIVE_API_URL"
)
adzuna_app_id: Optional[str] = Field(default=None, env="ADZUNA_APP_ID")
adzuna_app_key: Optional[str] = Field(default=None, env="ADZUNA_APP_KEY")
adzuna_country: str = Field(default="gb", env="ADZUNA_COUNTRY")
# Embedding Model Settings
embedding_model: str = Field(default="all-MiniLM-L6-v2", env="EMBEDDING_MODEL")
embedding_dimension: int = Field(default=384, env="EMBEDDING_DIMENSION")
# LLM Settings
llm_provider: str = Field(
default="huggingface", env="LLM_PROVIDER"
) # openai, anthropic, huggingface
llm_model: str = Field(default="deepseek/deepseek-v3-turbo", env="LLM_MODEL")
hf_inference_provider: str = Field(
default="novita", env="HF_INFERENCE_PROVIDER"
) # novita, together, fireworks, etc.
max_tokens: int = Field(default=300, env="MAX_TOKENS")
temperature: float = Field(default=0.7, env="TEMPERATURE")
# Application Settings
app_name: str = Field(default="Job Search MCP Server", env="APP_NAME")
debug: bool = Field(default=False, env="DEBUG")
host: str = Field(default="127.0.0.1", env="HOST")
port: int = Field(default=7860, env="PORT")
# Storage Settings
profiles_db_path: str = Field(
default="./data/profiles.json", env="PROFILES_DB_PATH"
)
jobs_cache_path: str = Field(
default="./data/jobs_cache.json", env="JOBS_CACHE_PATH"
)
embeddings_cache_path: str = Field(
default="./data/embeddings.faiss", env="EMBEDDINGS_CACHE_PATH"
)
# Search Settings
max_jobs_per_search: int = Field(default=50, env="MAX_JOBS_PER_SEARCH")
similarity_threshold: float = Field(default=0.7, env="SIMILARITY_THRESHOLD")
class Config:
"""Pydantic config."""
env_file = ".env"
env_file_encoding = "utf-8"
@lru_cache()
def get_settings() -> Settings:
"""Get cached settings instance."""
settings = Settings()
# Debug print the HF token
if settings.hf_access_token:
print(
f"🔑 HF Access Token loaded: {settings.hf_access_token[:20]}...{settings.hf_access_token[-10:]}"
)
else:
print("❌ No HF Access Token found in environment variables")
return settings
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