Phase 7: Hoàn thiện Modular RAG Backend với FastAPI và Đa LLM Provider

core/config.py

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+import os
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+class Settings(BaseSettings):
+    # Azure AD / Microsoft Graph
+    tenant_id: str = ""
+    client_id: str = ""
+    client_secret: str = ""
+    
+    # VLM (Vision-Language Model) Configuration
+    VLM_ENDPOINT: str = "http://10.202.50.3:8080/v1/chat/completions"
+    VLM_TEMPERATURE: float = 0.1
+    VLM_MAX_TOKENS: int = 2000
+    VLM_TIMEOUT: float = 120.0
+    
+    # SharePoint
+    sharepoint_site_id: str = ""
+    sharepoint_drive_id: str = ""
+
+    # OpenSearch
+    opensearch_host: str = "localhost"
+    opensearch_port: int = 9200
+    opensearch_user: str = "admin"
+    opensearch_pass: str = "admin"
+
+    # Chat LLM Config
+    llm_provider: str = "gemini"
+    gemini_api_key: str = ""
+    groq_api_key: str = ""
+    groq_model: str = "llama-3.3-70b-versatile"
+    local_llm_endpoint: str = "http://10.202.50.3:8081/v1/chat/completions"
+    openai_api_key: str = ""
+
+    # General Settings
+    log_level: str = "INFO"
+    environment: str = "development"
+
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
+
+settings = Settings()