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import os
from dotenv import load_dotenv
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from typing import Optional
load_dotenv()
gemini_api_key = os.getenv("GEMINI_API_KEY")
if not gemini_api_key:
raise ValueError("GEMINI_API_KEY não encontrada nas variáveis de ambiente")
class Config:
# Model configuration
GEMINI_MODEL = "gemini-2.5-flash"
GEMINI_EMBEDDING_MODEL = "models/embedding-001"
GEMINI_API_KEY = gemini_api_key
# Application configuration
MAX_UPLOAD_LENGTH = 16 * 1024 * 1024
MAX_CONVERSATION_LENGTH = 100 # Maximum messages per conversation
MAX_CONTEXT_MESSAGES = 10 # Maximum messages to include in context
# Agent configuration
AGENTS_CONFIG = {
"agents": [
{
"name": "crypto_data",
"description": "Handles cryptocurrency-related queries",
"type": "specialized",
"enabled": True,
"priority": 1
},
{
"name": "general",
"description": "Handles general conversation and queries",
"type": "general",
"enabled": True,
"priority": 2
}
]
}
# LangGraph configuration
LANGGRAPH_CONFIG = {
"max_iterations": 10,
"timeout": 30,
"memory_window": 10,
"enable_memory": True
}
# Conversation configuration
CONVERSATION_CONFIG = {
"default_user_id": "anonymous",
"max_conversations_per_user": 50,
"conversation_timeout_hours": 24,
"enable_context_extraction": True
}
# LLM instances (singleton pattern)
_llm_instance: Optional[ChatGoogleGenerativeAI] = None
_embeddings_instance: Optional[GoogleGenerativeAIEmbeddings] = None
@classmethod
def get_llm(cls) -> ChatGoogleGenerativeAI:
"""Get or create LLM instance (singleton)"""
if cls._llm_instance is None:
cls._llm_instance = ChatGoogleGenerativeAI(
model=cls.GEMINI_MODEL,
temperature=0.7,
google_api_key=cls.GEMINI_API_KEY
)
return cls._llm_instance
@classmethod
def get_embeddings(cls) -> GoogleGenerativeAIEmbeddings:
"""Get or create embeddings instance (singleton)"""
if cls._embeddings_instance is None:
cls._embeddings_instance = GoogleGenerativeAIEmbeddings(
model=cls.GEMINI_EMBEDDING_MODEL,
google_api_key=cls.GEMINI_API_KEY
)
return cls._embeddings_instance
@classmethod
def get_agent_config(cls, agent_name: str) -> Optional[dict]:
"""Get configuration for a specific agent"""
for agent in cls.AGENTS_CONFIG["agents"]:
if agent["name"] == agent_name:
return agent
return None
@classmethod
def get_enabled_agents(cls) -> list:
"""Get list of enabled agents"""
return [
agent for agent in cls.AGENTS_CONFIG["agents"]
if agent.get("enabled", True)
]
@classmethod
def validate_config(cls) -> bool:
"""Validate configuration"""
try:
# Test LLM connection
llm = cls.get_llm()
# Test embeddings connection
embeddings = cls.get_embeddings()
return True
except Exception as e:
print(f"Configuration validation failed: {e}")
return False |