364 lines
12 KiB
Python
364 lines
12 KiB
Python
# ============================================================
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# LLM_PROVIDERS.PY - Abstração de Provedores de LLM
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# Suporta: Gemini, OpenAI, Anthropic, Ollama (Local)
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# ============================================================
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import os
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import requests
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import json
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from typing import Optional, Dict, List
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# ============================================================
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# CONFIGURAÇÃO DE PROVIDERS
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# ============================================================
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LLM_PROVIDERS = {
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"gemini": {
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"name": "Google Gemini",
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"type": "api",
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"models": ["gemini-2.5-flash", "gemini-2.0-pro", "gemini-1.5-flash"],
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"default": "gemini-2.5-flash",
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"endpoint": "https://generativelanguage.googleapis.com/v1beta/models"
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},
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"openai": {
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"name": "OpenAI GPT",
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"type": "api",
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"models": ["gpt-4o", "gpt-4-turbo", "gpt-3.5-turbo"],
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"default": "gpt-4o",
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"endpoint": "https://api.openai.com/v1"
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},
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"anthropic": {
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"name": "Anthropic Claude",
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"type": "api",
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"models": ["claude-3-5-sonnet-20241022", "claude-3-5-haiku-20241022", "claude-3-opus-20240229"],
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"default": "claude-3-5-sonnet-20241022",
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"endpoint": "https://api.anthropic.com/v1"
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},
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"ollama": {
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"name": "Ollama (Local)",
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"type": "local",
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"endpoint": os.getenv("OLLAMA_HOST", "http://localhost:11434"),
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"models": None,
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"default": "qwen2.5-coder:1.5b"
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}
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}
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# ============================================================
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# CONFIG MANAGER
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# ============================================================
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CONFIG_FILE = "/app/data/config.json"
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def get_config() -> dict:
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"""Carrega configuração do orchestrator."""
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if not os.path.exists("/app/data"):
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os.makedirs("/app/data", exist_ok=True)
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if os.path.exists(CONFIG_FILE):
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try:
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with open(CONFIG_FILE, "r") as f:
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return json.load(f)
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except Exception:
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pass
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return {
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"orchestrator": {
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"planner": {"provider": "gemini", "model": "gemini-2.5-flash"},
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"executor": {"provider": "ollama", "model": "qwen2.5-coder:1.5b"}
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},
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"api_keys": {
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"openai": "",
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"anthropic": "",
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"gemini": ""
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}
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}
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def save_config(cfg: dict):
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"""Salva configuração do orchestrator."""
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if not os.path.exists("/app/data"):
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os.makedirs("/app/data", exist_ok=True)
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with open(CONFIG_FILE, "w") as f:
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json.dump(cfg, f, indent=4)
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def get_orchestrator_config() -> dict:
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"""Retorna config do orchestrator."""
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cfg = get_config()
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return cfg.get("orchestrator", {
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"planner": {"provider": "gemini", "model": "gemini-2.5-flash"},
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"executor": {"provider": "ollama", "model": "qwen2.5-coder:1.5b"}
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})
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def set_planner(provider: str = None, model: str = None) -> dict:
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"""Define o provider e modelo do planner."""
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cfg = get_config()
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if "orchestrator" not in cfg:
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cfg["orchestrator"] = {}
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if provider:
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cfg["orchestrator"]["planner"] = {
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"provider": provider,
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"model": model or LLM_PROVIDERS[provider]["default"]
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}
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save_config(cfg)
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return cfg["orchestrator"].get("planner", {"provider": "gemini", "model": "gemini-2.5-flash"})
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def set_executor(provider: str = None, model: str = None) -> dict:
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"""Define o provider e modelo do executor."""
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cfg = get_config()
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if "orchestrator" not in cfg:
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cfg["orchestrator"] = {}
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if provider:
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cfg["orchestrator"]["executor"] = {
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"provider": provider,
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"model": model or LLM_PROVIDERS[provider]["default"]
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}
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save_config(cfg)
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return cfg["orchestrator"].get("executor", {"provider": "ollama", "model": "qwen2.5-coder:1.5b"})
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return cfg["orchestrator"]["executor"]
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def set_api_key(provider: str, key: str):
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"""Armazena API key de um provider."""
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cfg = get_config()
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if "api_keys" not in cfg:
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cfg["api_keys"] = {}
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cfg["api_keys"][provider] = key
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save_config(cfg)
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def get_api_key(provider: str) -> str:
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"""Busca API key de um provider (config ou env var)."""
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cfg = get_config()
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# Primeiro verifica config
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api_keys = cfg.get("api_keys", {})
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if api_keys.get(provider):
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return api_keys[provider]
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# Fallback para environment variable
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env_vars = {
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"openai": "OPENAI_API_KEY",
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"anthropic": "ANTHROPIC_API_KEY",
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"gemini": "GEMINI_API_KEY"
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}
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if provider in env_vars:
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return os.getenv(env_vars[provider], "")
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return ""
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# ============================================================
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# OLLAMA DISCOVERY
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# ============================================================
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def list_ollama_models() -> List[str]:
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"""Busca modelos disponíveis no Ollama."""
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try:
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endpoint = LLM_PROVIDERS["ollama"]["endpoint"]
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response = requests.get(f"{endpoint}/api/tags", timeout=5)
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if response.status_code == 200:
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models = [m["name"] for m in response.json().get("models", [])]
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LLM_PROVIDERS["ollama"]["models"] = models
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return models
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except Exception as e:
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print(f"Erro ao buscar modelos Ollama: {e}")
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return []
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def get_available_models(provider: str = None) -> List[Dict]:
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"""Retorna modelos disponíveis para um provider ou todos."""
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if provider:
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p = LLM_PROVIDERS.get(provider)
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if not p:
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return []
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if p["type"] == "local" and provider == "ollama":
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models = list_ollama_models()
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return [{"provider": provider, "models": models}]
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else:
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return [{"provider": provider, "models": p.get("models", [p["default"]])}]
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# Todos os providers
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result = []
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for prov_id, prov in LLM_PROVIDERS.items():
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if prov_id == "ollama":
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models = list_ollama_models()
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result.append({"provider": prov_id, "name": prov["name"], "models": models})
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else:
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result.append({"provider": prov_id, "name": prov["name"], "models": prov.get("models", [prov["default"]])})
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return result
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# ============================================================
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# LLM CALL FUNCTIONS
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# ============================================================
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def call_llm(provider: str, model: str, prompt: str, system_prompt: str = None, **kwargs) -> str:
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"""
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Chama o LLM especificado.
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Args:
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provider: Nome do provider (gemini, openai, anthropic, ollama)
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model: Nome do modelo
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prompt: Prompt do usuário
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system_prompt: Prompt de sistema (opcional)
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Returns:
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Resposta do LLM como string
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"""
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if provider == "gemini":
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return _call_gemini(model, prompt, system_prompt)
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elif provider == "openai":
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return _call_openai(model, prompt, system_prompt)
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elif provider == "anthropic":
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return _call_anthropic(model, prompt, system_prompt)
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elif provider == "ollama":
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return _call_ollama(model, prompt, system_prompt)
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else:
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return f"Erro: Provider '{provider}' não suportado."
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# ----------------------------------------
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# GEMINI
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# ----------------------------------------
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def _call_gemini(model: str, prompt: str, system_prompt: str = None) -> str:
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"""Chama API do Google Gemini."""
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api_key = get_api_key("gemini")
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if not api_key:
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api_key = os.getenv("GEMINI_API_KEY", "")
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url = f"https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent?key={api_key}"
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contents = [{"parts": [{"text": prompt}]}]
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if system_prompt:
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contents.insert(0, {"role": "model", "parts": [{"text": system_prompt}]})
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payload = {"contents": contents}
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try:
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res = requests.post(url, json=payload, timeout=60)
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if res.status_code == 200:
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return res.json()["candidates"][0]["content"]["parts"][0]["text"]
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return f"Erro Gemini: {res.status_code} - {res.text}"
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except Exception as e:
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return f"Erro Gemini: {str(e)}"
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# ----------------------------------------
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# OPENAI
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# ----------------------------------------
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def _call_openai(model: str, prompt: str, system_prompt: str = None) -> str:
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"""Chama API da OpenAI."""
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api_key = get_api_key("openai")
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if not api_key:
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api_key = os.getenv("OPENAI_API_KEY", "")
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url = f"https://api.openai.com/v1/chat/completions"
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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payload = {
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"model": model,
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"messages": messages,
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"temperature": 0.7
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}
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try:
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res = requests.post(url, json=payload, headers={
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}, timeout=60)
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if res.status_code == 200:
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return res.json()["choices"][0]["message"]["content"]
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return f"Erro OpenAI: {res.status_code} - {res.text}"
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except Exception as e:
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return f"Erro OpenAI: {str(e)}"
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# ----------------------------------------
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# ANTHROPIC
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# ----------------------------------------
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def _call_anthropic(model: str, prompt: str, system_prompt: str = None) -> str:
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"""Chama API da Anthropic (Claude)."""
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api_key = get_api_key("anthropic")
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if not api_key:
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api_key = os.getenv("ANTHROPIC_API_KEY", "")
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url = "https://api.anthropic.com/v1/messages"
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headers = {
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"x-api-key": api_key,
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"anthropic-version": "2023-06-01",
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"content-type": "application/json"
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}
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payload = {
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"model": model,
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"max_tokens": 4096,
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"messages": [{"role": "user", "content": prompt}]
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}
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if system_prompt:
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payload["system"] = system_prompt
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try:
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res = requests.post(url, json=payload, headers=headers, timeout=60)
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if res.status_code == 200:
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return res.json()["content"][0]["text"]
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return f"Erro Anthropic: {res.status_code} - {res.text}"
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except Exception as e:
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return f"Erro Anthropic: {str(e)}"
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# ----------------------------------------
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# OLLAMA (LOCAL)
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# ----------------------------------------
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def _call_ollama(model: str, prompt: str, system_prompt: str = None) -> str:
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"""Chama Ollama local."""
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endpoint = LLM_PROVIDERS["ollama"]["endpoint"]
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payload = {
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"model": model,
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"prompt": prompt,
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"stream": False
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}
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if system_prompt:
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payload["system"] = system_prompt
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try:
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res = requests.post(f"{endpoint}/api/generate", json=payload, timeout=120)
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if res.status_code == 200:
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return res.json().get("response", "")
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return f"Erro Ollama: {res.status_code} - {res.text}"
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except Exception as e:
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return f"Erro Ollama: {str(e)}"
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# ============================================================
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# HELPER FUNCTIONS
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# ============================================================
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def get_planner_llm() -> tuple:
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"""Retorna provider e modelo do planner configurado."""
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cfg = get_orchestrator_config()
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planner = cfg.get("planner", {"provider": "gemini", "model": "gemini-2.5-flash"})
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return planner["provider"], planner["model"]
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def get_executor_llm() -> tuple:
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"""Retorna provider e modelo do executor configurado."""
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cfg = get_orchestrator_config()
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executor = cfg.get("executor", {"provider": "ollama", "model": "qwen2.5-coder:1.5b"})
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return executor["provider"], executor["model"]
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def call_planner(prompt: str, system_prompt: str = None) -> str:
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"""Chama o LLM do planner com a config atual."""
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provider, model = get_planner_llm()
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return call_llm(provider, model, prompt, system_prompt)
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def call_executor(prompt: str, system_prompt: str = None) -> str:
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"""Chama o LLM do executor com a config atual."""
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provider, model = get_executor_llm()
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return call_llm(provider, model, prompt, system_prompt)
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