Initial commit: VoIdeaAI - voice-first AI idea assistant

app/services/llm_service.py

@@ -0,0 +1,130 @@
+"""Unified LLM service for VoIdeaAI.
+
+Supports OpenAI-compatible APIs (OpenAI, YandexGPT via API).
+"""
+
+import json
+import logging
+from datetime import datetime, timezone
+from typing import Any
+
+import httpx
+
+from app.core.config import get_settings
+from app.core.database import async_session_maker
+from app.models.log import LogEntry
+from app.services.debug_service import DebugService
+
+logger = logging.getLogger("voidea.llm")
+
+OPENAI_URL = "https://api.openai.com/v1/chat/completions"
+
+
+async def _log_llm_call(messages: list[dict], response: str | None, model: str, duration_ms: int):
+    """Log LLM call details if debug mode is active."""
+    try:
+        async with async_session_maker() as db:
+            svc = DebugService(db)
+            if await svc.is_debug_mode():
+                log = LogEntry(
+                    level="DEBUG",
+                    source="llm",
+                    message=f"LLM call: model={model} duration={duration_ms}ms success={response is not None}",
+                    details=json.dumps({
+                        "messages": messages,
+                        "response": response,
+                        "model": model,
+                    }, ensure_ascii=False),
+                    created_at=datetime.now(timezone.utc),
+                )
+                db.add(log)
+                await db.commit()
+    except Exception:
+        pass
+
+
+async def chat_completion(
+    messages: list[dict[str, str]],
+    model: str = "gpt-4o-mini",
+    temperature: float = 0.7,
+    max_tokens: int = 1024,
+) -> str | None:
+    """Call an LLM with OpenAI-compatible chat completions format.
+
+    Args:
+        messages: List of {"role": "system"|"user"|"assistant", "content": "..."}
+        model: Model name
+        temperature: Response creativity
+        max_tokens: Max tokens in response
+
+    Returns:
+        Response text or None on failure
+    """
+    import time
+    start = time.time()
+
+    settings = get_settings()
+    api_key = settings.openai_api_key or settings.ai_yandex_key or ""
+    if not api_key:
+        await _log_llm_call(messages, None, model, 0)
+        return None
+
+    url = settings.ai_yandex_url.rstrip("/") + "/chat/completions" if settings.ai_yandex_key else OPENAI_URL
+
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        try:
+            resp = await client.post(
+                url,
+                headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
+                json={
+                    "model": model,
+                    "messages": messages,
+                    "temperature": temperature,
+                    "max_tokens": max_tokens,
+                },
+            )
+            elapsed = int((time.time() - start) * 1000)
+            if resp.status_code != 200:
+                await _log_llm_call(messages, None, model, elapsed)
+                return None
+            data = resp.json()
+            content = data["choices"][0]["message"]["content"]
+            await _log_llm_call(messages, content, model, elapsed)
+            return content
+        except Exception as e:
+            elapsed = int((time.time() - start) * 1000)
+            await _log_llm_call(messages, None, model, elapsed)
+            return None
+
+
+async def classify_intent(text: str, agents: list[dict[str, Any]]) -> str | None:
+    """Use LLM to classify user intent and select the best agent.
+
+    Args:
+        text: User input text
+        agents: List of agent dicts with name and description
+
+    Returns:
+        Selected agent name or None
+    """
+    agent_list = "\n".join(f"- {a['name']}: {a['description']}" for a in agents)
+
+    prompt = f"""Ты — дирижёр умных ассистентов. Определи, какой агент лучше всего подходит для ответа пользователю.
+
+Доступные агенты:
+{agent_list}
+
+Ответь ТОЛЬКО именем агента, без пояснений."""
+
+    messages = [
+        {"role": "system", "content": prompt},
+        {"role": "user", "content": text},
+    ]
+
+    result = await chat_completion(messages, temperature=0.3, max_tokens=64)
+    if not result:
+        return None
+
+    result = result.strip().strip('"').strip("'")
+    agent_names = {a["name"] for a in agents}
+    return result if result in agent_names else None