Backup automatico script del 2026-08-23 12:04

services/loogle-mcp/app/knowledge/embeddings.py

@@ -0,0 +1,87 @@
+# -*- coding: utf-8 -*-
+"""Embedding providers — con thermal gate e keep_alive adattivo."""
+
+import logging
+import os
+import time
+from typing import Optional
+
+import httpx
+
+from . import thermal
+
+LOGGER = logging.getLogger("loogle_mcp.embeddings")
+
+
+def embed_texts(texts: list[str]) -> list[list[float]]:
+    if not texts:
+        return []
+    ollama_url = os.environ.get("OLLAMA_URL", "").strip()
+    if ollama_url:
+        try:
+            return _embed_ollama(texts, ollama_url)
+        except Exception as exc:
+            LOGGER.warning("Ollama embedding failed: %s", exc)
+    openai_key = os.environ.get("OPENAI_API_KEY", "").strip()
+    if openai_key:
+        return _embed_openai(texts, openai_key)
+    raise RuntimeError("Nessun provider embedding configurato (OLLAMA_URL o OPENAI_API_KEY)")
+
+
+def _embed_ollama(texts: list[str], base_url: str) -> list[list[float]]:
+    model = os.environ.get("OLLAMA_EMBED_MODEL", "nomic-embed-text")
+    vectors = []
+    timeout = httpx.Timeout(connect=30.0, read=300.0, write=30.0, pool=30.0)
+    with httpx.Client(timeout=timeout) as client:
+        for i, text in enumerate(texts):
+            status = thermal.wait_for_headroom(context=f"embed:{i+1}/{len(texts)}")
+            keep_alive = thermal.suggested_keep_alive(status)
+            delay = thermal.suggested_delay_s(status)
+
+            payload = {"model": model, "prompt": text, "keep_alive": keep_alive}
+            # options.num_thread limita i thread CPU lato Ollama (se supportato)
+            num_thread = os.environ.get("OLLAMA_NUM_THREAD", "").strip()
+            if num_thread:
+                try:
+                    payload["options"] = {"num_thread": int(num_thread)}
+                except ValueError:
+                    pass
+
+            resp = client.post(f"{base_url.rstrip('/')}/api/embeddings", json=payload)
+            if resp.status_code >= 400:
+                LOGGER.warning(
+                    "Ollama embeddings HTTP %s: %s — payload keys=%s",
+                    resp.status_code,
+                    resp.text[:300],
+                    list(payload.keys()),
+                )
+            resp.raise_for_status()
+            vectors.append(resp.json()["embedding"])
+
+            if delay > 0 and i + 1 < len(texts):
+                time.sleep(delay)
+
+            # Unload solo se esplicitamente richiesto (zona HARD) — evita spike da reload
+            if keep_alive == 0:
+                try:
+                    client.post(
+                        f"{base_url.rstrip('/')}/api/generate",
+                        json={"model": model, "keep_alive": 0},
+                        timeout=30.0,
+                    )
+                except Exception:
+                    pass
+    return vectors
+
+
+def _embed_openai(texts: list[str], api_key: str) -> list[list[float]]:
+    model = os.environ.get("OPENAI_EMBED_MODEL", "text-embedding-3-small")
+    with httpx.Client(timeout=120.0) as client:
+        resp = client.post(
+            "https://api.openai.com/v1/embeddings",
+            headers={"Authorization": f"Bearer {api_key}"},
+            json={"model": model, "input": texts},
+        )
+        resp.raise_for_status()
+        data = resp.json()["data"]
+        return [item["embedding"] for item in sorted(data, key=lambda x: x["index"])]