Initial commit: Pegas Memory (PG + Qdrant + Memgraph, MCP server, 14 tools)

pegas_memory/embedding.py

@@ -0,0 +1,27 @@
+"""EmbeddingGemma 2 через Ollama (dim=768)."""
+from __future__ import annotations
+import httpx
+from . import config
+
+_client = httpx.Client(timeout=120)
+
+def embed(text: str) -> list[float]:
+    """Вернуть вектор для одного текста."""
+    r = _client.post(f"{config.OLLAMA_URL}/api/embed", json={
+        "model": config.EMBED_MODEL,
+        "input": text,
+    })
+    r.raise_for_status()
+    data = r.json()
+    vec = data["embeddings"][0]
+    assert len(vec) == config.EMBED_DIM, f"unexpected dim {len(vec)}"
+    return vec
+
+def embed_batch(texts: list[str]) -> list[list[float]]:
+    """Вернуть векторы для пачки текстов (один запрос)."""
+    r = _client.post(f"{config.OLLAMA_URL}/api/embed", json={
+        "model": config.EMBED_MODEL,
+        "input": texts,
+    })
+    r.raise_for_status()
+    return r.json()["embeddings"]