feat: ollama embedder mit exponential backoff retry

app/ingest/embedder.py

@@ -0,0 +1,56 @@
+import asyncio
+import logging
+from functools import lru_cache
+
+from ollama import AsyncClient
+
+from app.config import get_settings
+
+
+logger = logging.getLogger(__name__)
+
+
+_BACKOFF_SECONDS = [1, 2, 4]
+
+
+class EmbeddingError(Exception):
+    pass
+
+
+@lru_cache(maxsize=1)
+def _client() -> AsyncClient:
+    return AsyncClient(host=get_settings().ollama_url)
+
+
+async def embed_texts(texts: list[str], model: str) -> list[list[float]]:
+    """Embed each text via Ollama. Retries individual calls 3x with backoff."""
+    vectors: list[list[float]] = []
+    for text in texts:
+        vec = await _embed_one(text, model)
+        vectors.append(vec)
+    return vectors
+
+
+async def _embed_one(text: str, model: str) -> list[float]:
+    last_err: Exception | None = None
+    client = _client()
+    for attempt in range(len(_BACKOFF_SECONDS) + 1):
+        try:
+            response = await client.embeddings(model=model, prompt=text)
+            return list(response["embedding"])
+        except Exception as exc:
+            last_err = exc
+            if attempt < len(_BACKOFF_SECONDS):
+                wait = _BACKOFF_SECONDS[attempt]
+                logger.warning(
+                    "ollama embed retry",
+                    extra={"event": "embed_retry", "attempt": attempt + 1, "wait_s": wait, "error": str(exc)},
+                )
+                await asyncio.sleep(wait)
+    raise EmbeddingError(f"embed failed after retries: {last_err}")
+
+
+async def embedding_dimension(model: str) -> int:
+    """Probe a single embedding to discover the model's vector dimension."""
+    vec = await _embed_one("dimension probe", model)
+    return len(vec)