Complete local hybrid search and improve agent reliability

SovereignAI-Starter/app/embeddings.py

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+"""Optional local Ollama embeddings for semantic knowledge retrieval."""
+
+from __future__ import annotations
+
+import math
+import os
+from typing import Sequence
+import httpx
+
+
+class EmbeddingUnavailable(RuntimeError):
+    """The configured local embedding model is missing or cannot embed text."""
+
+
+def embedding_model_name() -> str | None:
+    return os.getenv("KNOWLEDGE_EMBEDDING_MODEL", "granite-embedding:278m").strip() or None
+
+
+def _ollama_base_url() -> str:
+    configured = os.getenv("LOCAL_LLM_BASE_URL", "http://127.0.0.1:11434/v1").rstrip("/")
+    if configured.endswith("/v1"):
+        configured = configured[:-3]
+    return configured
+
+
+async def embed_texts(texts: Sequence[str], *, model: str | None = None) -> list[list[float]]:
+    """Generate bounded-size embedding batches through the local Ollama API."""
+    if not texts:
+        return []
+    configured_model = model if model is not None else embedding_model_name()
+    model_name = configured_model.strip() if configured_model else ""
+    if not model_name:
+        raise EmbeddingUnavailable("لم يُضبط نموذج التضمين المحلي.")
+    endpoint = f"{_ollama_base_url()}/api/embed"
+    results: list[list[float]] = []
+    try:
+        async with httpx.AsyncClient(timeout=httpx.Timeout(180.0, connect=5.0)) as client:
+            for offset in range(0, len(texts), 32):
+                batch = [str(text)[:4_000] for text in texts[offset : offset + 32]]
+                response = await client.post(
+                    endpoint,
+                    json={"model": model_name, "input": batch, "keep_alive": "5m"},
+                )
+                response.raise_for_status()
+                payload = response.json()
+                vectors = payload.get("embeddings") if isinstance(payload, dict) else None
+                if not isinstance(vectors, list) or len(vectors) != len(batch):
+                    raise EmbeddingUnavailable("أعاد نموذج التضمين عدد متجهات غير متوقع.")
+                dimension = None
+                for vector in vectors:
+                    if not isinstance(vector, list) or not vector:
+                        raise EmbeddingUnavailable("أعاد نموذج التضمين متجهًا فارغًا.")
+                    if dimension is None:
+                        dimension = len(vector)
+                    if len(vector) != dimension:
+                        raise EmbeddingUnavailable("أبعاد متجهات التضمين غير متطابقة.")
+                    numeric = [float(value) for value in vector]
+                    if not all(math.isfinite(value) for value in numeric):
+                        raise EmbeddingUnavailable("أعاد نموذج التضمين قيمًا غير صالحة.")
+                    if not any(value != 0 for value in numeric):
+                        raise EmbeddingUnavailable("أعاد نموذج التضمين متجهًا صفريًا.")
+                    results.append(numeric)
+    except EmbeddingUnavailable:
+        raise
+    except (httpx.HTTPError, ValueError, TypeError, KeyError) as exc:
+        raise EmbeddingUnavailable("نموذج التضمين المحلي غير متاح؛ سيبقى البحث النصي مستخدمًا.") from exc
+    return results