feat: Add handler-base library for NATS AI/ML services

- Handler base class with graceful shutdown and signal handling
- NATSClient with JetStream and msgpack serialization
- Pydantic Settings for environment configuration
- HealthServer for Kubernetes probes
- OpenTelemetry telemetry setup
- Service clients: STT, TTS, LLM, Embeddings, Reranker, Milvus

handler_base/clients/embeddings.py

@@ -0,0 +1,91 @@
+"""
+Embeddings service client (Infinity/BGE).
+"""
+import logging
+from typing import Optional
+
+import httpx
+
+from handler_base.config import EmbeddingsSettings
+from handler_base.telemetry import create_span
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingsClient:
+    """
+    Client for the embeddings service (Infinity with BGE models).
+    
+    Usage:
+        client = EmbeddingsClient()
+        embeddings = await client.embed(["Hello world"])
+    """
+    
+    def __init__(self, settings: Optional[EmbeddingsSettings] = None):
+        self.settings = settings or EmbeddingsSettings()
+        self._client = httpx.AsyncClient(
+            base_url=self.settings.embeddings_url,
+            timeout=self.settings.http_timeout,
+        )
+    
+    async def close(self) -> None:
+        """Close the HTTP client."""
+        await self._client.aclose()
+    
+    async def embed(
+        self,
+        texts: list[str],
+        model: Optional[str] = None,
+    ) -> list[list[float]]:
+        """
+        Generate embeddings for a list of texts.
+        
+        Args:
+            texts: List of texts to embed
+            model: Model name (defaults to settings)
+            
+        Returns:
+            List of embedding vectors
+        """
+        model = model or self.settings.embeddings_model
+        
+        with create_span("embeddings.embed") as span:
+            if span:
+                span.set_attribute("embeddings.model", model)
+                span.set_attribute("embeddings.batch_size", len(texts))
+            
+            response = await self._client.post(
+                "/embeddings",
+                json={"input": texts, "model": model},
+            )
+            response.raise_for_status()
+            
+            result = response.json()
+            embeddings = [d["embedding"] for d in result.get("data", [])]
+            
+            if span:
+                span.set_attribute("embeddings.dimensions", len(embeddings[0]) if embeddings else 0)
+            
+            return embeddings
+    
+    async def embed_single(self, text: str, model: Optional[str] = None) -> list[float]:
+        """
+        Generate embedding for a single text.
+        
+        Args:
+            text: Text to embed
+            model: Model name (defaults to settings)
+            
+        Returns:
+            Embedding vector
+        """
+        embeddings = await self.embed([text], model)
+        return embeddings[0] if embeddings else []
+    
+    async def health(self) -> bool:
+        """Check if the embeddings service is healthy."""
+        try:
+            response = await self._client.get("/health")
+            return response.status_code == 200
+        except Exception:
+            return False