Batch embeddings (#668)

Base Service (trustgraph-base/trustgraph/base/embeddings_service.py):
- Changed on_request to use request.texts

FastEmbed Processor
(trustgraph-flow/trustgraph/embeddings/fastembed/processor.py):
- on_embeddings(texts, model=None) now processes full batch efficiently
- Returns [[v.tolist()] for v in vecs] - list of vector sets

Ollama Processor (trustgraph-flow/trustgraph/embeddings/ollama/processor.py):
- on_embeddings(texts, model=None) passes list directly to Ollama
- Returns [[embedding] for embedding in embeds.embeddings]

EmbeddingsClient (trustgraph-base/trustgraph/base/embeddings_client.py):
- embed(texts, timeout=300) accepts list of texts

Tests Updated:
- test_fastembed_dynamic_model.py - 4 tests updated for new interface
- test_ollama_dynamic_model.py - 4 tests updated for new interface

Updated CLI, SDK and APIs

trustgraph-base/trustgraph/api/socket_client.py

@@ -712,27 +712,27 @@ class SocketFlowInstance:
 
         return self.client._send_request_sync("document-embeddings", self.flow_id, request, False)
 
-    def embeddings(self, text: str, **kwargs: Any) -> Dict[str, Any]:
+    def embeddings(self, texts: list, **kwargs: Any) -> Dict[str, Any]:
         """
-        Generate vector embeddings for text.
+        Generate vector embeddings for one or more texts.
 
         Args:
-            text: Input text to embed
+            texts: List of input texts to embed
             **kwargs: Additional parameters passed to the service
 
         Returns:
-            dict: Response containing vectors
+            dict: Response containing vectors (one set per input text)
 
         Example:
             ```python
             socket = api.socket()
             flow = socket.flow("default")
 
-            result = flow.embeddings("quantum computing")
+            result = flow.embeddings(["quantum computing"])
             vectors = result.get("vectors", [])
             ```
         """
-        request = {"text": text}
+        request = {"texts": texts}
         request.update(kwargs)
 
         return self.client._send_request_sync("embeddings", self.flow_id, request, False)