Introduce batching to text embedding service to speed things up on ol…

…der machines

services/backend/text_embedding_api/text_embedding_service.py

@@ -1,13 +1,44 @@
 # Standard Library
 import os
-from typing import List
+import logging
+from typing import List, Iterator
 
 # Third Party
 from sentence_transformers import SentenceTransformer
 
+# Initialize logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Configuration
 default_model = "sentence-transformers/all-MiniLM-L6-v2"
 model_name = os.getenv("MODEL_NAME", default_model)
+
+# Load model
 model = SentenceTransformer(model_name)
 
-def embed_multiple(texts: List[str]) -> List[List[float]]:
-    return model.encode(texts).tolist()
+def batch_iterable(iterable: List[str], batch_size: int) -> Iterator[List[str]]:
+    """
+    Yields batches of the iterable with a given size.
+    """
+    length = len(iterable)
+    for idx in range(0, length, batch_size):
+        yield iterable[idx:min(idx + batch_size, length)]
+
+def embed_multiple(texts: List[str], batch_size: int = 8) -> List[List[float]]:
+    """
+    Encodes texts into embeddings, processing in batches.
+    """
+    all_embeddings = []
+    for i, batch in enumerate(batch_iterable(texts, batch_size), start=1):
+        logging.info(f'Encoding batch {i}/{(len(texts) - 1) // batch_size + 1}')
+        try:
+            batch_embeddings = model.encode(batch).tolist()
+            all_embeddings.extend(batch_embeddings)
+        except Exception as e:
+            logging.error(f'Error encoding batch {i}: {e}')
+            continue
+    return all_embeddings
+
+# Example usage
+# texts = ["This is a sentence", "Here is another one", "..."]  # Add your texts here
+# embeddings = embed_multiple(texts, batch_size=64)  # Adjust batch_size based on your system's capability