fix: Address remaining Copilot review comments

- Add batch_size validation (must be >= 1)
- Handle OMIT sentinel properly in both v1 and v2 clients
- Remove images parameter from v2 embed_stream (text-only support)
- Document that embed_stream is for texts only, use embed() for images

All tests passing (5/6, 1 skipped requires API key)

Author: fede-kamel

src/cohere/base_client.py

@@ -1190,15 +1190,22 @@ def embed_stream(
             print(f"Embedding {embedding.index}: {embedding.embedding[:5]}...")
             # Process/save embedding immediately
         """
+        # Validate batch_size
+        if batch_size < 1:
+            raise ValueError("batch_size must be at least 1")
+
+        # Handle OMIT sentinel and empty texts
+        if texts is None or texts is OMIT:
+            return
         if not texts:
             return
-            
+
         from .streaming_utils import StreamingEmbedParser
-        
+
         # Process texts in batches
-        texts_list = list(texts) if texts else []
+        texts_list = list(texts)
         total_embeddings_yielded = 0
-        
+
         for batch_start in range(0, len(texts_list), batch_size):
             batch_end = min(batch_start + batch_size, len(texts_list))
             batch_texts = texts_list[batch_start:batch_end]

src/cohere/v2/client.py

@@ -498,7 +498,6 @@ def embed_stream(
         model: str,
         input_type: EmbedInputType,
         texts: typing.Optional[typing.Sequence[str]] = OMIT,
-        images: typing.Optional[typing.Sequence[str]] = OMIT,
         max_tokens: typing.Optional[int] = OMIT,
         output_dimension: typing.Optional[int] = OMIT,
         embedding_types: typing.Optional[typing.Sequence[EmbeddingType]] = OMIT,
@@ -508,11 +507,14 @@ def embed_stream(
     ) -> typing.Iterator[typing.Any]:  # Returns Iterator[StreamedEmbedding]
         """
         Memory-efficient streaming version of embed that yields embeddings one at a time.
-        
+
         This method processes texts in batches and yields individual embeddings as they are
         parsed from the response, without loading all embeddings into memory at once.
         Ideal for processing large datasets where memory usage is a concern.
 
+        Note: This method only supports text embeddings. For image embeddings, use the
+        regular embed() method.
+
         Parameters
         ----------
         model : str
@@ -570,25 +572,31 @@ def embed_stream(
             print(f"Embedding {embedding.index}: {embedding.embedding[:5]}...")
             # Process/save embedding immediately
         """
+        # Validate batch_size
+        if batch_size < 1:
+            raise ValueError("batch_size must be at least 1")
+
+        # Handle OMIT sentinel and empty texts
+        if texts is None or texts is OMIT:
+            return
         if not texts:
             return
-            
+
         from ..streaming_utils import StreamingEmbedParser
-        
+
         # Process texts in batches
-        texts_list = list(texts) if texts else []
+        texts_list = list(texts)
         total_embeddings_yielded = 0
-        
+
         for batch_start in range(0, len(texts_list), batch_size):
             batch_end = min(batch_start + batch_size, len(texts_list))
             batch_texts = texts_list[batch_start:batch_end]
-            
+
             # Get response for this batch
             response = self._raw_client.embed(
                 model=model,
                 input_type=input_type,
                 texts=batch_texts,
-                images=images if batch_start == 0 else None,  # Only include images in first batch
                 max_tokens=max_tokens,
                 output_dimension=output_dimension,
                 embedding_types=embedding_types,