[ENH] Python client cleanup: export hosted ef from utils, fix sparse auto-embed

chromadb/api/models/CollectionCommon.py

@@ -584,15 +584,19 @@ def _apply_sparse_embeddings_to_metadatas(
         metadatas: Optional[List[Metadata]],
         documents: Optional[List[Document]] = None,
     ) -> Optional[List[Metadata]]:
-        if metadatas is None:
-            return None
-
         sparse_targets = self._get_sparse_embedding_targets()
         if not sparse_targets:
             return metadatas
 
+        # If no metadatas provided, create empty dicts based on documents length
+        if metadatas is None:
+            if documents is None:
+                return None
+            metadatas = [{} for _ in range(len(documents))]
+
+        # Create copies, converting None to empty dict
         updated_metadatas: List[Dict[str, Any]] = [
-            dict(metadata) for metadata in metadatas
+            dict(metadata) if metadata is not None else {} for metadata in metadatas
         ]
 
         documents_list = list(documents) if documents is not None else None
@@ -607,26 +611,60 @@ def _apply_sparse_embeddings_to_metadatas(
                 embedding_func = cast(SparseEmbeddingFunction[Any], embedding_func)
             validate_sparse_embedding_function(embedding_func)
 
+            # Initialize collection lists for batch processing
             inputs: List[str] = []
             positions: List[int] = []
 
+            # Handle special case: source_key is "#document"
+            if source_key == DOCUMENT_KEY:
+                if documents_list is None:
+                    continue
+
+                # Collect documents that need embedding
+                for idx, metadata in enumerate(updated_metadatas):
+                    # Skip if target already exists in metadata
+                    if target_key in metadata:
+                        continue
+
+                    # Get document at this position
+                    if idx < len(documents_list):
+                        doc = documents_list[idx]
+                        if isinstance(doc, str):
+                            inputs.append(doc)
+                            positions.append(idx)
+
+                # Generate embeddings for all collected documents
+                if len(inputs) == 0:
+                    continue
+
+                sparse_embeddings = self._sparse_embed(
+                    input=inputs,
+                    sparse_embedding_function=embedding_func,
+                )
+
+                if len(sparse_embeddings) != len(positions):
+                    raise ValueError(
+                        "Sparse embedding function returned unexpected number of embeddings."
+                    )
+
+                for position, embedding in zip(positions, sparse_embeddings):
+                    updated_metadatas[position][target_key] = embedding
+
+                continue  # Skip the metadata-based logic below
+
+            # Handle normal case: source_key is a metadata field
             for idx, metadata in enumerate(updated_metadatas):
                 if target_key in metadata:
                     continue
 
-                if source_key == DOCUMENT_KEY:
-                    source_value = None
-                    if documents_list is not None and idx < len(documents_list):
-                        source_value = documents_list[idx]
-                else:
-                    source_value = metadata.get(source_key)
+                source_value = metadata.get(source_key)
                 if not isinstance(source_value, str):
                     continue
 
                 inputs.append(source_value)
                 positions.append(idx)
 
-            if not inputs:
+            if len(inputs) == 0:
                 continue
 
             sparse_embeddings = self._sparse_embed(
@@ -642,8 +680,13 @@ def _apply_sparse_embeddings_to_metadatas(
             for position, embedding in zip(positions, sparse_embeddings):
                 updated_metadatas[position][target_key] = embedding
 
-        validate_metadatas(cast(List[Metadata], updated_metadatas))
-        return cast(List[Metadata], updated_metadatas)
+        # Convert empty dicts back to None, validation requires non-empty dicts or None
+        result_metadatas: List[Optional[Metadata]] = [
+            metadata if metadata else None for metadata in updated_metadatas
+        ]
+
+        validate_metadatas(cast(List[Metadata], result_metadatas))
+        return cast(List[Metadata], result_metadatas)
 
     def _embed_record_set(
         self,