multivector support

python/starpoint/db.py

@@ -192,7 +192,7 @@ def query(
             sql=sql,
             collection_id=collection_id,
             collection_name=collection_name,
-            query_embedding=query_embedding,
+            query_embeddings=query_embedding,
             params=params,
             text_search_query=text_search_query,
             text_search_weight=text_search_weight,

python/starpoint/reader.py

@@ -48,7 +48,7 @@ def query(
         sql: Optional[str] = None,
         collection_id: Optional[str] = None,
         collection_name: Optional[str] = None,
-        query_embedding: Optional[List[float]] = None,
+        query_embeddings: Optional[Dict[Any, Any]] = None,
         params: Optional[List[Any]] = None,
         text_search_query: Optional[List[str]] = None,
         text_search_weight: Optional[float] = None,
@@ -91,7 +91,7 @@ def query(
         request_data = dict(
             collection_id=collection_id,
             collection_name=collection_name,
-            query_embedding=query_embedding,
+            query_embeddings=query_embeddings,
             sql=sql,
             params=params,
             text_search_query=text_search_query,

python/starpoint/writer.py

@@ -100,51 +100,6 @@ def delete(
             return {}
         return response.json()
 
-    def column_delete(
-        self,
-        embeddings: List[List[float]],
-        document_metadatas: List[Dict[Any, Any]],
-        collection_id: Optional[str] = None,
-        collection_name: Optional[str] = None,
-    ) -> Dict[Any, Any]:
-        """Deletes documents from an existing collection by embedding and document metadata arrays.
-        The arrays are zipped together and updates the document in the order of the two arrays.
-
-        Args:
-            embeddings: A list of embeddings.
-                Order of the embeddings should match the document_metadatas.
-            document_metadatas: A list of metadata to be associated with embeddings.
-                Order of these metadatas should match the embeddings.
-            collection_id: The collection's id where the documents will be deleted.
-                This or the `collection_name` needs to be provided.
-            collection_name: The collection's name where the documents will be deleted.
-                This or the `collection_id` needs to be provided.
-
-        Returns:
-            dict: delete response json
-
-        Raises:
-            ValueError: If neither collection id and collection name are provided.
-            ValueError: If both collection id and collection name are provided.
-            requests.exceptions.SSLError: Failure likely due to network issues.
-        """
-        if len(embeddings) != len(document_metadatas):
-            LOGGER.warning(EMBEDDING_METADATA_LENGTH_MISMATCH_WARNING)
-
-        documents = [
-            {
-                "embedding": embedding,
-                "metadata": document_metadata,
-            }
-            for embedding, document_metadata in zip(embeddings, document_metadatas)
-        ]
-
-        return self.delete(
-            documents=documents,
-            collection_id=collection_id,
-            collection_name=collection_name,
-        )
-
     def insert(
         self,
         documents: List[Dict[Any, Any]],
@@ -214,7 +169,7 @@ def insert(
 
     def column_insert(
         self,
-        embeddings: List[List[float]],
+        embeddings: List[Dict[Any, Any]],
         document_metadatas: List[Dict[Any, Any]],
         collection_id: Optional[str] = None,
         collection_name: Optional[str] = None,
@@ -245,7 +200,7 @@ def column_insert(
 
         documents = [
             {
-                "embedding": embedding,
+                "embeddings": embedding,
                 "metadata": document_metadata,
             }
             for embedding, document_metadata in zip(embeddings, document_metadatas)
@@ -325,7 +280,7 @@ def update(
 
     def column_update(
         self,
-        embeddings: List[List[float]],
+        embeddings: List[Dict[Any, Any]],
         document_metadatas: List[Dict[Any, Any]],
         collection_id: Optional[str] = None,
         collection_name: Optional[str] = None,
@@ -356,7 +311,7 @@ def column_update(
 
         documents = [
             {
-                "embedding": embedding,
+                "embeddings": embedding,
                 "metadata": document_metadata,
             }
             for embedding, document_metadata in zip(embeddings, document_metadatas)