Add object-based embedding method

python/pyproject.toml

@@ -19,6 +19,7 @@ dependencies = [
   "requests~=2.31",
   "validators~=0.21",
   "pandas~=2.0",
+  "more-itertools~=10.1",
 ]
 
 [project.optional-dependencies]

python/starpoint/embedding.py

@@ -1,6 +1,7 @@
 import logging
+import more_itertools
 from enum import Enum
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, Hashable, List, Optional
 from uuid import UUID
 
 import requests
@@ -104,6 +105,50 @@ def embed_and_join_metadata_by_columns(
         ]
         return self.embed_items(text_embedding_items=text_embedding_items, model=model)
 
+    def embed_and_join_metadata(
+        self,
+        text_embedding_items: List[Dict],
+        embedding_key: Hashable,
+        model: EmbeddingModel,
+    ) -> Dict[str, List[Dict]]:
+        """Takes some texts and creates embeddings using a model in starpoint. Prefer using `embed_items`
+        instead, as mismatched `texts` and `metadatas` will output undesirable results.
+        Under the hood this is using `embed_items`.
+
+        Args:
+            text_embedding_items: List of dicts of data to create embeddings from.
+            embedding_key: the key in the embedding items to use to generate the embeddings against
+            model: An enum choice from EmbeddingModel.
+
+        Returns:
+            dict: Result with list of texts, metadata, and embeddings.
+
+        Raises:
+            requests.exceptions.SSLError: Failure likely due to network issues.
+        """
+        texts = list(map(lambda item: item.get(embedding_key), text_embedding_items))
+        if not texts:
+            raise ValueError(
+                "text_embedding_items received an empty list of list of empty items."
+            )
+        elif not all(texts):
+            unqualified_indices = list(
+                more_itertools.locate(texts, lambda x: x is None)
+            )
+            raise ValueError(
+                "The following indices had items that did not have the "
+                f"{embedding_key}:\n {unqualified_indices}"
+            )
+
+        # TODO: Figure out if we should do a deep copy here instead of editing the original dict
+        metadatas = list(
+            map(lambda item: item.pop(embedding_key), text_embedding_items)
+        )
+
+        return self.embed_and_join_metadata_by_columns(
+            texts=text_embedding_items, metadatas=metadatas, model=model
+        )
+
     def embed_items(
         self,
         text_embedding_items: List[Dict],
@@ -122,6 +167,7 @@ def embed_items(
         Raises:
             requests.exceptions.SSLError: Failure likely due to network issues.
         """
+
         request_data = dict(items=text_embedding_items, model=model.value)
         try:
             response = requests.post(