Handle max batch size for embeddings. (#83)

google/generativeai/text.py

@@ -15,8 +15,9 @@
 from __future__ import annotations
 
 import dataclasses
-from collections.abc import Sequence
-from typing import Iterable, overload
+from collections.abc import Iterable, Sequence
+import itertools
+from typing import Iterable, overload, TypeVar
 
 import google.ai.generativelanguage as glm
 
@@ -28,6 +29,26 @@
 from google.generativeai.types import safety_types
 
 DEFAULT_TEXT_MODEL = "models/text-bison-001"
+EMBEDDING_MAX_BATCH_SIZE = 100
+
+try:
+    # python 3.12+
+    _batched = itertools.batched  # type: ignore
+except AttributeError:
+    T = TypeVar("T")
+
+    def _batched(iterable: Iterable[T], n: int) -> Iterable[list[T]]:
+        if n < 1:
+            raise ValueError(f"Batch size `n` must be >1, got: {n}")
+        batch = []
+        for item in iterable:
+            batch.append(item)
+            if len(batch) == n:
+                yield batch
+                batch = []
+
+        if batch:
+            yield batch
 
 
 def _make_text_prompt(prompt: str | dict[str, str]) -> glm.TextPrompt:
@@ -282,9 +303,13 @@ def generate_embeddings(
         embedding_dict = type(embedding_response).to_dict(embedding_response)
         embedding_dict["embedding"] = embedding_dict["embedding"]["value"]
     else:
-        embedding_request = glm.BatchEmbedTextRequest(model=model, texts=text)
-        embedding_response = client.batch_embed_text(embedding_request)
-        embedding_dict = type(embedding_response).to_dict(embedding_response)
-        embedding_dict["embedding"] = [e["value"] for e in embedding_dict["embeddings"]]
+        result = {"embedding": []}
+        for batch in _batched(text, EMBEDDING_MAX_BATCH_SIZE):
+            # TODO(markdaoust): This could use an option for returning an iterator or wait-bar.
+            embedding_request = glm.BatchEmbedTextRequest(model=model, texts=batch)
+            embedding_response = client.batch_embed_text(embedding_request)
+            embedding_dict = type(embedding_response).to_dict(embedding_response)
+            result["embedding"].extend(e["value"] for e in embedding_dict["embeddings"])
+        return result
 
     return embedding_dict

tests/test_text.py

@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import copy
+import math
 import unittest
 import unittest.mock as mock
 
@@ -61,7 +62,10 @@ def batch_embed_text(
             request: glm.EmbedTextRequest,
         ) -> glm.EmbedTextResponse:
             self.observed_requests.append(request)
-            return self.responses["batch_embed_text"]
+
+            return glm.BatchEmbedTextResponse(
+                embeddings=[glm.Embedding(value=[1, 2, 3])] * len(request.texts)
+            )
 
         @add_client_method
         def count_text_tokens(
@@ -120,27 +124,46 @@ def test_generate_embeddings(self, model, text):
     @parameterized.named_parameters(
         [
             dict(
-                testcase_name="basic_model",
+                testcase_name="small-2",
                 model="models/chat-lamda-001",
                 text=["Who are you?", "Who am I?"],
-            )
+            ),
+            dict(
+                testcase_name="even-batch",
+                model="models/chat-lamda-001",
+                text=["Who are you?"] * 100,
+            ),
+            dict(
+                testcase_name="even-batch-plus-one",
+                model="models/chat-lamda-001",
+                text=["Who are you?"] * 101,
+            ),
+            dict(
+                testcase_name="odd-batch",
+                model="models/chat-lamda-001",
+                text=["Who are you?"] * 237,
+            ),
         ]
     )
     def test_generate_embeddings_batch(self, model, text):
-        self.responses["batch_embed_text"] = glm.BatchEmbedTextResponse(
-            embeddings=[
-                glm.Embedding(value=[1, 2, 3]),
-                glm.Embedding(value=[4, 5, 6]),
-            ]
-        )
-
         emb = text_service.generate_embeddings(model=model, text=text)
 
         self.assertIsInstance(emb, dict)
-        self.assertEqual(
-            self.observed_requests[-1], glm.BatchEmbedTextRequest(model=model, texts=text)
-        )
+
+        # Check first and last requests.
+        self.assertEqual(self.observed_requests[-1].model, model)
+        self.assertEqual(self.observed_requests[-1].texts[-1], text[-1])
+        self.assertEqual(self.observed_requests[0].texts[0], text[0])
+
+        # Check that the list has the right length.
         self.assertIsInstance(emb["embedding"][0], list)
+        self.assertLen(emb["embedding"], len(text))
+
+        # Check that the right number of requests were sent.
+        self.assertLen(
+            self.observed_requests,
+            math.ceil(len(text) / text_service.EMBEDDING_MAX_BATCH_SIZE),
+        )
 
     @parameterized.named_parameters(
         [