openai: embeddings: supported chunk_size when check_embedding_ctx_length is disabled

libs/partners/openai/langchain_openai/embeddings/base.py

@@ -254,14 +254,14 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
     retry_max_seconds: int = 20
     """Max number of seconds to wait between retries"""
     http_client: Union[Any, None] = None
-    """Optional httpx.Client. Only used for sync invocations. Must specify 
+    """Optional httpx.Client. Only used for sync invocations. Must specify
         http_async_client as well if you'd like a custom client for async invocations.
     """
     http_async_client: Union[Any, None] = None
-    """Optional httpx.AsyncClient. Only used for async invocations. Must specify 
+    """Optional httpx.AsyncClient. Only used for async invocations. Must specify
         http_client as well if you'd like a custom client for sync invocations."""
     check_embedding_ctx_length: bool = True
-    """Whether to check the token length of inputs and automatically split inputs 
+    """Whether to check the token length of inputs and automatically split inputs
         longer than embedding_ctx_length."""
 
     model_config = ConfigDict(
@@ -558,7 +558,7 @@ async def empty_embedding() -> List[float]:
         return [e if e is not None else await empty_embedding() for e in embeddings]
 
     def embed_documents(
-        self, texts: List[str], chunk_size: Optional[int] = 0
+        self, texts: List[str], chunk_size: int | None = None
     ) -> List[List[float]]:
         """Call out to OpenAI's embedding endpoint for embedding search docs.
 
@@ -570,10 +570,13 @@ def embed_documents(
         Returns:
             List of embeddings, one for each text.
         """
+        chunk_size_ = chunk_size or self.chunk_size
         if not self.check_embedding_ctx_length:
             embeddings: List[List[float]] = []
-            for text in texts:
-                response = self.client.create(input=text, **self._invocation_params)
+            for i in range(0, len(texts), self.chunk_size):
+                response = self.client.create(
+                    input=texts[i : i + chunk_size_], **self._invocation_params
+                )
                 if not isinstance(response, dict):
                     response = response.dict()
                 embeddings.extend(r["embedding"] for r in response["data"])
@@ -585,7 +588,7 @@ def embed_documents(
         return self._get_len_safe_embeddings(texts, engine=engine)
 
     async def aembed_documents(
-        self, texts: List[str], chunk_size: Optional[int] = 0
+        self, texts: List[str], chunk_size: int | None = None
     ) -> List[List[float]]:
         """Call out to OpenAI's embedding endpoint async for embedding search docs.
 
@@ -597,11 +600,12 @@ async def aembed_documents(
         Returns:
             List of embeddings, one for each text.
         """
+        chunk_size_ = chunk_size or self.chunk_size
         if not self.check_embedding_ctx_length:
             embeddings: List[List[float]] = []
-            for text in texts:
+            for i in range(0, len(texts), chunk_size_):
                 response = await self.async_client.create(
-                    input=text, **self._invocation_params
+                    input=texts[i : i + chunk_size_], **self._invocation_params
                 )
                 if not isinstance(response, dict):
                     response = response.dict()

libs/partners/openai/tests/integration_tests/embeddings/test_base.py

@@ -61,3 +61,12 @@ async def test_langchain_openai_embeddings_equivalent_to_raw_async() -> None:
         .embedding
     )
     assert np.isclose(lc_output, direct_output).all()
+
+
+def test_langchain_openai_embeddings_dimensions_large_num() -> None:
+    """Test openai embeddings."""
+    documents = [f"foo bar {i}" for i in range(2000)]
+    embedding = OpenAIEmbeddings(model="text-embedding-3-small", dimensions=128)
+    output = embedding.embed_documents(documents)
+    assert len(output) == 2000
+    assert len(output[0]) == 128