Reduce generation of temporary objects (#7950)

Just a tiny change to use `list.append(...)` and `list.extend(...)`
instead of `list += [...]` so that no unnecessary temporary lists are
created.

Since its a tiny miscellaneous thing I guess @baskaryan is the
maintainer to tag?

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

libs/langchain/langchain/embeddings/openai.py

@@ -338,10 +338,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 disallowed_special=self.disallowed_special,
             )
             for j in range(0, len(token), self.embedding_ctx_length):
-                tokens += [token[j : j + self.embedding_ctx_length]]
-                indices += [i]
+                tokens.append(token[j : j + self.embedding_ctx_length])
+                indices.append(i)
 
-        batched_embeddings = []
+        batched_embeddings: List[List[float]] = []
         _chunk_size = chunk_size or self.chunk_size
 
         if self.show_progress_bar:
@@ -360,7 +360,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 input=tokens[i : i + _chunk_size],
                 **self._invocation_params,
             )
-            batched_embeddings += [r["embedding"] for r in response["data"]]
+            batched_embeddings.extend(r["embedding"] for r in response["data"])
 
         results: List[List[List[float]]] = [[] for _ in range(len(texts))]
         num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
@@ -419,10 +419,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 disallowed_special=self.disallowed_special,
             )
             for j in range(0, len(token), self.embedding_ctx_length):
-                tokens += [token[j : j + self.embedding_ctx_length]]
-                indices += [i]
+                tokens.append(token[j : j + self.embedding_ctx_length])
+                indices.append(i)
 
-        batched_embeddings = []
+        batched_embeddings: List[List[float]] = []
         _chunk_size = chunk_size or self.chunk_size
         for i in range(0, len(tokens), _chunk_size):
             response = await async_embed_with_retry(
@@ -430,7 +430,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 input=tokens[i : i + _chunk_size],
                 **self._invocation_params,
             )
-            batched_embeddings += [r["embedding"] for r in response["data"]]
+            batched_embeddings.extend(r["embedding"] for r in response["data"])
 
         results: List[List[List[float]]] = [[] for _ in range(len(texts))]
         num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]