From 383c67c1b259ddd0faada1469abdfa7b04cfe481 Mon Sep 17 00:00:00 2001
From: Hasegawa Yuya <52068175+Hase-U@users.noreply.github.com>
Date: Fri, 17 Feb 2023 17:53:16 +0900
Subject: [PATCH] Fix Issue #1100 (#1101)

https://github.com/hwchase17/langchain/issues/1100
When faiss data and doc.index are created in past versions, error occurs
that say there was no attribute. So I put hasattr in the check as a
simple solution.

However, increasing the number of such checks is not good for
conservatism, so I think there is a better solution.


Also, the code for the batch process was left out, so I put it back in.
---
 langchain/embeddings/openai.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/langchain/embeddings/openai.py b/langchain/embeddings/openai.py
index fbf6a1bc..3b8fa555 100644
--- a/langchain/embeddings/openai.py
+++ b/langchain/embeddings/openai.py
@@ -147,11 +147,13 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 texts, engine=self.document_model_name, chunk_size=chunk_size
             )
         else:
-            responses = [
-                self._embedding_func(text, engine=self.document_model_name)
-                for text in texts
-            ]
-            return responses
+            results = []
+            for i in range(0, len(texts), chunk_size):
+                response = self.client.create(
+                    input=texts[i : i + chunk_size], engine=self.document_model_name
+                )
+                results += [r["embedding"] for r in response["data"]]
+            return results
 
     def embed_query(self, text: str) -> List[float]:
         """Call out to OpenAI's embedding endpoint for embedding query text.