openai embeddings (#3488)

langchain/embeddings/openai.py

@@ -179,14 +179,20 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 )
                 batched_embeddings += [r["embedding"] for r in response["data"]]
 
-            results: List[List[List[float]]] = [[] for i in range(len(texts))]
-            lens: List[List[int]] = [[] for i in range(len(texts))]
+            results: List[List[List[float]]] = [[] for _ in range(len(texts))]
+            lens: List[List[int]] = [[] for _ in range(len(texts))]
             for i in range(len(indices)):
                 results[indices[i]].append(batched_embeddings[i])
                 lens[indices[i]].append(len(batched_embeddings[i]))
 
             for i in range(len(texts)):
-                average = np.average(results[i], axis=0, weights=lens[i])
+                _result = results[i]
+                if len(_result) == 0:
+                    average = embed_with_retry(self, input="", engine=self.deployment)[
+                        "data"
+                    ][0]["embedding"]
+                else:
+                    average = np.average(_result, axis=0, weights=lens[i])
                 embeddings[i] = (average / np.linalg.norm(average)).tolist()
 
             return embeddings

tests/integration_tests/embeddings/test_openai.py

@@ -1,4 +1,7 @@
 """Test openai embeddings."""
+import numpy as np
+import openai
+
 from langchain.embeddings.openai import OpenAIEmbeddings
 
 
@@ -29,3 +32,17 @@ def test_openai_embedding_query() -> None:
     embedding = OpenAIEmbeddings()
     output = embedding.embed_query(document)
     assert len(output) == 1536
+
+
+def test_openai_embedding_with_empty_string() -> None:
+    """Test openai embeddings with empty string."""
+    document = ["", "abc"]
+    embedding = OpenAIEmbeddings()
+    output = embedding.embed_documents(document)
+    assert len(output) == 2
+    assert len(output[0]) == 1536
+    expected_output = openai.Embedding.create(input="", model="text-embedding-ada-002")[
+        "data"
+    ][0]["embedding"]
+    assert np.allclose(output[0], expected_output)
+    assert len(output[1]) == 1536