From 90fd840fb17e6733aa240af51828c99695c5cd53 Mon Sep 17 00:00:00 2001
From: Ofer Mendelevitch <ofer@vectara.com>
Date: Sat, 19 Aug 2023 16:51:53 -0700
Subject: [PATCH] fixed formatting

---
 libs/langchain/langchain/vectorstores/vectara.py          | 8 ++++----
 .../tests/integration_tests/vectorstores/test_vectara.py  | 8 +++++---
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/libs/langchain/langchain/vectorstores/vectara.py b/libs/langchain/langchain/vectorstores/vectara.py
index f1c2bc6ca9e..cd8ee9c9fad 100644
--- a/libs/langchain/langchain/vectorstores/vectara.py
+++ b/libs/langchain/langchain/vectorstores/vectara.py
@@ -311,9 +311,9 @@ class Vectara(VectorStore):
 
         metadatas = []
         for x in responses:
-            md = { m["name"]: m["value"] for m in x["metadata"] }
-            doc_num = x['documentIndex']
-            doc_md = { m["name"]: m["value"] for m in documents[doc_num]['metadata'] }
+            md = {m["name"]: m["value"] for m in x["metadata"]}
+            doc_num = x["documentIndex"]
+            doc_md = {m["name"]: m["value"] for m in documents[doc_num]["metadata"]}
             md.update(doc_md)
             metadatas.append(md)
 
@@ -325,7 +325,7 @@ class Vectara(VectorStore):
                 ),
                 x["score"],
             )
-            for x,md in zip(responses,metadatas)
+            for x, md in zip(responses, metadatas)
         ]
 
         return docs
diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_vectara.py b/libs/langchain/tests/integration_tests/vectorstores/test_vectara.py
index 3b2decfc2f9..5ed1d17343a 100644
--- a/libs/langchain/tests/integration_tests/vectorstores/test_vectara.py
+++ b/libs/langchain/tests/integration_tests/vectorstores/test_vectara.py
@@ -14,6 +14,7 @@ from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
 #    VECTARA_API_KEY, VECTARA_CORPUS_ID and VECTARA_CUSTOMER_ID
 #
 
+
 def get_abbr(s: str) -> str:
     words = s.split(" ")  # Split the string into words
     first_letters = [word[0] for word in words]  # Extract the first letter of each word
@@ -51,9 +52,9 @@ def test_vectara_add_documents() -> None:
     )
     assert len(output1) == 2
     assert output1[0].page_content == "large language model"
-    assert output1[0].metadata['abbr'] == "llm"
+    assert output1[0].metadata["abbr"] == "llm"
     assert output1[1].page_content == "information retrieval"
-    assert output1[1].metadata['abbr'] == "ir"
+    assert output1[1].metadata["abbr"] == "ir"
 
     # test with metadata filter (doc level)
     # since the query does not match test_num=1 directly we get RAG as the matching result
@@ -65,11 +66,12 @@ def test_vectara_add_documents() -> None:
     )
     assert len(output2) == 1
     assert output2[0].page_content == "retrieval augmented generation"
-    assert output2[0].metadata['abbr'] == "rag"
+    assert output2[0].metadata["abbr"] == "rag"
 
     for doc_id in doc_ids:
         docsearch._delete_doc(doc_id)
 
+
 def test_vectara_from_files() -> None:
     """Test end to end construction and search."""