fix lint

deepset-ai · Dec 3, 2024 · 0da8899 · 0da8899
1 parent 2b3fad2
commit 0da8899
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Showing 2 changed files with 6 additions and 4 deletions.
diff --git a/integrations/mongodb_atlas/examples/embedding_retrieval.py b/integrations/mongodb_atlas/examples/embedding_retrieval.py
@@ -19,7 +19,8 @@
 
 # To use the MongoDBAtlasDocumentStore, you must have a running MongoDB Atlas database.
 # For details, see https://www.mongodb.com/docs/atlas/getting-started/
-# NOTE: you need to create manually the vector search index and the full text search index in your MongoDB Atlas database.
+# NOTE: you need to create manually the vector search index and the full text search
+#       index in your MongoDB Atlas database.
 
 # Once your database is set, set the environment variable `MONGO_CONNECTION_STRING`
 # with the connection string to your MongoDB Atlas database.
@@ -40,7 +41,7 @@
 # Create the indexing Pipeline and index some documents
 file_paths = glob.glob("neural-search-pills/pills/*.md")
 
-print(f"Creating indexing pipeline")
+print("Creating indexing pipeline")
 indexing = Pipeline()
 indexing.add_component("converter", MarkdownToDocument())
 indexing.add_component("splitter", DocumentSplitter(split_by="sentence", split_length=2))

diff --git a/integrations/mongodb_atlas/examples/hybrid_retrieval.py b/integrations/mongodb_atlas/examples/hybrid_retrieval.py
@@ -23,7 +23,8 @@
 
 # To use the MongoDBAtlasDocumentStore, you must have a running MongoDB Atlas database.
 # For details, see https://www.mongodb.com/docs/atlas/getting-started/
-# NOTE: you need to create manually the vector search index and the full text search index in your MongoDB Atlas database.
+# NOTE: you need to create manually the vector search index and the full text search
+#       index in your MongoDB Atlas database.
 
 # Once your database is set, set the environment variable `MONGO_CONNECTION_STRING`
 # with the connection string to your MongoDB Atlas database.
@@ -43,7 +44,7 @@
 print(f"Cleaning up collection {document_store.collection_name}")
 document_store.collection.delete_many({})
 
-print(f"Creating indexing pipeline with")
+print("Creating indexing pipeline")
 indexing = Pipeline()
 indexing.add_component("converter", MarkdownToDocument())
 indexing.add_component("splitter", DocumentSplitter(split_by="sentence", split_length=2))