infra: rm unused # noqa violations (#22049)

Updating #21137

templates/anthropic-iterative-search/main.py

@@ -5,7 +5,7 @@ if __name__ == "__main__":
         "Which movie came out first: Oppenheimer, or "
         "Are You There God It's Me Margaret?"
     )
-    print(  # noqa: T201
+    print(
         final_chain.with_config(configurable={"chain": "retrieve"}).invoke(
             {"query": query}
         )

templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py

@@ -42,7 +42,7 @@ retriever = vector_store.as_retriever(search_kwargs={"k": 3})
 # Please remove this and/or adapt to your use case!
 inserted_lines = populate(vector_store)
 if inserted_lines:
-    print(f"Done ({inserted_lines} lines inserted).")  # noqa: T201
+    print(f"Done ({inserted_lines} lines inserted).")
 
 entomology_template = """
 You are an expert entomologist, tasked with answering enthusiast biologists' questions.

templates/csv-agent/main.py

@@ -2,4 +2,4 @@ from csv_agent.agent import agent_executor
 
 if __name__ == "__main__":
     question = "who was in cabin c28?"
-    print(agent_executor.invoke({"input": question}))  # noqa: T201
+    print(agent_executor.invoke({"input": question}))

templates/elastic-query-generator/main.py

@@ -1,4 +1,4 @@
 from elastic_query_generator.chain import chain
 
 if __name__ == "__main__":
-    print(chain.invoke({"input": "how many customers named Carol"}))  # noqa: T201
+    print(chain.invoke({"input": "how many customers named Carol"}))

templates/neo4j-advanced-rag/main.py

@@ -2,7 +2,7 @@ from neo4j_advanced_rag.chain import chain
 
 if __name__ == "__main__":
     original_query = "What is the plot of the Dune?"
-    print(  # noqa: T201
+    print(
         chain.invoke(
             {"question": original_query},
             {"configurable": {"strategy": "parent_strategy"}},

templates/neo4j-cypher-ft/main.py

@@ -2,4 +2,4 @@ from neo4j_cypher_ft.chain import chain
 
 if __name__ == "__main__":
     original_query = "Did tom cruis act in top gun?"
-    print(chain.invoke({"question": original_query}))  # noqa: T201
+    print(chain.invoke({"question": original_query}))

templates/neo4j-cypher-memory/main.py

@@ -2,7 +2,7 @@ from neo4j_cypher_memory.chain import chain
 
 if __name__ == "__main__":
     original_query = "Who played in Top Gun?"
-    print(  # noqa: T201
+    print(
         chain.invoke(
             {
                 "question": original_query,
@@ -12,7 +12,7 @@ if __name__ == "__main__":
         )
     )
     follow_up_query = "Did they play in any other movies?"
-    print(  # noqa: T201
+    print(
         chain.invoke(
             {
                 "question": follow_up_query,

templates/neo4j-cypher/main.py

@@ -2,4 +2,4 @@ from neo4j_cypher.chain import chain
 
 if __name__ == "__main__":
     original_query = "Who played in Top Gun?"
-    print(chain.invoke({"question": original_query}))  # noqa: T201
+    print(chain.invoke({"question": original_query}))

templates/neo4j-generation/main.py

@@ -4,7 +4,7 @@ if __name__ == "__main__":
     text = "Harrison works at LangChain, which is located in San Francisco"
     allowed_nodes = ["Person", "Organization", "Location"]
     allowed_relationships = ["WORKS_AT", "LOCATED_IN"]
-    print(  # noqa: T201
+    print(
         chain(
             text,
             allowed_nodes=allowed_nodes,

templates/neo4j-parent/main.py

@@ -2,4 +2,4 @@ from neo4j_parent.chain import chain
 
 if __name__ == "__main__":
     original_query = "What is the plot of the Dune?"
-    print(chain.invoke(original_query))  # noqa: T201
+    print(chain.invoke(original_query))

templates/neo4j-semantic-layer/main.py

@@ -11,7 +11,7 @@ if __name__ == "__main__":
             "\n\n1. John Travolta\n2. John McDonough",
         )
     ]
-    print(agent_executor.invoke({"input": original_query}))  # noqa: T201
-    print(  # noqa: T201
+    print(agent_executor.invoke({"input": original_query}))
+    print(
         agent_executor.invoke({"input": followup_query, "chat_history": chat_history})
     )

templates/neo4j-semantic-layer/neo4j_semantic_layer/memory_tool.py

@@ -34,7 +34,7 @@ def store_movie_rating(movie: str, rating: int):
     try:
         return response[0]["response"]
     except Exception as e:
-        print(e)  # noqa: T201
+        print(e)
         return "Something went wrong"
 
 

templates/neo4j-semantic-ollama/main.py

@@ -11,7 +11,7 @@ if __name__ == "__main__":
             "\n\n1. John Travolta\n2. John McDonough",
         )
     ]
-    print(agent_executor.invoke({"input": original_query}))  # noqa: T201
-    print(  # noqa: T201
+    print(agent_executor.invoke({"input": original_query}))
+    print(
         agent_executor.invoke({"input": followup_query, "chat_history": chat_history})
     )

templates/neo4j-semantic-ollama/neo4j_semantic_ollama/memory_tool.py

@@ -35,7 +35,7 @@ def store_movie_rating(movie: str, rating: int):
     try:
         return response[0]["response"]
     except Exception as e:
-        print(e)  # noqa: T201
+        print(e)
         return "Something went wrong"
 
 

templates/neo4j-vector-memory/main.py

@@ -4,13 +4,13 @@ if __name__ == "__main__":
     user_id = "user_id_1"
     session_id = "session_id_1"
     original_query = "What is the plot of the Dune?"
-    print(  # noqa: T201
+    print(
         chain.invoke(
             {"question": original_query, "user_id": user_id, "session_id": session_id}
         )
     )
     follow_up_query = "Tell me more about Leto"
-    print(  # noqa: T201
+    print(
         chain.invoke(
             {"question": follow_up_query, "user_id": user_id, "session_id": session_id}
         )

templates/nvidia-rag-canonical/ingest.py

@@ -7,7 +7,7 @@ from langchain_nvidia_aiplay import NVIDIAEmbeddings
 from langchain_text_splitters.character import CharacterTextSplitter
 
 if os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
-    print("Valid NVIDIA_API_KEY already in environment. Delete to reset")  # noqa: T201
+    print("Valid NVIDIA_API_KEY already in environment. Delete to reset")
 else:
     nvapi_key = getpass.getpass("NVAPI Key (starts with nvapi-): ")
     assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is not a valid key"

templates/nvidia-rag-canonical/nvidia_rag_canonical/chain.py

@@ -23,7 +23,7 @@ INGESTION_CHUNK_SIZE = 500
 INGESTION_CHUNK_OVERLAP = 0
 
 if os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
-    print("Valid NVIDIA_API_KEY already in environment. Delete to reset")  # noqa: T201
+    print("Valid NVIDIA_API_KEY already in environment. Delete to reset")
 else:
     nvapi_key = getpass.getpass("NVAPI Key (starts with nvapi-): ")
     assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is not a valid key"

templates/openai-functions-agent-gmail/main.py

@@ -6,4 +6,4 @@ if __name__ == "__main__":
         "First do background research on the sender and topics to make sure you"
         " understand the context, then write the draft."
     )
-    print(agent_executor.invoke({"input": question, "chat_history": []}))  # noqa: T201
+    print(agent_executor.invoke({"input": question, "chat_history": []}))

templates/openai-functions-agent/main.py

@@ -2,4 +2,4 @@ from openai_functions_agent.agent import agent_executor
 
 if __name__ == "__main__":
     question = "who won the womens world cup in 2023?"
-    print(agent_executor.invoke({"input": question, "chat_history": []}))  # noqa: T201
+    print(agent_executor.invoke({"input": question, "chat_history": []}))

templates/plate-chain/plate_chain/prompts.py

@@ -18,7 +18,7 @@ FULL_PROMPT = """# Context
 ```json
 {json_format}
 ```
-"""  # noqa: E50
+"""
 
 NUM_PLATES_PROMPT = """- There {num_plates_str} in this data."""
 ROWS_PROMPT = """- Each plate has {num_rows} rows."""

templates/propositional-retrieval/propositional_retrieval/ingest.py

@@ -68,7 +68,7 @@ if __name__ == "__main__":
     from langchain_text_splitters import RecursiveCharacterTextSplitter  # noqa
     from langchain_community.document_loaders.recursive_url_loader import (
         RecursiveUrlLoader,
-    )  # noqa
+    )
 
     # The attention is all you need paper
     # Could add more parsing here, as it's very raw.

templates/rag-astradb/astradb_entomology_rag/__init__.py

@@ -26,7 +26,7 @@ retriever = vector_store.as_retriever(search_kwargs={"k": 3})
 
 inserted_lines = populate(vector_store)
 if inserted_lines:
-    print(f"Done ({inserted_lines} lines inserted).")  # noqa: T201
+    print(f"Done ({inserted_lines} lines inserted).")
 
 entomology_template = """
 You are an expert entomologist, tasked with answering enthusiast biologists' questions.

templates/rag-astradb/main.py

@@ -2,4 +2,4 @@ from astradb_entomology_rag import chain
 
 if __name__ == "__main__":
     response = chain.invoke("Are there more coleoptera or bugs?")
-    print(response)  # noqa: T201
+    print(response)

templates/rag-aws-bedrock/main.py

@@ -3,4 +3,4 @@ from rag_aws_bedrock.chain import chain
 if __name__ == "__main__":
     query = "What is this data about?"
 
-    print(chain.invoke(query))  # noqa: T201
+    print(chain.invoke(query))

templates/rag-aws-kendra/main.py

@@ -3,4 +3,4 @@ from rag_aws_kendra.chain import chain
 if __name__ == "__main__":
     query = "Does Kendra support table extraction?"
 
-    print(chain.invoke(query))  # noqa: T201
+    print(chain.invoke(query))

templates/rag-chroma-multi-modal-multi-vector/ingest.py

@@ -66,7 +66,7 @@ def generate_img_summaries(img_base64_list):
             image_summaries.append(image_summarize(base64_image, prompt))
             processed_images.append(base64_image)
         except Exception as e:
-            print(f"Error with image {i+1}: {e}")  # noqa: T201
+            print(f"Error with image {i+1}: {e}")
 
     return image_summaries, processed_images
 
@@ -178,14 +178,14 @@ def create_multi_vector_retriever(
 # Load PDF
 doc_path = Path(__file__).parent / "docs/DDOG_Q3_earnings_deck.pdf"
 rel_doc_path = doc_path.relative_to(Path.cwd())
-print("Extract slides as images")  # noqa: T201
+print("Extract slides as images")
 pil_images = get_images_from_pdf(rel_doc_path)
 
 # Convert to b64
 images_base_64 = [convert_to_base64(i) for i in pil_images]
 
 # Image summaries
-print("Generate image summaries")  # noqa: T201
+print("Generate image summaries")
 image_summaries, images_base_64_processed = generate_img_summaries(images_base_64)
 
 # The vectorstore to use to index the images summaries

templates/rag-chroma-multi-modal/ingest.py

@@ -27,14 +27,14 @@ doc_path = Path(__file__).parent / "docs/DDOG_Q3_earnings_deck.pdf"
 img_dump_path = Path(__file__).parent / "docs/"
 rel_doc_path = doc_path.relative_to(Path.cwd())
 rel_img_dump_path = img_dump_path.relative_to(Path.cwd())
-print("pdf index")  # noqa: T201
+print("pdf index")
 pil_images = get_images_from_pdf(rel_doc_path, rel_img_dump_path)
-print("done")  # noqa: T201
+print("done")
 vectorstore = Path(__file__).parent / "chroma_db_multi_modal"
 re_vectorstore_path = vectorstore.relative_to(Path.cwd())
 
 # Load embedding function
-print("Loading embedding function")  # noqa: T201
+print("Loading embedding function")
 embedding = OpenCLIPEmbeddings(model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k")
 
 # Create chroma
@@ -54,5 +54,5 @@ image_uris = sorted(
 )
 
 # Add images
-print("Embedding images")  # noqa: T201
+print("Embedding images")
 vectorstore_mmembd.add_images(uris=image_uris)

templates/rag-elasticsearch/main.py

@@ -14,7 +14,7 @@ if __name__ == "__main__":
             "chat_history": [],
         }
     )
-    print(response)  # noqa: T201
+    print(response)
 
     follow_up_question = "What are their objectives?"
 
@@ -30,4 +30,4 @@ if __name__ == "__main__":
         }
     )
 
-    print(response)  # noqa: T201
+    print(response)

templates/rag-fusion/main.py

@@ -2,4 +2,4 @@ from rag_fusion.chain import chain
 
 if __name__ == "__main__":
     original_query = "impact of climate change"
-    print(chain.invoke(original_query))  # noqa: T201
+    print(chain.invoke(original_query))

templates/rag-gemini-multi-modal/ingest.py

@@ -27,14 +27,14 @@ doc_path = Path(__file__).parent / "docs/DDOG_Q3_earnings_deck.pdf"
 img_dump_path = Path(__file__).parent / "docs/"
 rel_doc_path = doc_path.relative_to(Path.cwd())
 rel_img_dump_path = img_dump_path.relative_to(Path.cwd())
-print("pdf index")  # noqa: T201
+print("pdf index")
 pil_images = get_images_from_pdf(rel_doc_path, rel_img_dump_path)
-print("done")  # noqa: T201
+print("done")
 vectorstore = Path(__file__).parent / "chroma_db_multi_modal"
 re_vectorstore_path = vectorstore.relative_to(Path.cwd())
 
 # Load embedding function
-print("Loading embedding function")  # noqa: T201
+print("Loading embedding function")
 embedding = OpenCLIPEmbeddings(model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k")
 
 # Create chroma
@@ -54,5 +54,5 @@ image_uris = sorted(
 )
 
 # Add images
-print("Embedding images")  # noqa: T201
+print("Embedding images")
 vectorstore_mmembd.add_images(uris=image_uris)

templates/rag-google-cloud-sensitive-data-protection/main.py

@@ -6,4 +6,4 @@ if __name__ == "__main__":
         "is 555-555-5555. And my email is lovely.pirate@gmail.com. Have a nice day.",
         "chat_history": [],
     }
-    print(chain.invoke(query))  # noqa: T201
+    print(chain.invoke(query))

templates/rag-google-cloud-vertexai-search/main.py

@@ -2,4 +2,4 @@ from rag_google_cloud_vertexai_search.chain import chain
 
 if __name__ == "__main__":
     query = "Who is the CEO of Google Cloud?"
-    print(chain.invoke(query))  # noqa: T201
+    print(chain.invoke(query))

templates/rag-multi-modal-local/ingest.py

@@ -20,7 +20,7 @@ vectorstore = Path(__file__).parent / "chroma_db_multi_modal"
 re_vectorstore_path = vectorstore.relative_to(Path.cwd())
 
 # Load embedding function
-print("Loading embedding function")  # noqa: T201
+print("Loading embedding function")
 embedding = OpenCLIPEmbeddings(model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k")
 
 # Create chroma
@@ -31,5 +31,5 @@ vectorstore_mmembd = Chroma(
 )
 
 # Add images
-print("Embedding images")  # noqa: T201
+print("Embedding images")
 vectorstore_mmembd.add_images(uris=image_uris)

templates/rag-multi-modal-mv-local/ingest.py

@@ -63,7 +63,7 @@ def generate_img_summaries(img_base64_list):
             image_summaries.append(image_summarize(base64_image, prompt))
             processed_images.append(base64_image)
         except Exception as e:
-            print(f"Error with image {i+1}: {e}")  # noqa: T201
+            print(f"Error with image {i+1}: {e}")
 
     return image_summaries, processed_images
 
@@ -162,14 +162,14 @@ def create_multi_vector_retriever(vectorstore, image_summaries, images):
 # Load images
 doc_path = Path(__file__).parent / "docs/"
 rel_doc_path = doc_path.relative_to(Path.cwd())
-print("Read images")  # noqa: T201
+print("Read images")
 pil_images = get_images(rel_doc_path)
 
 # Convert to b64
 images_base_64 = [convert_to_base64(i) for i in pil_images]
 
 # Image summaries
-print("Generate image summaries")  # noqa: T201
+print("Generate image summaries")
 image_summaries, images_base_64_processed = generate_img_summaries(images_base_64)
 
 # The vectorstore to use to index the images summaries

templates/rag-opensearch/dummy_index_setup.py

@@ -41,7 +41,7 @@ index_settings = {
 
 response = client.indices.create(index=OPENSEARCH_INDEX_NAME, body=index_settings)
 
-print(response)  # noqa: T201
+print(response)
 
 
 # Insert docs
@@ -57,4 +57,4 @@ for each in docs:
 
     response = client.index(index=OPENSEARCH_INDEX_NAME, body=document, refresh=True)
 
-    print(response)  # noqa: T201
+    print(response)

templates/rag-redis-multi-modal-multi-vector/ingest.py

@@ -62,7 +62,7 @@ def generate_img_summaries(img_base64_list):
             image_summaries.append(image_summarize(base64_image, prompt))
             processed_images.append(base64_image)
         except Exception as e:
-            print(f"Error with image {i+1}: {e}")  # noqa: T201
+            print(f"Error with image {i+1}: {e}")
 
     return image_summaries, processed_images
 
@@ -151,14 +151,14 @@ if __name__ == "__main__":
     doc_path = Path(__file__).parent / "docs/nvda-f3q24-investor-presentation-final.pdf"
     rel_doc_path = doc_path.relative_to(Path.cwd())
 
-    print("Extract slides as images")  # noqa: T201
+    print("Extract slides as images")
     pil_images = get_images_from_pdf(rel_doc_path)
 
     # Convert to b64
     images_base_64 = [convert_to_base64(i) for i in pil_images]
 
     # Generate image summaries
-    print("Generate image summaries")  # noqa: T201
+    print("Generate image summaries")
     image_summaries, images_base_64_processed = generate_img_summaries(images_base_64)
 
     # Create documents

templates/rag-redis/ingest.py

@@ -17,7 +17,7 @@ def ingest_documents():
     data_path = "data/"
     doc = [os.path.join(data_path, file) for file in os.listdir(data_path)][0]
 
-    print("Parsing 10k filing doc for NIKE", doc)  # noqa: T201
+    print("Parsing 10k filing doc for NIKE", doc)
 
     text_splitter = RecursiveCharacterTextSplitter(
         chunk_size=1500, chunk_overlap=100, add_start_index=True
@@ -25,7 +25,7 @@ def ingest_documents():
     loader = UnstructuredFileLoader(doc, mode="single", strategy="fast")
     chunks = loader.load_and_split(text_splitter)
 
-    print("Done preprocessing. Created", len(chunks), "chunks of the original pdf")  # noqa: T201
+    print("Done preprocessing. Created", len(chunks), "chunks of the original pdf")
     # Create vectorstore
     embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
 

templates/rag-self-query/main.py

@@ -14,7 +14,7 @@ if __name__ == "__main__":
             "chat_history": [],
         }
     )
-    print(response)  # noqa: T201
+    print(response)
 
     follow_up_question = "What are their objectives?"
 
@@ -30,4 +30,4 @@ if __name__ == "__main__":
         }
     )
 
-    print(response)  # noqa: T201
+    print(response)

templates/rag-timescale-conversation/rag_timescale_conversation/load_sample_dataset.py

@@ -47,7 +47,7 @@ def load_ts_git_dataset(
             with open(json_file_path, "w") as json_file:
                 json_file.write(response.text)
         else:
-            print(f"Failed to download JSON file. Status code: {response.status_code}")  # noqa: T201
+            print(f"Failed to download JSON file. Status code: {response.status_code}")
 
     loader = JSONLoader(
         file_path=json_file_path,

templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/load_sample_dataset.py

@@ -47,7 +47,7 @@ def load_ts_git_dataset(
             with open(json_file_path, "w") as json_file:
                 json_file.write(response.text)
         else:
-            print(f"Failed to download JSON file. Status code: {response.status_code}")  # noqa: T201
+            print(f"Failed to download JSON file. Status code: {response.status_code}")
 
     loader = JSONLoader(
         file_path=json_file_path,

templates/research-assistant/research_assistant/search/web.py

@@ -40,7 +40,7 @@ def scrape_text(url: str):
         else:
             return f"Failed to retrieve the webpage: Status code {response.status_code}"
     except Exception as e:
-        print(e)  # noqa: T201
+        print(e)
         return f"Failed to retrieve the webpage: {e}"
 
 

templates/sql-llamacpp/sql_llamacpp/chain.py

@@ -19,15 +19,15 @@ url = (
 )
 # Check if file is present in the current directory
 if not os.path.exists(file_name):
-    print(f"'{file_name}' not found. Downloading...")  # noqa: T201
+    print(f"'{file_name}' not found. Downloading...")
     # Download the file
     response = requests.get(url)
     response.raise_for_status()  # Raise an exception for HTTP errors
     with open(file_name, "wb") as f:
         f.write(response.content)
-    print(f"'{file_name}' has been downloaded.")  # noqa: T201
+    print(f"'{file_name}' has been downloaded.")
 else:
-    print(f"'{file_name}' already exists in the current directory.")  # noqa: T201
+    print(f"'{file_name}' already exists in the current directory.")
 
 # Add the LLM downloaded from HF
 model_path = file_name

templates/sql-research-assistant/sql_research_assistant/chain.py

@@ -17,6 +17,6 @@ chain = chain_notypes.with_types(input_type=InputType)
 
 
 if __name__ == "__main__":
-    print(  # noqa: T201
+    print(
         chain.invoke({"question": "who is typically older: point guards or centers?"})
     )

templates/sql-research-assistant/sql_research_assistant/search/web.py

@@ -40,7 +40,7 @@ def scrape_text(url: str):
         else:
             return f"Failed to retrieve the webpage: Status code {response.status_code}"
     except Exception as e:
-        print(e)  # noqa: T201
+        print(e)
         return f"Failed to retrieve the webpage: {e}"
 
 

templates/xml-agent/main.py

@@ -2,4 +2,4 @@ from xml_agent.agent import agent_executor
 
 if __name__ == "__main__":
     question = "who won the womens world cup in 2023?"
-    print(agent_executor.invoke({"question": question, "chat_history": []}))  # noqa: T201
+    print(agent_executor.invoke({"question": question, "chat_history": []}))