lint: fix lint

pandasai/ee/vectorstores/milvus.py

@@ -25,7 +25,7 @@ class Milvus(VectorStore):
         default=384, description="default embedding model dimension"
     )
 
-    # Initializes the Milvus object with collection names, a URI for the Milvus database, 
+    # Initializes the Milvus object with collection names, a URI for the Milvus database,
     # a logger, and the embedding function.
     def __init__(
         self,
@@ -86,10 +86,7 @@ def add_question_answer(
             collection_name=self.qa_collection_name,
             data=data,
         )
-
         return milvus_ids
-
-
 
     # Adds documents to the Milvus collection.
     # It accepts documents, optional IDs, and metadata, and stores them in the document collection.
@@ -124,7 +121,7 @@ def add_docs(
             collection_name=self.docs_collection_name,
             data=data,
         )
-        
+
         return milvus_ids
 
     # Retrieves the most relevant question-answer pairs from the QA collection
@@ -146,7 +143,6 @@ def get_relevant_question_answers(self, question: str, k: int = 1) -> List[Dict]
             filter="",
             output_fields=[DOCUMENT],
         )
-
         return self._convert_search_response(response)
 
     # Retrieves the most relevant documents from the document collection
@@ -166,7 +162,6 @@ def get_relevant_docs(self, question: str, k: int = 1) -> List[Dict]:
             limit=k,
             output_fields=[DOCUMENT],
         )
-
         return self._convert_search_response(response)
 
     # Converts the search response returned by Milvus into a list of dictionaries
@@ -271,7 +266,6 @@ def get_relevant_question_answers_by_id(self, ids: Iterable[str]) -> List[Dict]:
             ids=milvus_ids,
             output_fields=[DOCUMENT, ID, "distance", "entity"],
         )
-
         return self._convert_search_response(response)["documents"]
 
     # Deletes documents from the document collection based on a list of document IDs.
@@ -388,4 +382,4 @@ def _is_valid_uuid(self, id: str):
 
     # Generates a list of random UUIDs.
     def generate_random_uuids(self, n):
-        return [str(uuid.uuid4()) for _ in range(n)]
+        return [str(uuid.uuid4()) for _ in range(n)]

pandasai/pipelines/chat/code_cleaning.py

@@ -176,7 +176,10 @@ def _is_malicious_code(self, code) -> bool:
             "(chr",
             "b64decode",
         ]
-        return any(re.search(r'\b'+re.escape(module)+r'\b',code) for module in dangerous_modules)
+        return any(
+            re.search(r"\b" + re.escape(module) + r"\b", code)
+            for module in dangerous_modules
+        )
 
     def _is_jailbreak(self, node: ast.stmt) -> bool:
         """