fix: lint

pilot/server/vectordb_qa.py

@@ -52,4 +52,4 @@ def build_knowledge_prompt(query, docs, state):
             prompt = state.get_prompt()
             print("new prompt length:" + str(len(prompt)))
 
-        return prompt
+        return prompt

pilot/server/webserver.py

@@ -385,13 +385,16 @@ def http_bot(
                         cfg.set_last_plugin_return(plugin_resp)
                         print(plugin_resp)
                         state.messages[-1][-1] = (
-                                "Model推理信息:\n" + ai_response + "\n\nDB-GPT执行结果:\n" + plugin_resp
+                            "Model推理信息:\n"
+                            + ai_response
+                            + "\n\nDB-GPT执行结果:\n"
+                            + plugin_resp
                         )
                         yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
             except NotCommands as e:
                 print("命令执行:" + e.message)
                 state.messages[-1][-1] = (
-                        "命令执行:" + e.message + "\n模型输出:\n" + str(ai_response)
+                    "命令执行:" + e.message + "\n模型输出:\n" + str(ai_response)
                 )
                 yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
         else:
@@ -422,18 +425,32 @@ def http_bot(
 
                             output = post_process_code(output)
                             state.messages[-1][-1] = output + "▌"
-                            yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
+                            yield (state, state.to_gradio_chatbot()) + (
+                                disable_btn,
+                            ) * 5
                         else:
-                            output = data["text"] + f" (error_code: {data['error_code']})"
+                            output = (
+                                data["text"] + f" (error_code: {data['error_code']})"
+                            )
                             state.messages[-1][-1] = output
                             yield (state, state.to_gradio_chatbot()) + (
-                                disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
+                                disable_btn,
+                                disable_btn,
+                                disable_btn,
+                                enable_btn,
+                                enable_btn,
+                            )
                             return
 
             except requests.exceptions.RequestException as e:
                 state.messages[-1][-1] = server_error_msg + f" (error_code: 4)"
                 yield (state, state.to_gradio_chatbot()) + (
-                disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
+                    disable_btn,
+                    disable_btn,
+                    disable_btn,
+                    enable_btn,
+                    enable_btn,
+                )
                 return
 
             state.messages[-1][-1] = state.messages[-1][-1][:-1]

pilot/vector_store/milvus_store.py

@@ -32,7 +32,6 @@ def __init__(self, ctx: {}) -> None:
         self.fields = []
         self.alias = "default"
 
-
         # use HNSW by default.
         self.index_params = {
             "metric_type": "L2",
@@ -105,17 +104,18 @@ def init_schema_and_load(self, vector_name, documents):
         embeddings = self.embedding.embed_query(texts[0])
 
         if utility.has_collection(self.collection_name):
-            self.col = Collection(
-                self.collection_name, using=self.alias
-            )
+            self.col = Collection(self.collection_name, using=self.alias)
             self.fields = []
             for x in self.col.schema.fields:
                 self.fields.append(x.name)
                 if x.auto_id:
                     self.fields.remove(x.name)
                 if x.is_primary:
                     self.primary_field = x.name
-                if x.dtype == DataType.FLOAT_VECTOR or x.dtype == DataType.BINARY_VECTOR:
+                if (
+                    x.dtype == DataType.FLOAT_VECTOR
+                    or x.dtype == DataType.BINARY_VECTOR
+                ):
                     self.vector_field = x.name
             self._add_documents(texts, metadatas)
             return self.collection_name
@@ -132,9 +132,7 @@ def init_schema_and_load(self, vector_name, documents):
         for y in texts:
             max_length = max(max_length, len(y))
         # Create the text field
-        fields.append(
-            FieldSchema(text_field, DataType.VARCHAR, max_length= 65535)
-        )
+        fields.append(FieldSchema(text_field, DataType.VARCHAR, max_length=65535))
         # primary key field
         fields.append(
             FieldSchema(primary_field, DataType.INT64, is_primary=True, auto_id=True)
@@ -252,7 +250,9 @@ def load_document(self, documents) -> None:
         """load document in vector database."""
         # self.init_schema_and_load(self.collection_name, documents)
         batch_size = 500
-        batched_list = [documents[i:i + batch_size] for i in range(0, len(documents), batch_size)]
+        batched_list = [
+            documents[i : i + batch_size] for i in range(0, len(documents), batch_size)
+        ]
         # docs = []
         for doc_batch in batched_list:
             self.init_schema_and_load(self.collection_name, doc_batch)
@@ -320,4 +320,4 @@ def _search(
         return data[0], ret
 
     def close(self):
-        connections.disconnect()
+        connections.disconnect()