lint: fix code style and lint

pilot/model/llm_out/guanaco_llm.py

@@ -4,6 +4,7 @@ from threading import Thread
 from transformers import TextIteratorStreamer, StoppingCriteriaList, StoppingCriteria
 from pilot.conversation import ROLE_ASSISTANT, ROLE_USER
 
+
 def guanaco_generate_output(model, tokenizer, params, device, context_len=2048):
     """Fork from: https://github.com/KohakuBlueleaf/guanaco-lora/blob/main/generate.py"""
 
@@ -16,10 +17,15 @@ def guanaco_generate_output(model, tokenizer, params, device, context_len=2048):
     input_ids = tokenizer(query, return_tensors="pt").input_ids
     input_ids = input_ids.to(model.device)
 
-    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+    streamer = TextIteratorStreamer(
+        tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
+    )
     stop_token_ids = [0]
+
     class StopOnTokens(StoppingCriteria):
-        def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        def __call__(
+            self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
+        ) -> bool:
             for stop_id in stop_token_ids:
                 if input_ids[0][-1] == stop_id:
                     return True
@@ -35,10 +41,9 @@ def guanaco_generate_output(model, tokenizer, params, device, context_len=2048):
         top_k=1,
         streamer=streamer,
         repetition_penalty=1.7,
-        stopping_criteria=StoppingCriteriaList([stop])
+        stopping_criteria=StoppingCriteriaList([stop]),
     )
 
-
     t1 = Thread(target=model.generate, kwargs=generate_kwargs)
     t1.start()
 
@@ -52,4 +57,3 @@ def guanaco_generate_output(model, tokenizer, params, device, context_len=2048):
         out = decoded_output.split("### Response:")[-1].strip()
 
         yield out
-

pilot/out_parser/base.py

@@ -69,7 +69,6 @@ class BaseOutputParser(ABC):
 
     # TODO 后续和模型绑定
     def parse_model_stream_resp(self, response, skip_echo_len):
-
         for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
             if chunk:
                 data = json.loads(chunk.decode())
@@ -115,7 +114,6 @@ class BaseOutputParser(ABC):
         else:
             raise ValueError("Model server error!code=" + respObj_ex["error_code"])
 
-
     def parse_prompt_response(self, model_out_text) -> T:
         """
         parse model out text to prompt define response
@@ -145,7 +143,13 @@ class BaseOutputParser(ABC):
                 cleaned_output = m.group(0)
             else:
                 raise ValueError("model server out not fllow the prompt!")
-        cleaned_output = cleaned_output.strip().replace('\n', '').replace('\\n', '').replace('\\', '').replace('\\', '')
+        cleaned_output = (
+            cleaned_output.strip()
+            .replace("\n", "")
+            .replace("\\n", "")
+            .replace("\\", "")
+            .replace("\\", "")
+        )
         return cleaned_output
 
     def parse_view_response(self, ai_text, data) -> str:

pilot/scene/base_chat.py

@@ -57,7 +57,14 @@ class BaseChat(ABC):
 
         arbitrary_types_allowed = True
 
-    def __init__(self,temperature, max_new_tokens, chat_mode, chat_session_id, current_user_input):
+    def __init__(
+        self,
+        temperature,
+        max_new_tokens,
+        chat_mode,
+        chat_session_id,
+        current_user_input,
+    ):
         self.chat_session_id = chat_session_id
         self.chat_mode = chat_mode
         self.current_user_input: str = current_user_input
@@ -68,7 +75,9 @@ class BaseChat(ABC):
         ## TEST
         self.memory = FileHistoryMemory(chat_session_id)
         ### load prompt template
-        self.prompt_template: PromptTemplate = CFG.prompt_templates[self.chat_mode.value]
+        self.prompt_template: PromptTemplate = CFG.prompt_templates[
+            self.chat_mode.value
+        ]
         self.history_message: List[OnceConversation] = []
         self.current_message: OnceConversation = OnceConversation()
         self.current_tokens_used: int = 0
@@ -129,7 +138,7 @@ class BaseChat(ABC):
     def stream_call(self):
         payload = self.__call_base()
 
-        self.skip_echo_len = len(payload.get('prompt').replace("</s>", " ")) + 11
+        self.skip_echo_len = len(payload.get("prompt").replace("</s>", " ")) + 11
         logger.info(f"Requert: \n{payload}")
         ai_response_text = ""
         try:
@@ -175,10 +184,16 @@ class BaseChat(ABC):
 
             ### output parse
             ai_response_text = (
-                self.prompt_template.output_parser.parse_model_nostream_resp(response, self.prompt_template.sep)
+                self.prompt_template.output_parser.parse_model_nostream_resp(
+                    response, self.prompt_template.sep
+                )
             )
             self.current_message.add_ai_message(ai_response_text)
-            prompt_define_response = self.prompt_template.output_parser.parse_prompt_response(ai_response_text)
+            prompt_define_response = (
+                self.prompt_template.output_parser.parse_prompt_response(
+                    ai_response_text
+                )
+            )
 
             result = self.do_with_prompt_response(prompt_define_response)
 
@@ -197,7 +212,9 @@ class BaseChat(ABC):
                         speak_to_user = prompt_define_response.thoughts
             else:
                 speak_to_user = prompt_define_response
-            view_message = self.prompt_template.output_parser.parse_view_response(speak_to_user, result)
+            view_message = self.prompt_template.output_parser.parse_view_response(
+                speak_to_user, result
+            )
             self.current_message.add_view_message(view_message)
         except Exception as e:
             print(traceback.format_exc())
@@ -288,4 +305,3 @@ class BaseChat(ABC):
 
         """
         pass
-

pilot/scene/chat_execution/out_parser.py

@@ -21,7 +21,12 @@ class PluginAction(NamedTuple):
 class PluginChatOutputParser(BaseOutputParser):
     def parse_prompt_response(self, model_out_text) -> T:
         response = json.loads(super().parse_prompt_response(model_out_text))
-        command, thoughts, speak, reasoning = response["command"], response["thoughts"], response["speak"], response["reasoning"]
+        command, thoughts, speak, reasoning = (
+            response["command"],
+            response["thoughts"],
+            response["speak"],
+            response["reasoning"],
+        )
         return PluginAction(command, speak, reasoning, thoughts)
 
     def parse_view_response(self, speak, data) -> str:

pilot/source_embedding/url_embedding.py

@@ -11,6 +11,8 @@ from pilot.source_embedding import SourceEmbedding, register
 from pilot.source_embedding.chn_document_splitter import CHNDocumentSplitter
 
 CFG = Config()
+
+
 class URLEmbedding(SourceEmbedding):
     """url embedding for read url document."""
 
@@ -27,12 +29,12 @@ class URLEmbedding(SourceEmbedding):
         loader = WebBaseLoader(web_path=self.file_path)
         if CFG.LANGUAGE == "en":
             text_splitter = CharacterTextSplitter(
-                chunk_size=KNOWLEDGE_CHUNK_SPLIT_SIZE, chunk_overlap=20, length_function=len
+                chunk_size=KNOWLEDGE_CHUNK_SPLIT_SIZE,
+                chunk_overlap=20,
+                length_function=len,
             )
         else:
-            text_splitter = CHNDocumentSplitter(
+            text_splitter = CHNDocumentSplitter(pdf=True, sentence_size=1000)
-                pdf=True, sentence_size=1000
-            )
         return loader.load_and_split(text_splitter)
 
     @register