feat: 工作流

apps/application/flow/step_node/ai_chat_step_node/impl/base_chat_node.py

@@ -7,6 +7,7 @@
     @desc:
 """
 import json
+from functools import reduce
 from typing import List, Dict
 
 from django.db.models import QuerySet
@@ -139,16 +140,17 @@ class BaseChatNode(IChatNode):
     @staticmethod
     def get_history_message(history_chat_record, dialogue_number):
         start_index = len(history_chat_record) - dialogue_number
-        history_message = [[history_chat_record[index].get_human_message(), history_chat_record[index].get_ai_message()]
-                           for index in
-                           range(start_index if start_index > 0 else 0, len(history_chat_record))]
+        history_message = reduce(lambda x, y: [*x, *y], [
+            [history_chat_record[index].get_human_message(), history_chat_record[index].get_ai_message()]
+            for index in
+            range(start_index if start_index > 0 else 0, len(history_chat_record))], [])
         return history_message
 
     def generate_prompt_question(self, prompt):
         return HumanMessage(self.workflow_manage.generate_prompt(prompt))
 
     def generate_message_list(self, system: str, prompt: str, history_message):
-        if system is None or len(system) == 0:
+        if system is not None and len(system) > 0:
             return [SystemMessage(self.workflow_manage.generate_prompt(system)), *history_message,
                     HumanMessage(self.workflow_manage.generate_prompt(prompt))]
         else:

apps/application/flow/step_node/condition_node/i_condition_node.py

@@ -31,51 +31,6 @@ class ConditionNodeParamsSerializer(serializers.Serializer):
     branch = ConditionBranchSerializer(many=True)
 
 
-j = """
-     {  "branch": [
-                        {
-                            "conditions": [
-                                {
-                                    "field": [
-                                    "34902d3d-a3ff-497f-b8e1-0c34a44d7dd5",
-                                    "paragraph_list"
-                                    ],
-                                    "compare": "len_eq",
-                                    "value": "0"
-                                }
-                            ],
-                            "id": "2391",
-                            "condition": "and"
-                        },
-                        {
-                            "conditions": [
-                                {
-                                    "field": [
-                                        "34902d3d-a3ff-497f-b8e1-0c34a44d7dd5",
-                                        "paragraph_list"
-                                        ],
-                                        "compare": "len_eq",
-                                        "value": "1"
-                                }
-                            ],
-                            "id": "1143",
-                            "condition": "and"
-                        },
-                        {
-                            "conditions": [
-                                
-                            ],
-                            "id": "9208", 
-                            "condition": "and"
-                        }
-                    ]}
-"""
-a = json.loads(j)
-c = ConditionNodeParamsSerializer(data=a)
-c.is_valid(raise_exception=True)
-print(c.data)
-
-
 class IConditionNode(INode):
     def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
         return ConditionNodeParamsSerializer

apps/setting/models_provider/impl/wenxin_model_provider/model/qian_fan_chat_model.py

@@ -30,59 +30,3 @@ class QianfanChatModel(QianfanChatEndpoint):
     def get_num_tokens(self, text: str) -> int:
         tokenizer = TokenizerManage.get_tokenizer()
         return len(tokenizer.encode(text))
-
-    def stream(
-            self,
-            input: LanguageModelInput,
-            config: Optional[RunnableConfig] = None,
-            *,
-            stop: Optional[List[str]] = None,
-            **kwargs: Any,
-    ) -> Iterator[BaseMessageChunk]:
-        if len(input) % 2 == 0:
-            input = [HumanMessage(content='padding'), *input]
-        input = [
-            HumanMessage(content=input[index].content) if index % 2 == 0 else AIMessage(content=input[index].content)
-            for index in range(0, len(input))]
-        if type(self)._stream == BaseChatModel._stream:
-            # model doesn't implement streaming, so use default implementation
-            yield cast(
-                BaseMessageChunk, self.invoke(input, config=config, stop=stop, **kwargs)
-            )
-        else:
-            config = config or {}
-            messages = self._convert_input(input).to_messages()
-            params = self._get_invocation_params(stop=stop, **kwargs)
-            options = {"stop": stop, **kwargs}
-            callback_manager = CallbackManager.configure(
-                config.get("callbacks"),
-                self.callbacks,
-                self.verbose,
-                config.get("tags"),
-                self.tags,
-                config.get("metadata"),
-                self.metadata,
-            )
-            (run_manager,) = callback_manager.on_chat_model_start(
-                dumpd(self),
-                [messages],
-                invocation_params=params,
-                options=options,
-                name=config.get("run_name"),
-            )
-            try:
-                generation: Optional[ChatGenerationChunk] = None
-                for chunk in self._stream(
-                        messages, stop=stop, run_manager=run_manager, **kwargs
-                ):
-                    yield chunk.message
-                    if generation is None:
-                        generation = chunk
-                assert generation is not None
-            except BaseException as e:
-                run_manager.on_llm_error(e)
-                raise e
-            else:
-                run_manager.on_llm_end(
-                    LLMResult(generations=[[generation]]),
-                )