feat: application flow (#3152)

apps/application/chat_pipeline/I_base_chat_pipeline.py

@@ -0,0 +1,157 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： I_base_chat_pipeline.py
+    @date：2024/1/9 17:25
+    @desc:
+"""
+import time
+from abc import abstractmethod
+from typing import Type
+
+from rest_framework import serializers
+
+from dataset.models import Paragraph
+
+
+class ParagraphPipelineModel:
+
+    def __init__(self, _id: str, document_id: str, dataset_id: str, content: str, title: str, status: str,
+                 is_active: bool, comprehensive_score: float, similarity: float, dataset_name: str, document_name: str,
+                 hit_handling_method: str, directly_return_similarity: float, meta: dict = None):
+        self.id = _id
+        self.document_id = document_id
+        self.dataset_id = dataset_id
+        self.content = content
+        self.title = title
+        self.status = status,
+        self.is_active = is_active
+        self.comprehensive_score = comprehensive_score
+        self.similarity = similarity
+        self.dataset_name = dataset_name
+        self.document_name = document_name
+        self.hit_handling_method = hit_handling_method
+        self.directly_return_similarity = directly_return_similarity
+        self.meta = meta
+
+    def to_dict(self):
+        return {
+            'id': self.id,
+            'document_id': self.document_id,
+            'dataset_id': self.dataset_id,
+            'content': self.content,
+            'title': self.title,
+            'status': self.status,
+            'is_active': self.is_active,
+            'comprehensive_score': self.comprehensive_score,
+            'similarity': self.similarity,
+            'dataset_name': self.dataset_name,
+            'document_name': self.document_name,
+            'meta': self.meta,
+        }
+
+    class builder:
+        def __init__(self):
+            self.similarity = None
+            self.paragraph = {}
+            self.comprehensive_score = None
+            self.document_name = None
+            self.dataset_name = None
+            self.hit_handling_method = None
+            self.directly_return_similarity = 0.9
+            self.meta = {}
+
+        def add_paragraph(self, paragraph):
+            if isinstance(paragraph, Paragraph):
+                self.paragraph = {'id': paragraph.id,
+                                  'document_id': paragraph.document_id,
+                                  'dataset_id': paragraph.dataset_id,
+                                  'content': paragraph.content,
+                                  'title': paragraph.title,
+                                  'status': paragraph.status,
+                                  'is_active': paragraph.is_active,
+                                  }
+            else:
+                self.paragraph = paragraph
+            return self
+
+        def add_dataset_name(self, dataset_name):
+            self.dataset_name = dataset_name
+            return self
+
+        def add_document_name(self, document_name):
+            self.document_name = document_name
+            return self
+
+        def add_hit_handling_method(self, hit_handling_method):
+            self.hit_handling_method = hit_handling_method
+            return self
+
+        def add_directly_return_similarity(self, directly_return_similarity):
+            self.directly_return_similarity = directly_return_similarity
+            return self
+
+        def add_comprehensive_score(self, comprehensive_score: float):
+            self.comprehensive_score = comprehensive_score
+            return self
+
+        def add_similarity(self, similarity: float):
+            self.similarity = similarity
+            return self
+
+        def add_meta(self, meta: dict):
+            self.meta = meta
+            return self
+
+        def build(self):
+            return ParagraphPipelineModel(str(self.paragraph.get('id')), str(self.paragraph.get('document_id')),
+                                          str(self.paragraph.get('dataset_id')),
+                                          self.paragraph.get('content'), self.paragraph.get('title'),
+                                          self.paragraph.get('status'),
+                                          self.paragraph.get('is_active'),
+                                          self.comprehensive_score, self.similarity, self.dataset_name,
+                                          self.document_name, self.hit_handling_method, self.directly_return_similarity,
+                                          self.meta)
+
+
+class IBaseChatPipelineStep:
+    def __init__(self):
+        # 当前步骤上下文,用于存储当前步骤信息
+        self.context = {}
+
+    @abstractmethod
+    def get_step_serializer(self, manage) -> Type[serializers.Serializer]:
+        pass
+
+    def valid_args(self, manage):
+        step_serializer_clazz = self.get_step_serializer(manage)
+        step_serializer = step_serializer_clazz(data=manage.context)
+        step_serializer.is_valid(raise_exception=True)
+        self.context['step_args'] = step_serializer.data
+
+    def run(self, manage):
+        """
+
+        :param manage:      步骤管理器
+        :return: 执行结果
+        """
+        start_time = time.time()
+        self.context['start_time'] = start_time
+        # 校验参数,
+        self.valid_args(manage)
+        self._run(manage)
+        self.context['run_time'] = time.time() - start_time
+
+    def _run(self, manage):
+        pass
+
+    def execute(self, **kwargs):
+        pass
+
+    def get_details(self, manage, **kwargs):
+        """
+        运行详情
+        :return: 步骤详情
+        """
+        return None

apps/application/chat_pipeline/__init__.py

@@ -0,0 +1,8 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/1/9 17:23
+    @desc:
+"""

apps/application/chat_pipeline/pipeline_manage.py

@@ -0,0 +1,57 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： pipeline_manage.py
+    @date：2024/1/9 17:40
+    @desc:
+"""
+import time
+from functools import reduce
+from typing import List, Type, Dict
+
+from application.chat_pipeline.I_base_chat_pipeline import IBaseChatPipelineStep
+from common.handle.base_to_response import BaseToResponse
+from common.handle.impl.response.system_to_response import SystemToResponse
+
+
+class PipelineManage:
+    def __init__(self, step_list: List[Type[IBaseChatPipelineStep]],
+                 base_to_response: BaseToResponse = SystemToResponse()):
+        # 步骤执行器
+        self.step_list = [step() for step in step_list]
+        # 上下文
+        self.context = {'message_tokens': 0, 'answer_tokens': 0}
+        self.base_to_response = base_to_response
+
+    def run(self, context: Dict = None):
+        self.context['start_time'] = time.time()
+        if context is not None:
+            for key, value in context.items():
+                self.context[key] = value
+        for step in self.step_list:
+            step.run(self)
+
+    def get_details(self):
+        return reduce(lambda x, y: {**x, **y}, [{item.get('step_type'): item} for item in
+                                                filter(lambda r: r is not None,
+                                                       [row.get_details(self) for row in self.step_list])], {})
+
+    def get_base_to_response(self):
+        return self.base_to_response
+
+    class builder:
+        def __init__(self):
+            self.step_list: List[Type[IBaseChatPipelineStep]] = []
+            self.base_to_response = SystemToResponse()
+
+        def append_step(self, step: Type[IBaseChatPipelineStep]):
+            self.step_list.append(step)
+            return self
+
+        def add_base_to_response(self, base_to_response: BaseToResponse):
+            self.base_to_response = base_to_response
+            return self
+
+        def build(self):
+            return PipelineManage(step_list=self.step_list, base_to_response=self.base_to_response)

apps/application/chat_pipeline/step/__init__.py

@@ -0,0 +1,8 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/1/9 18:23
+    @desc:
+"""

apps/application/chat_pipeline/step/chat_step/__init__.py

@@ -0,0 +1,8 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/1/9 18:23
+    @desc:
+"""

apps/application/chat_pipeline/step/chat_step/i_chat_step.py

@@ -0,0 +1,110 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： i_chat_step.py
+    @date：2024/1/9 18:17
+    @desc: 对话
+"""
+from abc import abstractmethod
+from typing import Type, List
+
+from django.utils.translation import gettext_lazy as _
+from langchain.chat_models.base import BaseChatModel
+from langchain.schema import BaseMessage
+from rest_framework import serializers
+
+from application.chat_pipeline.I_base_chat_pipeline import IBaseChatPipelineStep, ParagraphPipelineModel
+from application.chat_pipeline.pipeline_manage import PipelineManage
+from application.serializers.application_serializers import NoReferencesSetting
+from common.field.common import InstanceField
+from common.util.field_message import ErrMessage
+
+
+class ModelField(serializers.Field):
+    def to_internal_value(self, data):
+        if not isinstance(data, BaseChatModel):
+            self.fail(_('Model type error'), value=data)
+        return data
+
+    def to_representation(self, value):
+        return value
+
+
+class MessageField(serializers.Field):
+    def to_internal_value(self, data):
+        if not isinstance(data, BaseMessage):
+            self.fail(_('Message type error'), value=data)
+        return data
+
+    def to_representation(self, value):
+        return value
+
+
+class PostResponseHandler:
+    @abstractmethod
+    def handler(self, chat_id, chat_record_id, paragraph_list: List[ParagraphPipelineModel], problem_text: str,
+                answer_text,
+                manage, step, padding_problem_text: str = None, client_id=None, **kwargs):
+        pass
+
+
+class IChatStep(IBaseChatPipelineStep):
+    class InstanceSerializer(serializers.Serializer):
+        # 对话列表
+        message_list = serializers.ListField(required=True, child=MessageField(required=True),
+                                             error_messages=ErrMessage.list(_("Conversation list")))
+        model_id = serializers.UUIDField(required=False, allow_null=True, error_messages=ErrMessage.uuid(_("Model id")))
+        # 段落列表
+        paragraph_list = serializers.ListField(error_messages=ErrMessage.list(_("Paragraph List")))
+        # 对话id
+        chat_id = serializers.UUIDField(required=True, error_messages=ErrMessage.uuid(_("Conversation ID")))
+        # 用户问题
+        problem_text = serializers.CharField(required=True, error_messages=ErrMessage.uuid(_("User Questions")))
+        # 后置处理器
+        post_response_handler = InstanceField(model_type=PostResponseHandler,
+                                              error_messages=ErrMessage.base(_("Post-processor")))
+        # 补全问题
+        padding_problem_text = serializers.CharField(required=False,
+                                                     error_messages=ErrMessage.base(_("Completion Question")))
+        # 是否使用流的形式输出
+        stream = serializers.BooleanField(required=False, error_messages=ErrMessage.base(_("Streaming Output")))
+        client_id = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Client id")))
+        client_type = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Client Type")))
+        # 未查询到引用分段
+        no_references_setting = NoReferencesSetting(required=True,
+                                                    error_messages=ErrMessage.base(_("No reference segment settings")))
+
+        user_id = serializers.UUIDField(required=True, error_messages=ErrMessage.uuid(_("User ID")))
+
+        model_setting = serializers.DictField(required=True, allow_null=True,
+                                              error_messages=ErrMessage.dict(_("Model settings")))
+
+        model_params_setting = serializers.DictField(required=False, allow_null=True,
+                                                     error_messages=ErrMessage.dict(_("Model parameter settings")))
+
+        def is_valid(self, *, raise_exception=False):
+            super().is_valid(raise_exception=True)
+            message_list: List = self.initial_data.get('message_list')
+            for message in message_list:
+                if not isinstance(message, BaseMessage):
+                    raise Exception(_("message type error"))
+
+    def get_step_serializer(self, manage: PipelineManage) -> Type[serializers.Serializer]:
+        return self.InstanceSerializer
+
+    def _run(self, manage: PipelineManage):
+        chat_result = self.execute(**self.context['step_args'], manage=manage)
+        manage.context['chat_result'] = chat_result
+
+    @abstractmethod
+    def execute(self, message_list: List[BaseMessage],
+                chat_id, problem_text,
+                post_response_handler: PostResponseHandler,
+                model_id: str = None,
+                user_id: str = None,
+                paragraph_list=None,
+                manage: PipelineManage = None,
+                padding_problem_text: str = None, stream: bool = True, client_id=None, client_type=None,
+                no_references_setting=None, model_params_setting=None, model_setting=None, **kwargs):
+        pass

apps/application/chat_pipeline/step/chat_step/impl/base_chat_step.py

@@ -0,0 +1,334 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： base_chat_step.py
+    @date：2024/1/9 18:25
+    @desc: 对话step Base实现
+"""
+import logging
+import time
+import traceback
+import uuid
+from typing import List
+
+from django.db.models import QuerySet
+from django.http import StreamingHttpResponse
+from django.utils.translation import gettext as _
+from langchain.chat_models.base import BaseChatModel
+from langchain.schema import BaseMessage
+from langchain.schema.messages import HumanMessage, AIMessage
+from langchain_core.messages import AIMessageChunk
+from rest_framework import status
+
+from application.chat_pipeline.I_base_chat_pipeline import ParagraphPipelineModel
+from application.chat_pipeline.pipeline_manage import PipelineManage
+from application.chat_pipeline.step.chat_step.i_chat_step import IChatStep, PostResponseHandler
+from application.flow.tools import Reasoning
+from application.models.api_key_model import ApplicationPublicAccessClient
+from common.constants.authentication_type import AuthenticationType
+from setting.models_provider.tools import get_model_instance_by_model_user_id
+
+
+def add_access_num(client_id=None, client_type=None, application_id=None):
+    if client_type == AuthenticationType.APPLICATION_ACCESS_TOKEN.value and application_id is not None:
+        application_public_access_client = (QuerySet(ApplicationPublicAccessClient).filter(client_id=client_id,
+                                                                                           application_id=application_id)
+                                            .first())
+        if application_public_access_client is not None:
+            application_public_access_client.access_num = application_public_access_client.access_num + 1
+            application_public_access_client.intraday_access_num = application_public_access_client.intraday_access_num + 1
+            application_public_access_client.save()
+
+
+def write_context(step, manage, request_token, response_token, all_text):
+    step.context['message_tokens'] = request_token
+    step.context['answer_tokens'] = response_token
+    current_time = time.time()
+    step.context['answer_text'] = all_text
+    step.context['run_time'] = current_time - step.context['start_time']
+    manage.context['run_time'] = current_time - manage.context['start_time']
+    manage.context['message_tokens'] = manage.context['message_tokens'] + request_token
+    manage.context['answer_tokens'] = manage.context['answer_tokens'] + response_token
+
+
+def event_content(response,
+                  chat_id,
+                  chat_record_id,
+                  paragraph_list: List[ParagraphPipelineModel],
+                  post_response_handler: PostResponseHandler,
+                  manage,
+                  step,
+                  chat_model,
+                  message_list: List[BaseMessage],
+                  problem_text: str,
+                  padding_problem_text: str = None,
+                  client_id=None, client_type=None,
+                  is_ai_chat: bool = None,
+                  model_setting=None):
+    if model_setting is None:
+        model_setting = {}
+    reasoning_content_enable = model_setting.get('reasoning_content_enable', False)
+    reasoning_content_start = model_setting.get('reasoning_content_start', '<think>')
+    reasoning_content_end = model_setting.get('reasoning_content_end', '</think>')
+    reasoning = Reasoning(reasoning_content_start,
+                          reasoning_content_end)
+    all_text = ''
+    reasoning_content = ''
+    try:
+        response_reasoning_content = False
+        for chunk in response:
+            reasoning_chunk = reasoning.get_reasoning_content(chunk)
+            content_chunk = reasoning_chunk.get('content')
+            if 'reasoning_content' in chunk.additional_kwargs:
+                response_reasoning_content = True
+                reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '')
+            else:
+                reasoning_content_chunk = reasoning_chunk.get('reasoning_content')
+            all_text += content_chunk
+            if reasoning_content_chunk is None:
+                reasoning_content_chunk = ''
+            reasoning_content += reasoning_content_chunk
+            yield manage.get_base_to_response().to_stream_chunk_response(chat_id, str(chat_record_id), 'ai-chat-node',
+                                                                         [], content_chunk,
+                                                                         False,
+                                                                         0, 0, {'node_is_end': False,
+                                                                                'view_type': 'many_view',
+                                                                                'node_type': 'ai-chat-node',
+                                                                                'real_node_id': 'ai-chat-node',
+                                                                                'reasoning_content': reasoning_content_chunk if reasoning_content_enable else ''})
+        reasoning_chunk = reasoning.get_end_reasoning_content()
+        all_text += reasoning_chunk.get('content')
+        reasoning_content_chunk = ""
+        if not response_reasoning_content:
+            reasoning_content_chunk = reasoning_chunk.get(
+                'reasoning_content')
+        yield manage.get_base_to_response().to_stream_chunk_response(chat_id, str(chat_record_id), 'ai-chat-node',
+                                                                     [], reasoning_chunk.get('content'),
+                                                                     False,
+                                                                     0, 0, {'node_is_end': False,
+                                                                            'view_type': 'many_view',
+                                                                            'node_type': 'ai-chat-node',
+                                                                            'real_node_id': 'ai-chat-node',
+                                                                            'reasoning_content'
+                                                                            : reasoning_content_chunk if reasoning_content_enable else ''})
+        # 获取token
+        if is_ai_chat:
+            try:
+                request_token = chat_model.get_num_tokens_from_messages(message_list)
+                response_token = chat_model.get_num_tokens(all_text)
+            except Exception as e:
+                request_token = 0
+                response_token = 0
+        else:
+            request_token = 0
+            response_token = 0
+        write_context(step, manage, request_token, response_token, all_text)
+        asker = manage.context.get('form_data', {}).get('asker', None)
+        post_response_handler.handler(chat_id, chat_record_id, paragraph_list, problem_text,
+                                      all_text, manage, step, padding_problem_text, client_id,
+                                      reasoning_content=reasoning_content if reasoning_content_enable else ''
+                                      , asker=asker)
+        yield manage.get_base_to_response().to_stream_chunk_response(chat_id, str(chat_record_id), 'ai-chat-node',
+                                                                     [], '', True,
+                                                                     request_token, response_token,
+                                                                     {'node_is_end': True, 'view_type': 'many_view',
+                                                                      'node_type': 'ai-chat-node'})
+        add_access_num(client_id, client_type, manage.context.get('application_id'))
+    except Exception as e:
+        logging.getLogger("max_kb_error").error(f'{str(e)}:{traceback.format_exc()}')
+        all_text = 'Exception:' + str(e)
+        write_context(step, manage, 0, 0, all_text)
+        asker = manage.context.get('form_data', {}).get('asker', None)
+        post_response_handler.handler(chat_id, chat_record_id, paragraph_list, problem_text,
+                                      all_text, manage, step, padding_problem_text, client_id, reasoning_content='',
+                                      asker=asker)
+        add_access_num(client_id, client_type, manage.context.get('application_id'))
+        yield manage.get_base_to_response().to_stream_chunk_response(chat_id, str(chat_record_id), 'ai-chat-node',
+                                                                     [], all_text,
+                                                                     False,
+                                                                     0, 0, {'node_is_end': False,
+                                                                            'view_type': 'many_view',
+                                                                            'node_type': 'ai-chat-node',
+                                                                            'real_node_id': 'ai-chat-node',
+                                                                            'reasoning_content': ''})
+
+
+class BaseChatStep(IChatStep):
+    def execute(self, message_list: List[BaseMessage],
+                chat_id,
+                problem_text,
+                post_response_handler: PostResponseHandler,
+                model_id: str = None,
+                user_id: str = None,
+                paragraph_list=None,
+                manage: PipelineManage = None,
+                padding_problem_text: str = None,
+                stream: bool = True,
+                client_id=None, client_type=None,
+                no_references_setting=None,
+                model_params_setting=None,
+                model_setting=None,
+                **kwargs):
+        chat_model = get_model_instance_by_model_user_id(model_id, user_id,
+                                                         **model_params_setting) if model_id is not None else None
+        if stream:
+            return self.execute_stream(message_list, chat_id, problem_text, post_response_handler, chat_model,
+                                       paragraph_list,
+                                       manage, padding_problem_text, client_id, client_type, no_references_setting,
+                                       model_setting)
+        else:
+            return self.execute_block(message_list, chat_id, problem_text, post_response_handler, chat_model,
+                                      paragraph_list,
+                                      manage, padding_problem_text, client_id, client_type, no_references_setting,
+                                      model_setting)
+
+    def get_details(self, manage, **kwargs):
+        return {
+            'step_type': 'chat_step',
+            'run_time': self.context['run_time'],
+            'model_id': str(manage.context['model_id']),
+            'message_list': self.reset_message_list(self.context['step_args'].get('message_list'),
+                                                    self.context['answer_text']),
+            'message_tokens': self.context['message_tokens'],
+            'answer_tokens': self.context['answer_tokens'],
+            'cost': 0,
+        }
+
+    @staticmethod
+    def reset_message_list(message_list: List[BaseMessage], answer_text):
+        result = [{'role': 'user' if isinstance(message, HumanMessage) else 'ai', 'content': message.content} for
+                  message
+                  in
+                  message_list]
+        result.append({'role': 'ai', 'content': answer_text})
+        return result
+
+    @staticmethod
+    def get_stream_result(message_list: List[BaseMessage],
+                          chat_model: BaseChatModel = None,
+                          paragraph_list=None,
+                          no_references_setting=None,
+                          problem_text=None):
+        if paragraph_list is None:
+            paragraph_list = []
+        directly_return_chunk_list = [AIMessageChunk(content=paragraph.content)
+                                      for paragraph in paragraph_list if (
+                                              paragraph.hit_handling_method == 'directly_return' and paragraph.similarity >= paragraph.directly_return_similarity)]
+        if directly_return_chunk_list is not None and len(directly_return_chunk_list) > 0:
+            return iter(directly_return_chunk_list), False
+        elif len(paragraph_list) == 0 and no_references_setting.get(
+                'status') == 'designated_answer':
+            return iter(
+                [AIMessageChunk(content=no_references_setting.get('value').replace('{question}', problem_text))]), False
+        if chat_model is None:
+            return iter([AIMessageChunk(
+                _('Sorry, the AI model is not configured. Please go to the application to set up the AI model first.'))]), False
+        else:
+            return chat_model.stream(message_list), True
+
+    def execute_stream(self, message_list: List[BaseMessage],
+                       chat_id,
+                       problem_text,
+                       post_response_handler: PostResponseHandler,
+                       chat_model: BaseChatModel = None,
+                       paragraph_list=None,
+                       manage: PipelineManage = None,
+                       padding_problem_text: str = None,
+                       client_id=None, client_type=None,
+                       no_references_setting=None,
+                       model_setting=None):
+        chat_result, is_ai_chat = self.get_stream_result(message_list, chat_model, paragraph_list,
+                                                         no_references_setting, problem_text)
+        chat_record_id = uuid.uuid1()
+        r = StreamingHttpResponse(
+            streaming_content=event_content(chat_result, chat_id, chat_record_id, paragraph_list,
+                                            post_response_handler, manage, self, chat_model, message_list, problem_text,
+                                            padding_problem_text, client_id, client_type, is_ai_chat, model_setting),
+            content_type='text/event-stream;charset=utf-8')
+
+        r['Cache-Control'] = 'no-cache'
+        return r
+
+    @staticmethod
+    def get_block_result(message_list: List[BaseMessage],
+                         chat_model: BaseChatModel = None,
+                         paragraph_list=None,
+                         no_references_setting=None,
+                         problem_text=None):
+        if paragraph_list is None:
+            paragraph_list = []
+        directly_return_chunk_list = [AIMessageChunk(content=paragraph.content)
+                                      for paragraph in paragraph_list if (
+                                              paragraph.hit_handling_method == 'directly_return' and paragraph.similarity >= paragraph.directly_return_similarity)]
+        if directly_return_chunk_list is not None and len(directly_return_chunk_list) > 0:
+            return directly_return_chunk_list[0], False
+        elif len(paragraph_list) == 0 and no_references_setting.get(
+                'status') == 'designated_answer':
+            return AIMessage(no_references_setting.get('value').replace('{question}', problem_text)), False
+        if chat_model is None:
+            return AIMessage(
+                _('Sorry, the AI model is not configured. Please go to the application to set up the AI model first.')), False
+        else:
+            return chat_model.invoke(message_list), True
+
+    def execute_block(self, message_list: List[BaseMessage],
+                      chat_id,
+                      problem_text,
+                      post_response_handler: PostResponseHandler,
+                      chat_model: BaseChatModel = None,
+                      paragraph_list=None,
+                      manage: PipelineManage = None,
+                      padding_problem_text: str = None,
+                      client_id=None, client_type=None, no_references_setting=None,
+                      model_setting=None):
+        reasoning_content_enable = model_setting.get('reasoning_content_enable', False)
+        reasoning_content_start = model_setting.get('reasoning_content_start', '<think>')
+        reasoning_content_end = model_setting.get('reasoning_content_end', '</think>')
+        reasoning = Reasoning(reasoning_content_start,
+                              reasoning_content_end)
+        chat_record_id = uuid.uuid1()
+        # 调用模型
+        try:
+            chat_result, is_ai_chat = self.get_block_result(message_list, chat_model, paragraph_list,
+                                                            no_references_setting, problem_text)
+            if is_ai_chat:
+                request_token = chat_model.get_num_tokens_from_messages(message_list)
+                response_token = chat_model.get_num_tokens(chat_result.content)
+            else:
+                request_token = 0
+                response_token = 0
+            write_context(self, manage, request_token, response_token, chat_result.content)
+            reasoning_result = reasoning.get_reasoning_content(chat_result)
+            reasoning_result_end = reasoning.get_end_reasoning_content()
+            content = reasoning_result.get('content') + reasoning_result_end.get('content')
+            if 'reasoning_content' in chat_result.response_metadata:
+                reasoning_content = chat_result.response_metadata.get('reasoning_content', '')
+            else:
+                reasoning_content = reasoning_result.get('reasoning_content') + reasoning_result_end.get(
+                    'reasoning_content')
+            asker = manage.context.get('form_data', {}).get('asker', None)
+            post_response_handler.handler(chat_id, chat_record_id, paragraph_list, problem_text,
+                                          content, manage, self, padding_problem_text, client_id,
+                                          reasoning_content=reasoning_content if reasoning_content_enable else '',
+                                          asker=asker)
+            add_access_num(client_id, client_type, manage.context.get('application_id'))
+            return manage.get_base_to_response().to_block_response(str(chat_id), str(chat_record_id),
+                                                                   content, True,
+                                                                   request_token, response_token,
+                                                                   {
+                                                                       'reasoning_content': reasoning_content if reasoning_content_enable else '',
+                                                                       'answer_list': [{
+                                                                           'content': content,
+                                                                           'reasoning_content': reasoning_content if reasoning_content_enable else ''
+                                                                       }]})
+        except Exception as e:
+            all_text = 'Exception:' + str(e)
+            write_context(self, manage, 0, 0, all_text)
+            asker = manage.context.get('form_data', {}).get('asker', None)
+            post_response_handler.handler(chat_id, chat_record_id, paragraph_list, problem_text,
+                                          all_text, manage, self, padding_problem_text, client_id, reasoning_content='',
+                                          asker=asker)
+            add_access_num(client_id, client_type, manage.context.get('application_id'))
+            return manage.get_base_to_response().to_block_response(str(chat_id), str(chat_record_id), all_text, True, 0,
+                                                                   0, _status=status.HTTP_500_INTERNAL_SERVER_ERROR)

apps/application/chat_pipeline/step/generate_human_message_step/__init__.py

@@ -0,0 +1,8 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/1/9 18:23
+    @desc:
+"""

apps/application/chat_pipeline/step/generate_human_message_step/i_generate_human_message_step.py

@@ -0,0 +1,81 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： i_generate_human_message_step.py
+    @date：2024/1/9 18:15
+    @desc: 生成对话模板
+"""
+from abc import abstractmethod
+from typing import Type, List
+
+from django.utils.translation import gettext_lazy as _
+from langchain.schema import BaseMessage
+from rest_framework import serializers
+
+from application.chat_pipeline.I_base_chat_pipeline import IBaseChatPipelineStep, ParagraphPipelineModel
+from application.chat_pipeline.pipeline_manage import PipelineManage
+from application.models import ChatRecord
+from application.serializers.application_serializers import NoReferencesSetting
+from common.field.common import InstanceField
+from common.util.field_message import ErrMessage
+
+
+class IGenerateHumanMessageStep(IBaseChatPipelineStep):
+    class InstanceSerializer(serializers.Serializer):
+        # 问题
+        problem_text = serializers.CharField(required=True, error_messages=ErrMessage.char(_("question")))
+        # 段落列表
+        paragraph_list = serializers.ListField(child=InstanceField(model_type=ParagraphPipelineModel, required=True),
+                                               error_messages=ErrMessage.list(_("Paragraph List")))
+        # 历史对答
+        history_chat_record = serializers.ListField(child=InstanceField(model_type=ChatRecord, required=True),
+                                                    error_messages=ErrMessage.list(_("History Questions")))
+        # 多轮对话数量
+        dialogue_number = serializers.IntegerField(required=True, error_messages=ErrMessage.integer(_("Number of multi-round conversations")))
+        # 最大携带知识库段落长度
+        max_paragraph_char_number = serializers.IntegerField(required=True, error_messages=ErrMessage.integer(
+            _("Maximum length of the knowledge base paragraph")))
+        # 模板
+        prompt = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Prompt word")))
+        system = serializers.CharField(required=False, allow_null=True, allow_blank=True,
+                                       error_messages=ErrMessage.char(_("System prompt words (role)")))
+        # 补齐问题
+        padding_problem_text = serializers.CharField(required=False, error_messages=ErrMessage.char(_("Completion problem")))
+        # 未查询到引用分段
+        no_references_setting = NoReferencesSetting(required=True, error_messages=ErrMessage.base(_("No reference segment settings")))
+
+    def get_step_serializer(self, manage: PipelineManage) -> Type[serializers.Serializer]:
+        return self.InstanceSerializer
+
+    def _run(self, manage: PipelineManage):
+        message_list = self.execute(**self.context['step_args'])
+        manage.context['message_list'] = message_list
+
+    @abstractmethod
+    def execute(self,
+                problem_text: str,
+                paragraph_list: List[ParagraphPipelineModel],
+                history_chat_record: List[ChatRecord],
+                dialogue_number: int,
+                max_paragraph_char_number: int,
+                prompt: str,
+                padding_problem_text: str = None,
+                no_references_setting=None,
+                system=None,
+                **kwargs) -> List[BaseMessage]:
+        """
+
+        :param problem_text:               原始问题文本
+        :param paragraph_list:             段落列表
+        :param history_chat_record:        历史对话记录
+        :param dialogue_number:            多轮对话数量
+        :param max_paragraph_char_number:  最大段落长度
+        :param prompt:                     模板
+        :param padding_problem_text        用户修改文本
+        :param kwargs:                     其他参数
+        :param no_references_setting:     无引用分段设置
+        :param system                     系统提示称
+        :return:
+        """
+        pass

apps/application/chat_pipeline/step/generate_human_message_step/impl/base_generate_human_message_step.py

@@ -0,0 +1,73 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： base_generate_human_message_step.py.py
+    @date：2024/1/10 17:50
+    @desc:
+"""
+from typing import List, Dict
+
+from langchain.schema import BaseMessage, HumanMessage
+from langchain_core.messages import SystemMessage
+
+from application.chat_pipeline.I_base_chat_pipeline import ParagraphPipelineModel
+from application.chat_pipeline.step.generate_human_message_step.i_generate_human_message_step import \
+    IGenerateHumanMessageStep
+from application.models import ChatRecord
+from common.util.split_model import flat_map
+
+
+class BaseGenerateHumanMessageStep(IGenerateHumanMessageStep):
+
+    def execute(self, problem_text: str,
+                paragraph_list: List[ParagraphPipelineModel],
+                history_chat_record: List[ChatRecord],
+                dialogue_number: int,
+                max_paragraph_char_number: int,
+                prompt: str,
+                padding_problem_text: str = None,
+                no_references_setting=None,
+                system=None,
+                **kwargs) -> List[BaseMessage]:
+        prompt = prompt if (paragraph_list is not None and len(paragraph_list) > 0) else no_references_setting.get(
+            'value')
+        exec_problem_text = padding_problem_text if padding_problem_text is not None else problem_text
+        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))]
+        if system is not None and len(system) > 0:
+            return [SystemMessage(system), *flat_map(history_message),
+                    self.to_human_message(prompt, exec_problem_text, max_paragraph_char_number, paragraph_list,
+                                          no_references_setting)]
+
+        return [*flat_map(history_message),
+                self.to_human_message(prompt, exec_problem_text, max_paragraph_char_number, paragraph_list,
+                                      no_references_setting)]
+
+    @staticmethod
+    def to_human_message(prompt: str,
+                         problem: str,
+                         max_paragraph_char_number: int,
+                         paragraph_list: List[ParagraphPipelineModel],
+                         no_references_setting: Dict):
+        if paragraph_list is None or len(paragraph_list) == 0:
+            if no_references_setting.get('status') == 'ai_questioning':
+                return HumanMessage(
+                    content=no_references_setting.get('value').replace('{question}', problem))
+            else:
+                return HumanMessage(content=prompt.replace('{data}', "").replace('{question}', problem))
+        temp_data = ""
+        data_list = []
+        for p in paragraph_list:
+            content = f"{p.title}:{p.content}"
+            temp_data += content
+            if len(temp_data) > max_paragraph_char_number:
+                row_data = content[0:max_paragraph_char_number - len(temp_data)]
+                data_list.append(f"<data>{row_data}</data>")
+                break
+            else:
+                data_list.append(f"<data>{content}</data>")
+        data = "\n".join(data_list)
+        return HumanMessage(content=prompt.replace('{data}', data).replace('{question}', problem))

apps/application/chat_pipeline/step/reset_problem_step/__init__.py

@@ -0,0 +1,8 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/1/9 18:23
+    @desc:
+"""

apps/application/chat_pipeline/step/reset_problem_step/i_reset_problem_step.py

@@ -0,0 +1,57 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： i_reset_problem_step.py
+    @date：2024/1/9 18:12
+    @desc: 重写处理问题
+"""
+from abc import abstractmethod
+from typing import Type, List
+
+from django.utils.translation import gettext_lazy as _
+from rest_framework import serializers
+
+from application.chat_pipeline.I_base_chat_pipeline import IBaseChatPipelineStep
+from application.chat_pipeline.pipeline_manage import PipelineManage
+from application.models import ChatRecord
+from common.field.common import InstanceField
+from common.util.field_message import ErrMessage
+
+
+class IResetProblemStep(IBaseChatPipelineStep):
+    class InstanceSerializer(serializers.Serializer):
+        # 问题文本
+        problem_text = serializers.CharField(required=True, error_messages=ErrMessage.float(_("question")))
+        # 历史对答
+        history_chat_record = serializers.ListField(child=InstanceField(model_type=ChatRecord, required=True),
+                                                    error_messages=ErrMessage.list(_("History Questions")))
+        # 大语言模型
+        model_id = serializers.UUIDField(required=False, allow_null=True, error_messages=ErrMessage.uuid(_("Model id")))
+        user_id = serializers.UUIDField(required=True, error_messages=ErrMessage.uuid(_("User ID")))
+        problem_optimization_prompt = serializers.CharField(required=False, max_length=102400,
+                                                            error_messages=ErrMessage.char(
+                                                                _("Question completion prompt")))
+
+    def get_step_serializer(self, manage: PipelineManage) -> Type[serializers.Serializer]:
+        return self.InstanceSerializer
+
+    def _run(self, manage: PipelineManage):
+        padding_problem = self.execute(**self.context.get('step_args'))
+        # 用户输入问题
+        source_problem_text = self.context.get('step_args').get('problem_text')
+        self.context['problem_text'] = source_problem_text
+        self.context['padding_problem_text'] = padding_problem
+        manage.context['problem_text'] = source_problem_text
+        manage.context['padding_problem_text'] = padding_problem
+        # 累加tokens
+        manage.context['message_tokens'] = manage.context.get('message_tokens', 0) + self.context.get('message_tokens',
+                                                                                                      0)
+        manage.context['answer_tokens'] = manage.context.get('answer_tokens', 0) + self.context.get('answer_tokens', 0)
+
+    @abstractmethod
+    def execute(self, problem_text: str, history_chat_record: List[ChatRecord] = None, model_id: str = None,
+                problem_optimization_prompt=None,
+                user_id=None,
+                **kwargs):
+        pass

apps/application/chat_pipeline/step/reset_problem_step/impl/base_reset_problem_step.py

@@ -0,0 +1,68 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： base_reset_problem_step.py
+    @date：2024/1/10 14:35
+    @desc:
+"""
+from typing import List
+
+from django.utils.translation import gettext as _
+from langchain.schema import HumanMessage
+
+from application.chat_pipeline.step.reset_problem_step.i_reset_problem_step import IResetProblemStep
+from application.models import ChatRecord
+from common.util.split_model import flat_map
+from setting.models_provider.tools import get_model_instance_by_model_user_id
+
+prompt = _(
+    "() contains the user's question. Answer the guessed user's question based on the context ({question}) Requirement: Output a complete question and put it in the <data></data> tag")
+
+
+class BaseResetProblemStep(IResetProblemStep):
+    def execute(self, problem_text: str, history_chat_record: List[ChatRecord] = None, model_id: str = None,
+                problem_optimization_prompt=None,
+                user_id=None,
+                **kwargs) -> str:
+        chat_model = get_model_instance_by_model_user_id(model_id, user_id) if model_id is not None else None
+        if chat_model is None:
+            return problem_text
+        start_index = len(history_chat_record) - 3
+        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))]
+        reset_prompt = problem_optimization_prompt if problem_optimization_prompt else prompt
+        message_list = [*flat_map(history_message),
+                        HumanMessage(content=reset_prompt.replace('{question}', problem_text))]
+        response = chat_model.invoke(message_list)
+        padding_problem = problem_text
+        if response.content.__contains__("<data>") and response.content.__contains__('</data>'):
+            padding_problem_data = response.content[
+                                   response.content.index('<data>') + 6:response.content.index('</data>')]
+            if padding_problem_data is not None and len(padding_problem_data.strip()) > 0:
+                padding_problem = padding_problem_data
+        elif len(response.content) > 0:
+            padding_problem = response.content
+
+        try:
+            request_token = chat_model.get_num_tokens_from_messages(message_list)
+            response_token = chat_model.get_num_tokens(padding_problem)
+        except Exception as e:
+            request_token = 0
+            response_token = 0
+        self.context['message_tokens'] = request_token
+        self.context['answer_tokens'] = response_token
+        return padding_problem
+
+    def get_details(self, manage, **kwargs):
+        return {
+            'step_type': 'problem_padding',
+            'run_time': self.context['run_time'],
+            'model_id': str(manage.context['model_id']) if 'model_id' in manage.context else None,
+            'message_tokens': self.context.get('message_tokens', 0),
+            'answer_tokens': self.context.get('answer_tokens', 0),
+            'cost': 0,
+            'padding_problem_text': self.context.get('padding_problem_text'),
+            'problem_text': self.context.get("step_args").get('problem_text'),
+        }

apps/application/chat_pipeline/step/search_dataset_step/__init__.py

@@ -0,0 +1,8 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/1/9 18:24
+    @desc:
+"""

apps/application/chat_pipeline/step/search_dataset_step/i_search_dataset_step.py

@@ -0,0 +1,77 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： i_search_dataset_step.py
+    @date：2024/1/9 18:10
+    @desc: 检索知识库
+"""
+import re
+from abc import abstractmethod
+from typing import List, Type
+
+from django.core import validators
+from django.utils.translation import gettext_lazy as _
+from rest_framework import serializers
+
+from application.chat_pipeline.I_base_chat_pipeline import IBaseChatPipelineStep, ParagraphPipelineModel
+from application.chat_pipeline.pipeline_manage import PipelineManage
+from common.util.field_message import ErrMessage
+
+
+class ISearchDatasetStep(IBaseChatPipelineStep):
+    class InstanceSerializer(serializers.Serializer):
+        # 原始问题文本
+        problem_text = serializers.CharField(required=True, error_messages=ErrMessage.char(_("question")))
+        # 系统补全问题文本
+        padding_problem_text = serializers.CharField(required=False,
+                                                     error_messages=ErrMessage.char(_("System completes question text")))
+        # 需要查询的数据集id列表
+        dataset_id_list = serializers.ListField(required=True, child=serializers.UUIDField(required=True),
+                                                error_messages=ErrMessage.list(_("Dataset id list")))
+        # 需要排除的文档id
+        exclude_document_id_list = serializers.ListField(required=True, child=serializers.UUIDField(required=True),
+                                                         error_messages=ErrMessage.list(_("List of document ids to exclude")))
+        # 需要排除向量id
+        exclude_paragraph_id_list = serializers.ListField(required=True, child=serializers.UUIDField(required=True),
+                                                          error_messages=ErrMessage.list(_("List of exclusion vector ids")))
+        # 需要查询的条数
+        top_n = serializers.IntegerField(required=True,
+                                         error_messages=ErrMessage.integer(_("Reference segment number")))
+        # 相似度 0-1之间
+        similarity = serializers.FloatField(required=True, max_value=1, min_value=0,
+                                            error_messages=ErrMessage.float(_("Similarity")))
+        search_mode = serializers.CharField(required=True, validators=[
+            validators.RegexValidator(regex=re.compile("^embedding|keywords|blend$"),
+                                      message=_("The type only supports embedding|keywords|blend"), code=500)
+        ], error_messages=ErrMessage.char(_("Retrieval Mode")))
+        user_id = serializers.UUIDField(required=True, error_messages=ErrMessage.uuid(_("User ID")))
+
+    def get_step_serializer(self, manage: PipelineManage) -> Type[InstanceSerializer]:
+        return self.InstanceSerializer
+
+    def _run(self, manage: PipelineManage):
+        paragraph_list = self.execute(**self.context['step_args'])
+        manage.context['paragraph_list'] = paragraph_list
+        self.context['paragraph_list'] = paragraph_list
+
+    @abstractmethod
+    def execute(self, problem_text: str, dataset_id_list: list[str], exclude_document_id_list: list[str],
+                exclude_paragraph_id_list: list[str], top_n: int, similarity: float, padding_problem_text: str = None,
+                search_mode: str = None,
+                user_id=None,
+                **kwargs) -> List[ParagraphPipelineModel]:
+        """
+        关于 用户和补全问题 说明: 补全问题如果有就使用补全问题去查询 反之就用用户原始问题查询
+        :param similarity:                         相关性
+        :param top_n:                              查询多少条
+        :param problem_text:                       用户问题
+        :param dataset_id_list:                    需要查询的数据集id列表
+        :param exclude_document_id_list:           需要排除的文档id
+        :param exclude_paragraph_id_list:          需要排除段落id
+        :param padding_problem_text                补全问题
+        :param search_mode                         检索模式
+        :param user_id                             用户id
+        :return: 段落列表
+        """
+        pass

apps/application/chat_pipeline/step/search_dataset_step/impl/base_search_dataset_step.py

@@ -0,0 +1,138 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： base_search_dataset_step.py
+    @date：2024/1/10 10:33
+    @desc:
+"""
+import os
+from typing import List, Dict
+
+from django.db.models import QuerySet
+from django.utils.translation import gettext_lazy as _
+from rest_framework.utils.formatting import lazy_format
+
+from application.chat_pipeline.I_base_chat_pipeline import ParagraphPipelineModel
+from application.chat_pipeline.step.search_dataset_step.i_search_dataset_step import ISearchDatasetStep
+from common.config.embedding_config import VectorStore, ModelManage
+from common.db.search import native_search
+from common.util.file_util import get_file_content
+from dataset.models import Paragraph, DataSet
+from embedding.models import SearchMode
+from setting.models import Model
+from setting.models_provider import get_model
+from smartdoc.conf import PROJECT_DIR
+
+
+def get_model_by_id(_id, user_id):
+    model = QuerySet(Model).filter(id=_id).first()
+    if model is None:
+        raise Exception(_("Model does not exist"))
+    if model.permission_type == 'PRIVATE' and str(model.user_id) != str(user_id):
+        message = lazy_format(_('No permission to use this model {model_name}'), model_name=model.name)
+        raise Exception(message)
+    return model
+
+
+def get_embedding_id(dataset_id_list):
+    dataset_list = QuerySet(DataSet).filter(id__in=dataset_id_list)
+    if len(set([dataset.embedding_mode_id for dataset in dataset_list])) > 1:
+        raise Exception(_("The vector model of the associated knowledge base is inconsistent and the segmentation cannot be recalled."))
+    if len(dataset_list) == 0:
+        raise Exception(_("The knowledge base setting is wrong, please reset the knowledge base"))
+    return dataset_list[0].embedding_mode_id
+
+
+class BaseSearchDatasetStep(ISearchDatasetStep):
+
+    def execute(self, problem_text: str, dataset_id_list: list[str], exclude_document_id_list: list[str],
+                exclude_paragraph_id_list: list[str], top_n: int, similarity: float, padding_problem_text: str = None,
+                search_mode: str = None,
+                user_id=None,
+                **kwargs) -> List[ParagraphPipelineModel]:
+        if len(dataset_id_list) == 0:
+            return []
+        exec_problem_text = padding_problem_text if padding_problem_text is not None else problem_text
+        model_id = get_embedding_id(dataset_id_list)
+        model = get_model_by_id(model_id, user_id)
+        self.context['model_name'] = model.name
+        embedding_model = ModelManage.get_model(model_id, lambda _id: get_model(model))
+        embedding_value = embedding_model.embed_query(exec_problem_text)
+        vector = VectorStore.get_embedding_vector()
+        embedding_list = vector.query(exec_problem_text, embedding_value, dataset_id_list, exclude_document_id_list,
+                                      exclude_paragraph_id_list, True, top_n, similarity, SearchMode(search_mode))
+        if embedding_list is None:
+            return []
+        paragraph_list = self.list_paragraph(embedding_list, vector)
+        result = [self.reset_paragraph(paragraph, embedding_list) for paragraph in paragraph_list]
+        return result
+
+    @staticmethod
+    def reset_paragraph(paragraph: Dict, embedding_list: List) -> ParagraphPipelineModel:
+        filter_embedding_list = [embedding for embedding in embedding_list if
+                                 str(embedding.get('paragraph_id')) == str(paragraph.get('id'))]
+        if filter_embedding_list is not None and len(filter_embedding_list) > 0:
+            find_embedding = filter_embedding_list[-1]
+            return (ParagraphPipelineModel.builder()
+                    .add_paragraph(paragraph)
+                    .add_similarity(find_embedding.get('similarity'))
+                    .add_comprehensive_score(find_embedding.get('comprehensive_score'))
+                    .add_dataset_name(paragraph.get('dataset_name'))
+                    .add_document_name(paragraph.get('document_name'))
+                    .add_hit_handling_method(paragraph.get('hit_handling_method'))
+                    .add_directly_return_similarity(paragraph.get('directly_return_similarity'))
+                    .add_meta(paragraph.get('meta'))
+                    .build())
+
+    @staticmethod
+    def get_similarity(paragraph, embedding_list: List):
+        filter_embedding_list = [embedding for embedding in embedding_list if
+                                 str(embedding.get('paragraph_id')) == str(paragraph.get('id'))]
+        if filter_embedding_list is not None and len(filter_embedding_list) > 0:
+            find_embedding = filter_embedding_list[-1]
+            return find_embedding.get('comprehensive_score')
+        return 0
+
+    @staticmethod
+    def list_paragraph(embedding_list: List, vector):
+        paragraph_id_list = [row.get('paragraph_id') for row in embedding_list]
+        if paragraph_id_list is None or len(paragraph_id_list) == 0:
+            return []
+        paragraph_list = native_search(QuerySet(Paragraph).filter(id__in=paragraph_id_list),
+                                       get_file_content(
+                                           os.path.join(PROJECT_DIR, "apps", "application", 'sql',
+                                                        'list_dataset_paragraph_by_paragraph_id.sql')),
+                                       with_table_name=True)
+        # 如果向量库中存在脏数据 直接删除
+        if len(paragraph_list) != len(paragraph_id_list):
+            exist_paragraph_list = [row.get('id') for row in paragraph_list]
+            for paragraph_id in paragraph_id_list:
+                if not exist_paragraph_list.__contains__(paragraph_id):
+                    vector.delete_by_paragraph_id(paragraph_id)
+        # 如果存在直接返回的则取直接返回段落
+        hit_handling_method_paragraph = [paragraph for paragraph in paragraph_list if
+                                         (paragraph.get(
+                                             'hit_handling_method') == 'directly_return' and BaseSearchDatasetStep.get_similarity(
+                                             paragraph, embedding_list) >= paragraph.get(
+                                             'directly_return_similarity'))]
+        if len(hit_handling_method_paragraph) > 0:
+            # 找到评分最高的
+            return [sorted(hit_handling_method_paragraph,
+                           key=lambda p: BaseSearchDatasetStep.get_similarity(p, embedding_list))[-1]]
+        return paragraph_list
+
+    def get_details(self, manage, **kwargs):
+        step_args = self.context['step_args']
+
+        return {
+            'step_type': 'search_step',
+            'paragraph_list': [row.to_dict() for row in self.context['paragraph_list']],
+            'run_time': self.context['run_time'],
+            'problem_text': step_args.get(
+                'padding_problem_text') if 'padding_problem_text' in step_args else step_args.get('problem_text'),
+            'model_name': self.context.get('model_name'),
+            'message_tokens': 0,
+            'answer_tokens': 0,
+            'cost': 0
+        }

apps/application/flow/__init__.py

@@ -0,0 +1,8 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/6/7 14:43
+    @desc:
+"""

apps/application/flow/common.py

@@ -0,0 +1,44 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： common.py
+    @date：2024/12/11 17:57
+    @desc:
+"""
+
+
+class Answer:
+    def __init__(self, content, view_type, runtime_node_id, chat_record_id, child_node, real_node_id,
+                 reasoning_content):
+        self.view_type = view_type
+        self.content = content
+        self.reasoning_content = reasoning_content
+        self.runtime_node_id = runtime_node_id
+        self.chat_record_id = chat_record_id
+        self.child_node = child_node
+        self.real_node_id = real_node_id
+
+    def to_dict(self):
+        return {'view_type': self.view_type, 'content': self.content, 'runtime_node_id': self.runtime_node_id,
+                'chat_record_id': self.chat_record_id,
+                'child_node': self.child_node,
+                'reasoning_content': self.reasoning_content,
+                'real_node_id': self.real_node_id}
+
+
+class NodeChunk:
+    def __init__(self):
+        self.status = 0
+        self.chunk_list = []
+
+    def add_chunk(self, chunk):
+        self.chunk_list.append(chunk)
+
+    def end(self, chunk=None):
+        if chunk is not None:
+            self.add_chunk(chunk)
+        self.status = 200
+
+    def is_end(self):
+        return self.status == 200

apps/application/flow/default_workflow.json

@@ -0,0 +1,451 @@
+{
+    "nodes": [
+        {
+            "id": "base-node",
+            "type": "base-node",
+            "x": 360,
+            "y": 2810,
+            "properties": {
+                "config": {
+
+                },
+                "height": 825.6,
+                "stepName": "基本信息",
+                "node_data": {
+                    "desc": "",
+                    "name": "maxkbapplication",
+                    "prologue": "您好，我是 MaxKB 小助手，您可以向我提出 MaxKB 使用问题。\n- MaxKB 主要功能有什么？\n- MaxKB 支持哪些大语言模型？\n- MaxKB 支持哪些文档类型？"
+                },
+                "input_field_list": [
+
+                ]
+            }
+        },
+        {
+            "id": "start-node",
+            "type": "start-node",
+            "x": 430,
+            "y": 3660,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "用户问题",
+                            "value": "question"
+                        }
+                    ],
+                    "globalFields": [
+                        {
+                            "label": "当前时间",
+                            "value": "time"
+                        }
+                    ]
+                },
+                "fields": [
+                    {
+                        "label": "用户问题",
+                        "value": "question"
+                    }
+                ],
+                "height": 276,
+                "stepName": "开始",
+                "globalFields": [
+                    {
+                        "label": "当前时间",
+                        "value": "time"
+                    }
+                ]
+            }
+        },
+        {
+            "id": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+            "type": "search-dataset-node",
+            "x": 840,
+            "y": 3210,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "检索结果的分段列表",
+                            "value": "paragraph_list"
+                        },
+                        {
+                            "label": "满足直接回答的分段列表",
+                            "value": "is_hit_handling_method_list"
+                        },
+                        {
+                            "label": "检索结果",
+                            "value": "data"
+                        },
+                        {
+                            "label": "满足直接回答的分段内容",
+                            "value": "directly_return"
+                        }
+                    ]
+                },
+                "height": 794,
+                "stepName": "知识库检索",
+                "node_data": {
+                    "dataset_id_list": [
+
+                    ],
+                    "dataset_setting": {
+                        "top_n": 3,
+                        "similarity": 0.6,
+                        "search_mode": "embedding",
+                        "max_paragraph_char_number": 5000
+                    },
+                    "question_reference_address": [
+                        "start-node",
+                        "question"
+                    ],
+                    "source_dataset_id_list": [
+
+                    ]
+                }
+            }
+        },
+        {
+            "id": "fc60863a-dec2-4854-9e5a-7a44b7187a2b",
+            "type": "condition-node",
+            "x": 1490,
+            "y": 3210,
+            "properties": {
+                "width": 600,
+                "config": {
+                    "fields": [
+                        {
+                            "label": "分支名称",
+                            "value": "branch_name"
+                        }
+                    ]
+                },
+                "height": 543.675,
+                "stepName": "判断器",
+                "node_data": {
+                    "branch": [
+                        {
+                            "id": "1009",
+                            "type": "IF",
+                            "condition": "and",
+                            "conditions": [
+                                {
+                                    "field": [
+                                        "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+                                        "is_hit_handling_method_list"
+                                    ],
+                                    "value": "1",
+                                    "compare": "len_ge"
+                                }
+                            ]
+                        },
+                        {
+                            "id": "4908",
+                            "type": "ELSE IF 1",
+                            "condition": "and",
+                            "conditions": [
+                                {
+                                    "field": [
+                                        "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+                                        "paragraph_list"
+                                    ],
+                                    "value": "1",
+                                    "compare": "len_ge"
+                                }
+                            ]
+                        },
+                        {
+                            "id": "161",
+                            "type": "ELSE",
+                            "condition": "and",
+                            "conditions": [
+
+                            ]
+                        }
+                    ]
+                },
+                "branch_condition_list": [
+                    {
+                        "index": 0,
+                        "height": 121.225,
+                        "id": "1009"
+                    },
+                    {
+                        "index": 1,
+                        "height": 121.225,
+                        "id": "4908"
+                    },
+                    {
+                        "index": 2,
+                        "height": 44,
+                        "id": "161"
+                    }
+                ]
+            }
+        },
+        {
+            "id": "4ffe1086-25df-4c85-b168-979b5bbf0a26",
+            "type": "reply-node",
+            "x": 2170,
+            "y": 2480,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "内容",
+                            "value": "answer"
+                        }
+                    ]
+                },
+                "height": 378,
+                "stepName": "指定回复",
+                "node_data": {
+                    "fields": [
+                        "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+                        "directly_return"
+                    ],
+                    "content": "",
+                    "reply_type": "referencing",
+                    "is_result": true
+                }
+            }
+        },
+        {
+            "id": "f1f1ee18-5a02-46f6-b4e6-226253cdffbb",
+            "type": "ai-chat-node",
+            "x": 2160,
+            "y": 3200,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "AI 回答内容",
+                            "value": "answer"
+                        }
+                    ]
+                },
+                "height": 763,
+                "stepName": "AI 对话",
+                "node_data": {
+                    "prompt": "已知信息：\n{{知识库检索.data}}\n问题：\n{{开始.question}}",
+                    "system": "",
+                    "model_id": "",
+                    "dialogue_number": 0,
+                    "is_result": true
+                }
+            }
+        },
+        {
+            "id": "309d0eef-c597-46b5-8d51-b9a28aaef4c7",
+            "type": "ai-chat-node",
+            "x": 2160,
+            "y": 3970,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "AI 回答内容",
+                            "value": "answer"
+                        }
+                    ]
+                },
+                "height": 763,
+                "stepName": "AI 对话1",
+                "node_data": {
+                    "prompt": "{{开始.question}}",
+                    "system": "",
+                    "model_id": "",
+                    "dialogue_number": 0,
+                    "is_result": true
+                }
+            }
+        }
+    ],
+    "edges": [
+        {
+            "id": "7d0f166f-c472-41b2-b9a2-c294f4c83d73",
+            "type": "app-edge",
+            "sourceNodeId": "start-node",
+            "targetNodeId": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+            "startPoint": {
+                "x": 590,
+                "y": 3660
+            },
+            "endPoint": {
+                "x": 680,
+                "y": 3210
+            },
+            "properties": {
+
+            },
+            "pointsList": [
+                {
+                    "x": 590,
+                    "y": 3660
+                },
+                {
+                    "x": 700,
+                    "y": 3660
+                },
+                {
+                    "x": 570,
+                    "y": 3210
+                },
+                {
+                    "x": 680,
+                    "y": 3210
+                }
+            ],
+            "sourceAnchorId": "start-node_right",
+            "targetAnchorId": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5_left"
+        },
+        {
+            "id": "35cb86dd-f328-429e-a973-12fd7218b696",
+            "type": "app-edge",
+            "sourceNodeId": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+            "targetNodeId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b",
+            "startPoint": {
+                "x": 1000,
+                "y": 3210
+            },
+            "endPoint": {
+                "x": 1200,
+                "y": 3210
+            },
+            "properties": {
+
+            },
+            "pointsList": [
+                {
+                    "x": 1000,
+                    "y": 3210
+                },
+                {
+                    "x": 1110,
+                    "y": 3210
+                },
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+                    "x": 1090,
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+                },
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+                    "y": 3210
+                }
+            ],
+            "sourceAnchorId": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5_right",
+            "targetAnchorId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b_left"
+        },
+        {
+            "id": "e8f6cfe6-7e48-41cd-abd3-abfb5304d0d8",
+            "type": "app-edge",
+            "sourceNodeId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b",
+            "targetNodeId": "4ffe1086-25df-4c85-b168-979b5bbf0a26",
+            "startPoint": {
+                "x": 1780,
+                "y": 3073.775
+            },
+            "endPoint": {
+                "x": 2010,
+                "y": 2480
+            },
+            "properties": {
+
+            },
+            "pointsList": [
+                {
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+                    "y": 3073.775
+                },
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+                    "y": 3073.775
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+                    "y": 2480
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+                    "y": 2480
+                }
+            ],
+            "sourceAnchorId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b_1009_right",
+            "targetAnchorId": "4ffe1086-25df-4c85-b168-979b5bbf0a26_left"
+        },
+        {
+            "id": "994ff325-6f7a-4ebc-b61b-10e15519d6d2",
+            "type": "app-edge",
+            "sourceNodeId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b",
+            "targetNodeId": "f1f1ee18-5a02-46f6-b4e6-226253cdffbb",
+            "startPoint": {
+                "x": 1780,
+                "y": 3203
+            },
+            "endPoint": {
+                "x": 2000,
+                "y": 3200
+            },
+            "properties": {
+
+            },
+            "pointsList": [
+                {
+                    "x": 1780,
+                    "y": 3203
+                },
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+                    "x": 1890,
+                    "y": 3203
+                },
+                {
+                    "x": 1890,
+                    "y": 3200
+                },
+                {
+                    "x": 2000,
+                    "y": 3200
+                }
+            ],
+            "sourceAnchorId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b_4908_right",
+            "targetAnchorId": "f1f1ee18-5a02-46f6-b4e6-226253cdffbb_left"
+        },
+        {
+            "id": "19270caf-bb9f-4ba7-9bf8-200aa70fecd5",
+            "type": "app-edge",
+            "sourceNodeId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b",
+            "targetNodeId": "309d0eef-c597-46b5-8d51-b9a28aaef4c7",
+            "startPoint": {
+                "x": 1780,
+                "y": 3293.6124999999997
+            },
+            "endPoint": {
+                "x": 2000,
+                "y": 3970
+            },
+            "properties": {
+
+            },
+            "pointsList": [
+                {
+                    "x": 1780,
+                    "y": 3293.6124999999997
+                },
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+                    "y": 3293.6124999999997
+                },
+                {
+                    "x": 1890,
+                    "y": 3970
+                },
+                {
+                    "x": 2000,
+                    "y": 3970
+                }
+            ],
+            "sourceAnchorId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b_161_right",
+            "targetAnchorId": "309d0eef-c597-46b5-8d51-b9a28aaef4c7_left"
+        }
+    ]
+}

apps/application/flow/default_workflow_en.json

@@ -0,0 +1,451 @@
+{
+    "nodes": [
+        {
+            "id": "base-node",
+            "type": "base-node",
+            "x": 360,
+            "y": 2810,
+            "properties": {
+                "config": {
+
+                },
+                "height": 825.6,
+                "stepName": "Base",
+                "node_data": {
+                    "desc": "",
+                    "name": "maxkbapplication",
+                    "prologue": "Hello, I am the MaxKB assistant. You can ask me about MaxKB usage issues.\n-What are the main functions of MaxKB?\n-What major language models does MaxKB support?\n-What document types does MaxKB support?"
+                },
+                "input_field_list": [
+
+                ]
+            }
+        },
+        {
+            "id": "start-node",
+            "type": "start-node",
+            "x": 430,
+            "y": 3660,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "用户问题",
+                            "value": "question"
+                        }
+                    ],
+                    "globalFields": [
+                        {
+                            "label": "当前时间",
+                            "value": "time"
+                        }
+                    ]
+                },
+                "fields": [
+                    {
+                        "label": "用户问题",
+                        "value": "question"
+                    }
+                ],
+                "height": 276,
+                "stepName": "Start",
+                "globalFields": [
+                    {
+                        "label": "当前时间",
+                        "value": "time"
+                    }
+                ]
+            }
+        },
+        {
+            "id": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+            "type": "search-dataset-node",
+            "x": 840,
+            "y": 3210,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "检索结果的分段列表",
+                            "value": "paragraph_list"
+                        },
+                        {
+                            "label": "满足直接回答的分段列表",
+                            "value": "is_hit_handling_method_list"
+                        },
+                        {
+                            "label": "检索结果",
+                            "value": "data"
+                        },
+                        {
+                            "label": "满足直接回答的分段内容",
+                            "value": "directly_return"
+                        }
+                    ]
+                },
+                "height": 794,
+                "stepName": "Knowledge Search",
+                "node_data": {
+                    "dataset_id_list": [
+
+                    ],
+                    "dataset_setting": {
+                        "top_n": 3,
+                        "similarity": 0.6,
+                        "search_mode": "embedding",
+                        "max_paragraph_char_number": 5000
+                    },
+                    "question_reference_address": [
+                        "start-node",
+                        "question"
+                    ],
+                    "source_dataset_id_list": [
+
+                    ]
+                }
+            }
+        },
+        {
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+            "targetAnchorId": "309d0eef-c597-46b5-8d51-b9a28aaef4c7_left"
+        }
+    ]
+}

apps/application/flow/default_workflow_zh_Hant.json

@@ -0,0 +1,451 @@
+{
+    "nodes": [
+        {
+            "id": "base-node",
+            "type": "base-node",
+            "x": 360,
+            "y": 2810,
+            "properties": {
+                "config": {
+
+                },
+                "height": 825.6,
+                "stepName": "基本資訊",
+                "node_data": {
+                    "desc": "",
+                    "name": "maxkbapplication",
+                    "prologue": "您好，我是MaxKB小助手，您可以向我提出MaxKB使用問題。\n- MaxKB主要功能有什麼？\n- MaxKB支持哪些大語言模型？\n- MaxKB支持哪些文檔類型？"
+                },
+                "input_field_list": [
+
+                ]
+            }
+        },
+        {
+            "id": "start-node",
+            "type": "start-node",
+            "x": 430,
+            "y": 3660,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "用户问题",
+                            "value": "question"
+                        }
+                    ],
+                    "globalFields": [
+                        {
+                            "label": "当前时间",
+                            "value": "time"
+                        }
+                    ]
+                },
+                "fields": [
+                    {
+                        "label": "用户问题",
+                        "value": "question"
+                    }
+                ],
+                "height": 276,
+                "stepName": "開始",
+                "globalFields": [
+                    {
+                        "label": "当前时间",
+                        "value": "time"
+                    }
+                ]
+            }
+        },
+        {
+            "id": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+            "type": "search-dataset-node",
+            "x": 840,
+            "y": 3210,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "检索结果的分段列表",
+                            "value": "paragraph_list"
+                        },
+                        {
+                            "label": "满足直接回答的分段列表",
+                            "value": "is_hit_handling_method_list"
+                        },
+                        {
+                            "label": "检索结果",
+                            "value": "data"
+                        },
+                        {
+                            "label": "满足直接回答的分段内容",
+                            "value": "directly_return"
+                        }
+                    ]
+                },
+                "height": 794,
+                "stepName": "知識庫檢索",
+                "node_data": {
+                    "dataset_id_list": [
+
+                    ],
+                    "dataset_setting": {
+                        "top_n": 3,
+                        "similarity": 0.6,
+                        "search_mode": "embedding",
+                        "max_paragraph_char_number": 5000
+                    },
+                    "question_reference_address": [
+                        "start-node",
+                        "question"
+                    ],
+                    "source_dataset_id_list": [
+
+                    ]
+                }
+            }
+        },
+        {
+            "id": "fc60863a-dec2-4854-9e5a-7a44b7187a2b",
+            "type": "condition-node",
+            "x": 1490,
+            "y": 3210,
+            "properties": {
+                "width": 600,
+                "config": {
+                    "fields": [
+                        {
+                            "label": "分支名称",
+                            "value": "branch_name"
+                        }
+                    ]
+                },
+                "height": 543.675,
+                "stepName": "判斷器",
+                "node_data": {
+                    "branch": [
+                        {
+                            "id": "1009",
+                            "type": "IF",
+                            "condition": "and",
+                            "conditions": [
+                                {
+                                    "field": [
+                                        "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+                                        "is_hit_handling_method_list"
+                                    ],
+                                    "value": "1",
+                                    "compare": "len_ge"
+                                }
+                            ]
+                        },
+                        {
+                            "id": "4908",
+                            "type": "ELSE IF 1",
+                            "condition": "and",
+                            "conditions": [
+                                {
+                                    "field": [
+                                        "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+                                        "paragraph_list"
+                                    ],
+                                    "value": "1",
+                                    "compare": "len_ge"
+                                }
+                            ]
+                        },
+                        {
+                            "id": "161",
+                            "type": "ELSE",
+                            "condition": "and",
+                            "conditions": [
+
+                            ]
+                        }
+                    ]
+                },
+                "branch_condition_list": [
+                    {
+                        "index": 0,
+                        "height": 121.225,
+                        "id": "1009"
+                    },
+                    {
+                        "index": 1,
+                        "height": 121.225,
+                        "id": "4908"
+                    },
+                    {
+                        "index": 2,
+                        "height": 44,
+                        "id": "161"
+                    }
+                ]
+            }
+        },
+        {
+            "id": "4ffe1086-25df-4c85-b168-979b5bbf0a26",
+            "type": "reply-node",
+            "x": 2170,
+            "y": 2480,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "内容",
+                            "value": "answer"
+                        }
+                    ]
+                },
+                "height": 378,
+                "stepName": "指定回覆",
+                "node_data": {
+                    "fields": [
+                        "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+                        "directly_return"
+                    ],
+                    "content": "",
+                    "reply_type": "referencing",
+                    "is_result": true
+                }
+            }
+        },
+        {
+            "id": "f1f1ee18-5a02-46f6-b4e6-226253cdffbb",
+            "type": "ai-chat-node",
+            "x": 2160,
+            "y": 3200,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "AI 回答内容",
+                            "value": "answer"
+                        }
+                    ]
+                },
+                "height": 763,
+                "stepName": "AI 對話",
+                "node_data": {
+                    "prompt": "已知資訊：\n{{知識庫檢索.data}}\n問題：\n{{開始.question}}",
+                    "system": "",
+                    "model_id": "",
+                    "dialogue_number": 0,
+                    "is_result": true
+                }
+            }
+        },
+        {
+            "id": "309d0eef-c597-46b5-8d51-b9a28aaef4c7",
+            "type": "ai-chat-node",
+            "x": 2160,
+            "y": 3970,
+            "properties": {
+                "config": {
+                    "fields": [
+                        {
+                            "label": "AI 回答内容",
+                            "value": "answer"
+                        }
+                    ]
+                },
+                "height": 763,
+                "stepName": "AI 對話1",
+                "node_data": {
+                    "prompt": "{{開始.question}}",
+                    "system": "",
+                    "model_id": "",
+                    "dialogue_number": 0,
+                    "is_result": true
+                }
+            }
+        }
+    ],
+    "edges": [
+        {
+            "id": "7d0f166f-c472-41b2-b9a2-c294f4c83d73",
+            "type": "app-edge",
+            "sourceNodeId": "start-node",
+            "targetNodeId": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+            "startPoint": {
+                "x": 590,
+                "y": 3660
+            },
+            "endPoint": {
+                "x": 680,
+                "y": 3210
+            },
+            "properties": {
+
+            },
+            "pointsList": [
+                {
+                    "x": 590,
+                    "y": 3660
+                },
+                {
+                    "x": 700,
+                    "y": 3660
+                },
+                {
+                    "x": 570,
+                    "y": 3210
+                },
+                {
+                    "x": 680,
+                    "y": 3210
+                }
+            ],
+            "sourceAnchorId": "start-node_right",
+            "targetAnchorId": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5_left"
+        },
+        {
+            "id": "35cb86dd-f328-429e-a973-12fd7218b696",
+            "type": "app-edge",
+            "sourceNodeId": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5",
+            "targetNodeId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b",
+            "startPoint": {
+                "x": 1000,
+                "y": 3210
+            },
+            "endPoint": {
+                "x": 1200,
+                "y": 3210
+            },
+            "properties": {
+
+            },
+            "pointsList": [
+                {
+                    "x": 1000,
+                    "y": 3210
+                },
+                {
+                    "x": 1110,
+                    "y": 3210
+                },
+                {
+                    "x": 1090,
+                    "y": 3210
+                },
+                {
+                    "x": 1200,
+                    "y": 3210
+                }
+            ],
+            "sourceAnchorId": "b931efe5-5b66-46e0-ae3b-0160cb18eeb5_right",
+            "targetAnchorId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b_left"
+        },
+        {
+            "id": "e8f6cfe6-7e48-41cd-abd3-abfb5304d0d8",
+            "type": "app-edge",
+            "sourceNodeId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b",
+            "targetNodeId": "4ffe1086-25df-4c85-b168-979b5bbf0a26",
+            "startPoint": {
+                "x": 1780,
+                "y": 3073.775
+            },
+            "endPoint": {
+                "x": 2010,
+                "y": 2480
+            },
+            "properties": {
+
+            },
+            "pointsList": [
+                {
+                    "x": 1780,
+                    "y": 3073.775
+                },
+                {
+                    "x": 1890,
+                    "y": 3073.775
+                },
+                {
+                    "x": 1900,
+                    "y": 2480
+                },
+                {
+                    "x": 2010,
+                    "y": 2480
+                }
+            ],
+            "sourceAnchorId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b_1009_right",
+            "targetAnchorId": "4ffe1086-25df-4c85-b168-979b5bbf0a26_left"
+        },
+        {
+            "id": "994ff325-6f7a-4ebc-b61b-10e15519d6d2",
+            "type": "app-edge",
+            "sourceNodeId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b",
+            "targetNodeId": "f1f1ee18-5a02-46f6-b4e6-226253cdffbb",
+            "startPoint": {
+                "x": 1780,
+                "y": 3203
+            },
+            "endPoint": {
+                "x": 2000,
+                "y": 3200
+            },
+            "properties": {
+
+            },
+            "pointsList": [
+                {
+                    "x": 1780,
+                    "y": 3203
+                },
+                {
+                    "x": 1890,
+                    "y": 3203
+                },
+                {
+                    "x": 1890,
+                    "y": 3200
+                },
+                {
+                    "x": 2000,
+                    "y": 3200
+                }
+            ],
+            "sourceAnchorId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b_4908_right",
+            "targetAnchorId": "f1f1ee18-5a02-46f6-b4e6-226253cdffbb_left"
+        },
+        {
+            "id": "19270caf-bb9f-4ba7-9bf8-200aa70fecd5",
+            "type": "app-edge",
+            "sourceNodeId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b",
+            "targetNodeId": "309d0eef-c597-46b5-8d51-b9a28aaef4c7",
+            "startPoint": {
+                "x": 1780,
+                "y": 3293.6124999999997
+            },
+            "endPoint": {
+                "x": 2000,
+                "y": 3970
+            },
+            "properties": {
+
+            },
+            "pointsList": [
+                {
+                    "x": 1780,
+                    "y": 3293.6124999999997
+                },
+                {
+                    "x": 1890,
+                    "y": 3293.6124999999997
+                },
+                {
+                    "x": 1890,
+                    "y": 3970
+                },
+                {
+                    "x": 2000,
+                    "y": 3970
+                }
+            ],
+            "sourceAnchorId": "fc60863a-dec2-4854-9e5a-7a44b7187a2b_161_right",
+            "targetAnchorId": "309d0eef-c597-46b5-8d51-b9a28aaef4c7_left"
+        }
+    ]
+}

apps/application/flow/i_step_node.py

@@ -0,0 +1,256 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： i_step_node.py
+    @date：2024/6/3 14:57
+    @desc:
+"""
+import time
+import uuid
+from abc import abstractmethod
+from hashlib import sha1
+from typing import Type, Dict, List
+
+from django.core import cache
+from django.db.models import QuerySet
+from rest_framework import serializers
+from rest_framework.exceptions import ValidationError, ErrorDetail
+
+from application.flow.common import Answer, NodeChunk
+from application.models import ChatRecord
+from application.models.api_key_model import ApplicationPublicAccessClient
+from common.constants.authentication_type import AuthenticationType
+from common.field.common import InstanceField
+from common.util.field_message import ErrMessage
+
+chat_cache = cache.caches['chat_cache']
+
+
+def write_context(step_variable: Dict, global_variable: Dict, node, workflow):
+    if step_variable is not None:
+        for key in step_variable:
+            node.context[key] = step_variable[key]
+        if workflow.is_result(node, NodeResult(step_variable, global_variable)) and 'answer' in step_variable:
+            answer = step_variable['answer']
+            yield answer
+            node.answer_text = answer
+    if global_variable is not None:
+        for key in global_variable:
+            workflow.context[key] = global_variable[key]
+    node.context['run_time'] = time.time() - node.context['start_time']
+
+
+def is_interrupt(node, step_variable: Dict, global_variable: Dict):
+    return node.type == 'form-node' and not node.context.get('is_submit', False)
+
+
+class WorkFlowPostHandler:
+    def __init__(self, chat_info, client_id, client_type):
+        self.chat_info = chat_info
+        self.client_id = client_id
+        self.client_type = client_type
+
+    def handler(self, chat_id,
+                chat_record_id,
+                answer,
+                workflow):
+        question = workflow.params['question']
+        details = workflow.get_runtime_details()
+        message_tokens = sum([row.get('message_tokens') for row in details.values() if
+                              'message_tokens' in row and row.get('message_tokens') is not None])
+        answer_tokens = sum([row.get('answer_tokens') for row in details.values() if
+                             'answer_tokens' in row and row.get('answer_tokens') is not None])
+        answer_text_list = workflow.get_answer_text_list()
+        answer_text = '\n\n'.join(
+            '\n\n'.join([a.get('content') for a in answer]) for answer in
+            answer_text_list)
+        if workflow.chat_record is not None:
+            chat_record = workflow.chat_record
+            chat_record.answer_text = answer_text
+            chat_record.details = details
+            chat_record.message_tokens = message_tokens
+            chat_record.answer_tokens = answer_tokens
+            chat_record.answer_text_list = answer_text_list
+            chat_record.run_time = time.time() - workflow.context['start_time']
+        else:
+            chat_record = ChatRecord(id=chat_record_id,
+                                     chat_id=chat_id,
+                                     problem_text=question,
+                                     answer_text=answer_text,
+                                     details=details,
+                                     message_tokens=message_tokens,
+                                     answer_tokens=answer_tokens,
+                                     answer_text_list=answer_text_list,
+                                     run_time=time.time() - workflow.context['start_time'],
+                                     index=0)
+        asker = workflow.context.get('asker', None)
+        self.chat_info.append_chat_record(chat_record, self.client_id, asker)
+        # 重新设置缓存
+        chat_cache.set(chat_id,
+                       self.chat_info, timeout=60 * 30)
+        if self.client_type == AuthenticationType.APPLICATION_ACCESS_TOKEN.value:
+            application_public_access_client = (QuerySet(ApplicationPublicAccessClient)
+                                                .filter(client_id=self.client_id,
+                                                        application_id=self.chat_info.application.id).first())
+            if application_public_access_client is not None:
+                application_public_access_client.access_num = application_public_access_client.access_num + 1
+                application_public_access_client.intraday_access_num = application_public_access_client.intraday_access_num + 1
+                application_public_access_client.save()
+
+
+class NodeResult:
+    def __init__(self, node_variable: Dict, workflow_variable: Dict,
+                 _write_context=write_context, _is_interrupt=is_interrupt):
+        self._write_context = _write_context
+        self.node_variable = node_variable
+        self.workflow_variable = workflow_variable
+        self._is_interrupt = _is_interrupt
+
+    def write_context(self, node, workflow):
+        return self._write_context(self.node_variable, self.workflow_variable, node, workflow)
+
+    def is_assertion_result(self):
+        return 'branch_id' in self.node_variable
+
+    def is_interrupt_exec(self, current_node):
+        """
+        是否中断执行
+        @param current_node:
+        @return:
+        """
+        return self._is_interrupt(current_node, self.node_variable, self.workflow_variable)
+
+
+class ReferenceAddressSerializer(serializers.Serializer):
+    node_id = serializers.CharField(required=True, error_messages=ErrMessage.char("节点id"))
+    fields = serializers.ListField(
+        child=serializers.CharField(required=True, error_messages=ErrMessage.char("节点字段")), required=True,
+        error_messages=ErrMessage.list("节点字段数组"))
+
+
+class FlowParamsSerializer(serializers.Serializer):
+    # 历史对答
+    history_chat_record = serializers.ListField(child=InstanceField(model_type=ChatRecord, required=True),
+                                                error_messages=ErrMessage.list("历史对答"))
+
+    question = serializers.CharField(required=True, error_messages=ErrMessage.list("用户问题"))
+
+    chat_id = serializers.CharField(required=True, error_messages=ErrMessage.list("对话id"))
+
+    chat_record_id = serializers.CharField(required=True, error_messages=ErrMessage.char("对话记录id"))
+
+    stream = serializers.BooleanField(required=True, error_messages=ErrMessage.boolean("流式输出"))
+
+    client_id = serializers.CharField(required=False, error_messages=ErrMessage.char("客户端id"))
+
+    client_type = serializers.CharField(required=False, error_messages=ErrMessage.char("客户端类型"))
+
+    user_id = serializers.UUIDField(required=True, error_messages=ErrMessage.uuid("用户id"))
+    re_chat = serializers.BooleanField(required=True, error_messages=ErrMessage.boolean("换个答案"))
+
+
+class INode:
+    view_type = 'many_view'
+
+    @abstractmethod
+    def save_context(self, details, workflow_manage):
+        pass
+
+    def get_answer_list(self) -> List[Answer] | None:
+        if self.answer_text is None:
+            return None
+        reasoning_content_enable = self.context.get('model_setting', {}).get('reasoning_content_enable', False)
+        return [
+            Answer(self.answer_text, self.view_type, self.runtime_node_id, self.workflow_params['chat_record_id'], {},
+                   self.runtime_node_id, self.context.get('reasoning_content', '') if reasoning_content_enable else '')]
+
+    def __init__(self, node, workflow_params, workflow_manage, up_node_id_list=None,
+                 get_node_params=lambda node: node.properties.get('node_data')):
+        # 当前步骤上下文,用于存储当前步骤信息
+        self.status = 200
+        self.err_message = ''
+        self.node = node
+        self.node_params = get_node_params(node)
+        self.workflow_params = workflow_params
+        self.workflow_manage = workflow_manage
+        self.node_params_serializer = None
+        self.flow_params_serializer = None
+        self.context = {}
+        self.answer_text = None
+        self.id = node.id
+        if up_node_id_list is None:
+            up_node_id_list = []
+        self.up_node_id_list = up_node_id_list
+        self.node_chunk = NodeChunk()
+        self.runtime_node_id = sha1(uuid.NAMESPACE_DNS.bytes + bytes(str(uuid.uuid5(uuid.NAMESPACE_DNS,
+                                                                                    "".join([*sorted(up_node_id_list),
+                                                                                             node.id]))),
+                                                                     "utf-8")).hexdigest()
+
+    def valid_args(self, node_params, flow_params):
+        flow_params_serializer_class = self.get_flow_params_serializer_class()
+        node_params_serializer_class = self.get_node_params_serializer_class()
+        if flow_params_serializer_class is not None and flow_params is not None:
+            self.flow_params_serializer = flow_params_serializer_class(data=flow_params)
+            self.flow_params_serializer.is_valid(raise_exception=True)
+        if node_params_serializer_class is not None:
+            self.node_params_serializer = node_params_serializer_class(data=node_params)
+            self.node_params_serializer.is_valid(raise_exception=True)
+        if self.node.properties.get('status', 200) != 200:
+            raise ValidationError(ErrorDetail(f'节点{self.node.properties.get("stepName")} 不可用'))
+
+    def get_reference_field(self, fields: List[str]):
+        return self.get_field(self.context, fields)
+
+    @staticmethod
+    def get_field(obj, fields: List[str]):
+        for field in fields:
+            value = obj.get(field)
+            if value is None:
+                return None
+            else:
+                obj = value
+        return obj
+
+    @abstractmethod
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        pass
+
+    def get_flow_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return FlowParamsSerializer
+
+    def get_write_error_context(self, e):
+        self.status = 500
+        self.answer_text = str(e)
+        self.err_message = str(e)
+        self.context['run_time'] = time.time() - self.context['start_time']
+
+        def write_error_context(answer, status=200):
+            pass
+
+        return write_error_context
+
+    def run(self) -> NodeResult:
+        """
+        :return: 执行结果
+        """
+        start_time = time.time()
+        self.context['start_time'] = start_time
+        result = self._run()
+        self.context['run_time'] = time.time() - start_time
+        return result
+
+    def _run(self):
+        result = self.execute()
+        return result
+
+    def execute(self, **kwargs) -> NodeResult:
+        pass
+
+    def get_details(self, index: int, **kwargs):
+        """
+        运行详情
+        :return: 步骤详情
+        """
+        return {}

apps/application/flow/step_node/__init__.py

@@ -0,0 +1,42 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/6/7 14:43
+    @desc:
+"""
+from .ai_chat_step_node import *
+from .application_node import BaseApplicationNode
+from .condition_node import *
+from .direct_reply_node import *
+from .form_node import *
+from .function_lib_node import *
+from .function_node import *
+from .question_node import *
+from .reranker_node import *
+
+from .document_extract_node import *
+from .image_understand_step_node import *
+from .image_generate_step_node import *
+
+from .search_dataset_node import *
+from .speech_to_text_step_node import BaseSpeechToTextNode
+from .start_node import *
+from .text_to_speech_step_node.impl.base_text_to_speech_node import BaseTextToSpeechNode
+from .variable_assign_node import BaseVariableAssignNode
+from .mcp_node import BaseMcpNode
+
+node_list = [BaseStartStepNode, BaseChatNode, BaseSearchDatasetNode, BaseQuestionNode,
+             BaseConditionNode, BaseReplyNode,
+             BaseFunctionNodeNode, BaseFunctionLibNodeNode, BaseRerankerNode, BaseApplicationNode,
+             BaseDocumentExtractNode,
+             BaseImageUnderstandNode, BaseFormNode, BaseSpeechToTextNode, BaseTextToSpeechNode,
+             BaseImageGenerateNode, BaseVariableAssignNode, BaseMcpNode]
+
+
+def get_node(node_type):
+    find_list = [node for node in node_list if node.type == node_type]
+    if len(find_list) > 0:
+        return find_list[0]
+    return None

apps/application/flow/step_node/ai_chat_step_node/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py
+    @date：2024/6/11 15:29
+    @desc:
+"""
+from .impl import *

apps/application/flow/step_node/ai_chat_step_node/i_chat_node.py

@@ -0,0 +1,58 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： i_chat_node.py
+    @date：2024/6/4 13:58
+    @desc:
+"""
+from typing import Type
+
+from django.utils.translation import gettext_lazy as _
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.field_message import ErrMessage
+
+
+class ChatNodeSerializer(serializers.Serializer):
+    model_id = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Model id")))
+    system = serializers.CharField(required=False, allow_blank=True, allow_null=True,
+                                   error_messages=ErrMessage.char(_("Role Setting")))
+    prompt = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Prompt word")))
+    # 多轮对话数量
+    dialogue_number = serializers.IntegerField(required=True, error_messages=ErrMessage.integer(
+        _("Number of multi-round conversations")))
+
+    is_result = serializers.BooleanField(required=False,
+                                         error_messages=ErrMessage.boolean(_('Whether to return content')))
+
+    model_params_setting = serializers.DictField(required=False,
+                                                 error_messages=ErrMessage.dict(_("Model parameter settings")))
+    model_setting = serializers.DictField(required=False,
+                                          error_messages=ErrMessage.dict('Model settings'))
+    dialogue_type = serializers.CharField(required=False, allow_blank=True, allow_null=True,
+                                          error_messages=ErrMessage.char(_("Context Type")))
+    mcp_enable = serializers.BooleanField(required=False,
+                                         error_messages=ErrMessage.boolean(_("Whether to enable MCP")))
+    mcp_servers = serializers.JSONField(required=False, error_messages=ErrMessage.list(_("MCP Server")))
+
+
+class IChatNode(INode):
+    type = 'ai-chat-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return ChatNodeSerializer
+
+    def _run(self):
+        return self.execute(**self.node_params_serializer.data, **self.flow_params_serializer.data)
+
+    def execute(self, model_id, system, prompt, dialogue_number, history_chat_record, stream, chat_id,
+                chat_record_id,
+                model_params_setting=None,
+                dialogue_type=None,
+                model_setting=None,
+                mcp_enable=False,
+                mcp_servers=None,
+                **kwargs) -> NodeResult:
+        pass

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

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py
+    @date：2024/6/11 15:34
+    @desc:
+"""
+from .base_chat_node import BaseChatNode

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

@@ -0,0 +1,288 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： base_question_node.py
+    @date：2024/6/4 14:30
+    @desc:
+"""
+import asyncio
+import json
+import re
+import time
+from functools import reduce
+from types import AsyncGeneratorType
+from typing import List, Dict
+
+from django.db.models import QuerySet
+from langchain.schema import HumanMessage, SystemMessage
+from langchain_core.messages import BaseMessage, AIMessage, AIMessageChunk, ToolMessage
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langgraph.prebuilt import create_react_agent
+
+from application.flow.i_step_node import NodeResult, INode
+from application.flow.step_node.ai_chat_step_node.i_chat_node import IChatNode
+from application.flow.tools import Reasoning
+from setting.models import Model
+from setting.models_provider import get_model_credential
+from setting.models_provider.tools import get_model_instance_by_model_user_id
+
+tool_message_template = """
+<details>
+    <summary>
+        <strong>Called MCP Tool: <em>%s</em></strong>
+    </summary>
+
+```json
+%s
+```
+</details>
+
+"""
+
+
+def _write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow, answer: str,
+                   reasoning_content: str):
+    chat_model = node_variable.get('chat_model')
+    message_tokens = chat_model.get_num_tokens_from_messages(node_variable.get('message_list'))
+    answer_tokens = chat_model.get_num_tokens(answer)
+    node.context['message_tokens'] = message_tokens
+    node.context['answer_tokens'] = answer_tokens
+    node.context['answer'] = answer
+    node.context['history_message'] = node_variable['history_message']
+    node.context['question'] = node_variable['question']
+    node.context['run_time'] = time.time() - node.context['start_time']
+    node.context['reasoning_content'] = reasoning_content
+    if workflow.is_result(node, NodeResult(node_variable, workflow_variable)):
+        node.answer_text = answer
+
+
+def write_context_stream(node_variable: Dict, workflow_variable: Dict, node: INode, workflow):
+    """
+    写入上下文数据 (流式)
+    @param node_variable:      节点数据
+    @param workflow_variable:  全局数据
+    @param node:               节点
+    @param workflow:           工作流管理器
+    """
+    response = node_variable.get('result')
+    answer = ''
+    reasoning_content = ''
+    model_setting = node.context.get('model_setting',
+                                     {'reasoning_content_enable': False, 'reasoning_content_end': '</think>',
+                                      'reasoning_content_start': '<think>'})
+    reasoning = Reasoning(model_setting.get('reasoning_content_start', '<think>'),
+                          model_setting.get('reasoning_content_end', '</think>'))
+    response_reasoning_content = False
+
+    for chunk in response:
+        reasoning_chunk = reasoning.get_reasoning_content(chunk)
+        content_chunk = reasoning_chunk.get('content')
+        if 'reasoning_content' in chunk.additional_kwargs:
+            response_reasoning_content = True
+            reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '')
+        else:
+            reasoning_content_chunk = reasoning_chunk.get('reasoning_content')
+        answer += content_chunk
+        if reasoning_content_chunk is None:
+            reasoning_content_chunk = ''
+        reasoning_content += reasoning_content_chunk
+        yield {'content': content_chunk,
+               'reasoning_content': reasoning_content_chunk if model_setting.get('reasoning_content_enable',
+                                                                                 False) else ''}
+
+    reasoning_chunk = reasoning.get_end_reasoning_content()
+    answer += reasoning_chunk.get('content')
+    reasoning_content_chunk = ""
+    if not response_reasoning_content:
+        reasoning_content_chunk = reasoning_chunk.get(
+            'reasoning_content')
+    yield {'content': reasoning_chunk.get('content'),
+           'reasoning_content': reasoning_content_chunk if model_setting.get('reasoning_content_enable',
+                                                                             False) else ''}
+    _write_context(node_variable, workflow_variable, node, workflow, answer, reasoning_content)
+
+
+async def _yield_mcp_response(chat_model, message_list, mcp_servers):
+    async with MultiServerMCPClient(json.loads(mcp_servers)) as client:
+        agent = create_react_agent(chat_model, client.get_tools())
+        response = agent.astream({"messages": message_list}, stream_mode='messages')
+        async for chunk in response:
+            if isinstance(chunk[0], ToolMessage):
+                content = tool_message_template % (chunk[0].name, chunk[0].content)
+                chunk[0].content = content
+                yield chunk[0]
+            if isinstance(chunk[0], AIMessageChunk):
+                yield chunk[0]
+
+
+def mcp_response_generator(chat_model, message_list, mcp_servers):
+    loop = asyncio.new_event_loop()
+    try:
+        async_gen = _yield_mcp_response(chat_model, message_list, mcp_servers)
+        while True:
+            try:
+                chunk = loop.run_until_complete(anext_async(async_gen))
+                yield chunk
+            except StopAsyncIteration:
+                break
+    except Exception as e:
+        print(f'exception: {e}')
+    finally:
+        loop.close()
+
+
+async def anext_async(agen):
+    return await agen.__anext__()
+
+
+def write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow):
+    """
+    写入上下文数据
+    @param node_variable:      节点数据
+    @param workflow_variable:  全局数据
+    @param node:               节点实例对象
+    @param workflow:           工作流管理器
+    """
+    response = node_variable.get('result')
+    model_setting = node.context.get('model_setting',
+                                     {'reasoning_content_enable': False, 'reasoning_content_end': '</think>',
+                                      'reasoning_content_start': '<think>'})
+    reasoning = Reasoning(model_setting.get('reasoning_content_start'), model_setting.get('reasoning_content_end'))
+    reasoning_result = reasoning.get_reasoning_content(response)
+    reasoning_result_end = reasoning.get_end_reasoning_content()
+    content = reasoning_result.get('content') + reasoning_result_end.get('content')
+    if 'reasoning_content' in response.response_metadata:
+        reasoning_content = response.response_metadata.get('reasoning_content', '')
+    else:
+        reasoning_content = reasoning_result.get('reasoning_content') + reasoning_result_end.get('reasoning_content')
+    _write_context(node_variable, workflow_variable, node, workflow, content, reasoning_content)
+
+
+def get_default_model_params_setting(model_id):
+    model = QuerySet(Model).filter(id=model_id).first()
+    credential = get_model_credential(model.provider, model.model_type, model.model_name)
+    model_params_setting = credential.get_model_params_setting_form(
+        model.model_name).get_default_form_data()
+    return model_params_setting
+
+
+def get_node_message(chat_record, runtime_node_id):
+    node_details = chat_record.get_node_details_runtime_node_id(runtime_node_id)
+    if node_details is None:
+        return []
+    return [HumanMessage(node_details.get('question')), AIMessage(node_details.get('answer'))]
+
+
+def get_workflow_message(chat_record):
+    return [chat_record.get_human_message(), chat_record.get_ai_message()]
+
+
+def get_message(chat_record, dialogue_type, runtime_node_id):
+    return get_node_message(chat_record, runtime_node_id) if dialogue_type == 'NODE' else get_workflow_message(
+        chat_record)
+
+
+class BaseChatNode(IChatNode):
+    def save_context(self, details, workflow_manage):
+        self.context['answer'] = details.get('answer')
+        self.context['question'] = details.get('question')
+        self.context['reasoning_content'] = details.get('reasoning_content')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
+
+    def execute(self, model_id, system, prompt, dialogue_number, history_chat_record, stream, chat_id, chat_record_id,
+                model_params_setting=None,
+                dialogue_type=None,
+                model_setting=None,
+                mcp_enable=False,
+                mcp_servers=None,
+                **kwargs) -> NodeResult:
+        if dialogue_type is None:
+            dialogue_type = 'WORKFLOW'
+
+        if model_params_setting is None:
+            model_params_setting = get_default_model_params_setting(model_id)
+        if model_setting is None:
+            model_setting = {'reasoning_content_enable': False, 'reasoning_content_end': '</think>',
+                             'reasoning_content_start': '<think>'}
+        self.context['model_setting'] = model_setting
+        chat_model = get_model_instance_by_model_user_id(model_id, self.flow_params_serializer.data.get('user_id'),
+                                                         **model_params_setting)
+        history_message = self.get_history_message(history_chat_record, dialogue_number, dialogue_type,
+                                                   self.runtime_node_id)
+        self.context['history_message'] = history_message
+        question = self.generate_prompt_question(prompt)
+        self.context['question'] = question.content
+        system = self.workflow_manage.generate_prompt(system)
+        self.context['system'] = system
+        message_list = self.generate_message_list(system, prompt, history_message)
+        self.context['message_list'] = message_list
+
+        if mcp_enable and mcp_servers is not None:
+            r = mcp_response_generator(chat_model, message_list, mcp_servers)
+            return NodeResult(
+                {'result': r, 'chat_model': chat_model, 'message_list': message_list,
+                 'history_message': history_message, 'question': question.content}, {},
+                _write_context=write_context_stream)
+
+        if stream:
+            r = chat_model.stream(message_list)
+            return NodeResult({'result': r, 'chat_model': chat_model, 'message_list': message_list,
+                               'history_message': history_message, 'question': question.content}, {},
+                              _write_context=write_context_stream)
+        else:
+            r = chat_model.invoke(message_list)
+            return NodeResult({'result': r, 'chat_model': chat_model, 'message_list': message_list,
+                               'history_message': history_message, 'question': question.content}, {},
+                              _write_context=write_context)
+
+    @staticmethod
+    def get_history_message(history_chat_record, dialogue_number, dialogue_type, runtime_node_id):
+        start_index = len(history_chat_record) - dialogue_number
+        history_message = reduce(lambda x, y: [*x, *y], [
+            get_message(history_chat_record[index], dialogue_type, runtime_node_id)
+            for index in
+            range(start_index if start_index > 0 else 0, len(history_chat_record))], [])
+        for message in history_message:
+            if isinstance(message.content, str):
+                message.content = re.sub('<form_rander>[\d\D]*?<\/form_rander>', '', message.content)
+        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 not None and len(system) > 0:
+            return [SystemMessage(self.workflow_manage.generate_prompt(system)), *history_message,
+                    HumanMessage(self.workflow_manage.generate_prompt(prompt))]
+        else:
+            return [*history_message, HumanMessage(self.workflow_manage.generate_prompt(prompt))]
+
+    @staticmethod
+    def reset_message_list(message_list: List[BaseMessage], answer_text):
+        result = [{'role': 'user' if isinstance(message, HumanMessage) else 'ai', 'content': message.content} for
+                  message
+                  in
+                  message_list]
+        result.append({'role': 'ai', 'content': answer_text})
+        return result
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            'run_time': self.context.get('run_time'),
+            'system': self.context.get('system'),
+            'history_message': [{'content': message.content, 'role': message.type} for message in
+                                (self.context.get('history_message') if self.context.get(
+                                    'history_message') is not None else [])],
+            'question': self.context.get('question'),
+            'answer': self.context.get('answer'),
+            'reasoning_content': self.context.get('reasoning_content'),
+            'type': self.node.type,
+            'message_tokens': self.context.get('message_tokens'),
+            'answer_tokens': self.context.get('answer_tokens'),
+            'status': self.status,
+            'err_message': self.err_message
+        }

apps/application/flow/step_node/application_node/__init__.py

@@ -0,0 +1,2 @@
+# coding=utf-8
+from .impl import *

apps/application/flow/step_node/application_node/i_application_node.py

@@ -0,0 +1,86 @@
+# coding=utf-8
+from typing import Type
+
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.field_message import ErrMessage
+
+from django.utils.translation import gettext_lazy as _
+
+
+class ApplicationNodeSerializer(serializers.Serializer):
+    application_id = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Application ID")))
+    question_reference_address = serializers.ListField(required=True,
+                                                       error_messages=ErrMessage.list(_("User Questions")))
+    api_input_field_list = serializers.ListField(required=False, error_messages=ErrMessage.list(_("API Input Fields")))
+    user_input_field_list = serializers.ListField(required=False,
+                                                  error_messages=ErrMessage.uuid(_("User Input Fields")))
+    image_list = serializers.ListField(required=False, error_messages=ErrMessage.list(_("picture")))
+    document_list = serializers.ListField(required=False, error_messages=ErrMessage.list(_("document")))
+    audio_list = serializers.ListField(required=False, error_messages=ErrMessage.list(_("Audio")))
+    child_node = serializers.DictField(required=False, allow_null=True,
+                                       error_messages=ErrMessage.dict(_("Child Nodes")))
+    node_data = serializers.DictField(required=False, allow_null=True, error_messages=ErrMessage.dict(_("Form Data")))
+
+
+class IApplicationNode(INode):
+    type = 'application-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return ApplicationNodeSerializer
+
+    def _run(self):
+        question = self.workflow_manage.get_reference_field(
+            self.node_params_serializer.data.get('question_reference_address')[0],
+            self.node_params_serializer.data.get('question_reference_address')[1:])
+        kwargs = {}
+        for api_input_field in self.node_params_serializer.data.get('api_input_field_list', []):
+            value = api_input_field.get('value', [''])[0] if api_input_field.get('value') else ''
+            kwargs[api_input_field['variable']] = self.workflow_manage.get_reference_field(value,
+                                                                                           api_input_field['value'][
+                                                                                           1:]) if value != '' else ''
+
+        for user_input_field in self.node_params_serializer.data.get('user_input_field_list', []):
+            value = user_input_field.get('value', [''])[0] if user_input_field.get('value') else ''
+            kwargs[user_input_field['field']] = self.workflow_manage.get_reference_field(value,
+                                                                                         user_input_field['value'][
+                                                                                         1:]) if value != '' else ''
+        # 判断是否包含这个属性
+        app_document_list = self.node_params_serializer.data.get('document_list', [])
+        if app_document_list and len(app_document_list) > 0:
+            app_document_list = self.workflow_manage.get_reference_field(
+                app_document_list[0],
+                app_document_list[1:])
+            for document in app_document_list:
+                if 'file_id' not in document:
+                    raise ValueError(
+                        _("Parameter value error: The uploaded document lacks file_id, and the document upload fails"))
+        app_image_list = self.node_params_serializer.data.get('image_list', [])
+        if app_image_list and len(app_image_list) > 0:
+            app_image_list = self.workflow_manage.get_reference_field(
+                app_image_list[0],
+                app_image_list[1:])
+            for image in app_image_list:
+                if 'file_id' not in image:
+                    raise ValueError(
+                        _("Parameter value error: The uploaded image lacks file_id, and the image upload fails"))
+
+        app_audio_list = self.node_params_serializer.data.get('audio_list', [])
+        if app_audio_list and len(app_audio_list) > 0:
+            app_audio_list = self.workflow_manage.get_reference_field(
+                app_audio_list[0],
+                app_audio_list[1:])
+            for audio in app_audio_list:
+                if 'file_id' not in audio:
+                    raise ValueError(
+                        _("Parameter value error: The uploaded audio lacks file_id, and the audio upload fails."))
+        return self.execute(**self.node_params_serializer.data, **self.flow_params_serializer.data,
+                            app_document_list=app_document_list, app_image_list=app_image_list,
+                            app_audio_list=app_audio_list,
+                            message=str(question), **kwargs)
+
+    def execute(self, application_id, message, chat_id, chat_record_id, stream, re_chat, client_id, client_type,
+                app_document_list=None, app_image_list=None, app_audio_list=None, child_node=None, node_data=None,
+                **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/application_node/impl/__init__.py

@@ -0,0 +1,2 @@
+# coding=utf-8
+from .base_application_node import BaseApplicationNode

apps/application/flow/step_node/application_node/impl/base_application_node.py

@@ -0,0 +1,267 @@
+# coding=utf-8
+import json
+import re
+import time
+import uuid
+from typing import Dict, List
+
+from application.flow.common import Answer
+from application.flow.i_step_node import NodeResult, INode
+from application.flow.step_node.application_node.i_application_node import IApplicationNode
+from application.models import Chat
+
+
+def string_to_uuid(input_str):
+    return str(uuid.uuid5(uuid.NAMESPACE_DNS, input_str))
+
+
+def _is_interrupt_exec(node, node_variable: Dict, workflow_variable: Dict):
+    return node_variable.get('is_interrupt_exec', False)
+
+
+def _write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow, answer: str,
+                   reasoning_content: str):
+    result = node_variable.get('result')
+    node.context['application_node_dict'] = node_variable.get('application_node_dict')
+    node.context['node_dict'] = node_variable.get('node_dict', {})
+    node.context['is_interrupt_exec'] = node_variable.get('is_interrupt_exec')
+    node.context['message_tokens'] = result.get('usage', {}).get('prompt_tokens', 0)
+    node.context['answer_tokens'] = result.get('usage', {}).get('completion_tokens', 0)
+    node.context['answer'] = answer
+    node.context['result'] = answer
+    node.context['reasoning_content'] = reasoning_content
+    node.context['question'] = node_variable['question']
+    node.context['run_time'] = time.time() - node.context['start_time']
+    if workflow.is_result(node, NodeResult(node_variable, workflow_variable)):
+        node.answer_text = answer
+
+
+def write_context_stream(node_variable: Dict, workflow_variable: Dict, node: INode, workflow):
+    """
+    写入上下文数据 (流式)
+    @param node_variable:      节点数据
+    @param workflow_variable:  全局数据
+    @param node:               节点
+    @param workflow:           工作流管理器
+    """
+    response = node_variable.get('result')
+    answer = ''
+    reasoning_content = ''
+    usage = {}
+    node_child_node = {}
+    application_node_dict = node.context.get('application_node_dict', {})
+    is_interrupt_exec = False
+    for chunk in response:
+        # 先把流转成字符串
+        response_content = chunk.decode('utf-8')[6:]
+        response_content = json.loads(response_content)
+        content = response_content.get('content', '')
+        runtime_node_id = response_content.get('runtime_node_id', '')
+        chat_record_id = response_content.get('chat_record_id', '')
+        child_node = response_content.get('child_node')
+        view_type = response_content.get('view_type')
+        node_type = response_content.get('node_type')
+        real_node_id = response_content.get('real_node_id')
+        node_is_end = response_content.get('node_is_end', False)
+        _reasoning_content = response_content.get('reasoning_content', '')
+        if node_type == 'form-node':
+            is_interrupt_exec = True
+        answer += content
+        reasoning_content += _reasoning_content
+        node_child_node = {'runtime_node_id': runtime_node_id, 'chat_record_id': chat_record_id,
+                           'child_node': child_node}
+
+        if real_node_id is not None:
+            application_node = application_node_dict.get(real_node_id, None)
+            if application_node is None:
+
+                application_node_dict[real_node_id] = {'content': content,
+                                                       'runtime_node_id': runtime_node_id,
+                                                       'chat_record_id': chat_record_id,
+                                                       'child_node': child_node,
+                                                       'index': len(application_node_dict),
+                                                       'view_type': view_type,
+                                                       'reasoning_content': _reasoning_content}
+            else:
+                application_node['content'] += content
+                application_node['reasoning_content'] += _reasoning_content
+
+        yield {'content': content,
+               'node_type': node_type,
+               'runtime_node_id': runtime_node_id, 'chat_record_id': chat_record_id,
+               'reasoning_content': _reasoning_content,
+               'child_node': child_node,
+               'real_node_id': real_node_id,
+               'node_is_end': node_is_end,
+               'view_type': view_type}
+        usage = response_content.get('usage', {})
+    node_variable['result'] = {'usage': usage}
+    node_variable['is_interrupt_exec'] = is_interrupt_exec
+    node_variable['child_node'] = node_child_node
+    node_variable['application_node_dict'] = application_node_dict
+    _write_context(node_variable, workflow_variable, node, workflow, answer, reasoning_content)
+
+
+def write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow):
+    """
+    写入上下文数据
+    @param node_variable:      节点数据
+    @param workflow_variable:  全局数据
+    @param node:               节点实例对象
+    @param workflow:           工作流管理器
+    """
+    response = node_variable.get('result', {}).get('data', {})
+    node_variable['result'] = {'usage': {'completion_tokens': response.get('completion_tokens'),
+                                         'prompt_tokens': response.get('prompt_tokens')}}
+    answer = response.get('content', '') or "抱歉，没有查找到相关内容，请重新描述您的问题或提供更多信息。"
+    reasoning_content = response.get('reasoning_content', '')
+    answer_list = response.get('answer_list', [])
+    node_variable['application_node_dict'] = {answer.get('real_node_id'): {**answer, 'index': index} for answer, index
+                                              in
+                                              zip(answer_list, range(len(answer_list)))}
+    _write_context(node_variable, workflow_variable, node, workflow, answer, reasoning_content)
+
+
+def reset_application_node_dict(application_node_dict, runtime_node_id, node_data):
+    try:
+        if application_node_dict is None:
+            return
+        for key in application_node_dict:
+            application_node = application_node_dict[key]
+            if application_node.get('runtime_node_id') == runtime_node_id:
+                content: str = application_node.get('content')
+                match = re.search('<form_rander>.*?</form_rander>', content)
+                if match:
+                    form_setting_str = match.group().replace('<form_rander>', '').replace('</form_rander>', '')
+                    form_setting = json.loads(form_setting_str)
+                    form_setting['is_submit'] = True
+                    form_setting['form_data'] = node_data
+                    value = f'<form_rander>{json.dumps(form_setting)}</form_rander>'
+                    res = re.sub('<form_rander>.*?</form_rander>',
+                                 '${value}', content)
+                    application_node['content'] = res.replace('${value}', value)
+    except Exception as e:
+        pass
+
+
+class BaseApplicationNode(IApplicationNode):
+    def get_answer_list(self) -> List[Answer] | None:
+        if self.answer_text is None:
+            return None
+        application_node_dict = self.context.get('application_node_dict')
+        if application_node_dict is None or len(application_node_dict) == 0:
+            return [
+                Answer(self.answer_text, self.view_type, self.runtime_node_id, self.workflow_params['chat_record_id'],
+                       self.context.get('child_node'), self.runtime_node_id, '')]
+        else:
+            return [Answer(n.get('content'), n.get('view_type'), self.runtime_node_id,
+                           self.workflow_params['chat_record_id'], {'runtime_node_id': n.get('runtime_node_id'),
+                                                                    'chat_record_id': n.get('chat_record_id')
+                               , 'child_node': n.get('child_node')}, n.get('real_node_id'),
+                           n.get('reasoning_content', ''))
+                    for n in
+                    sorted(application_node_dict.values(), key=lambda item: item.get('index'))]
+
+    def save_context(self, details, workflow_manage):
+        self.context['answer'] = details.get('answer')
+        self.context['result'] = details.get('answer')
+        self.context['question'] = details.get('question')
+        self.context['type'] = details.get('type')
+        self.context['reasoning_content'] = details.get('reasoning_content')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
+
+    def execute(self, application_id, message, chat_id, chat_record_id, stream, re_chat, client_id, client_type,
+                app_document_list=None, app_image_list=None, app_audio_list=None, child_node=None, node_data=None,
+                **kwargs) -> NodeResult:
+        from application.serializers.chat_message_serializers import ChatMessageSerializer
+        # 生成嵌入应用的chat_id
+        current_chat_id = string_to_uuid(chat_id + application_id)
+        Chat.objects.get_or_create(id=current_chat_id, defaults={
+            'application_id': application_id,
+            'abstract': message[0:1024],
+            'client_id': client_id,
+        })
+        if app_document_list is None:
+            app_document_list = []
+        if app_image_list is None:
+            app_image_list = []
+        if app_audio_list is None:
+            app_audio_list = []
+        runtime_node_id = None
+        record_id = None
+        child_node_value = None
+        if child_node is not None:
+            runtime_node_id = child_node.get('runtime_node_id')
+            record_id = child_node.get('chat_record_id')
+            child_node_value = child_node.get('child_node')
+            application_node_dict = self.context.get('application_node_dict')
+            reset_application_node_dict(application_node_dict, runtime_node_id, node_data)
+
+        response = ChatMessageSerializer(
+            data={'chat_id': current_chat_id, 'message': message,
+                  're_chat': re_chat,
+                  'stream': stream,
+                  'application_id': application_id,
+                  'client_id': client_id,
+                  'client_type': client_type,
+                  'document_list': app_document_list,
+                  'image_list': app_image_list,
+                  'audio_list': app_audio_list,
+                  'runtime_node_id': runtime_node_id,
+                  'chat_record_id': record_id,
+                  'child_node': child_node_value,
+                  'node_data': node_data,
+                  'form_data': kwargs}).chat()
+        if response.status_code == 200:
+            if stream:
+                content_generator = response.streaming_content
+                return NodeResult({'result': content_generator, 'question': message}, {},
+                                  _write_context=write_context_stream, _is_interrupt=_is_interrupt_exec)
+            else:
+                data = json.loads(response.content)
+                return NodeResult({'result': data, 'question': message}, {},
+                                  _write_context=write_context, _is_interrupt=_is_interrupt_exec)
+
+    def get_details(self, index: int, **kwargs):
+        global_fields = []
+        for api_input_field in self.node_params_serializer.data.get('api_input_field_list', []):
+            value = api_input_field.get('value', [''])[0] if api_input_field.get('value') else ''
+            global_fields.append({
+                'label': api_input_field['variable'],
+                'key': api_input_field['variable'],
+                'value': self.workflow_manage.get_reference_field(
+                    value,
+                    api_input_field['value'][1:]
+                ) if value != '' else ''
+            })
+
+        for user_input_field in self.node_params_serializer.data.get('user_input_field_list', []):
+            value = user_input_field.get('value', [''])[0] if user_input_field.get('value') else ''
+            global_fields.append({
+                'label': user_input_field['label'],
+                'key': user_input_field['field'],
+                'value': self.workflow_manage.get_reference_field(
+                    value,
+                    user_input_field['value'][1:]
+                ) if value != '' else ''
+            })
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            "info": self.node.properties.get('node_data'),
+            'run_time': self.context.get('run_time'),
+            'question': self.context.get('question'),
+            'answer': self.context.get('answer'),
+            'reasoning_content': self.context.get('reasoning_content'),
+            'type': self.node.type,
+            'message_tokens': self.context.get('message_tokens'),
+            'answer_tokens': self.context.get('answer_tokens'),
+            'status': self.status,
+            'err_message': self.err_message,
+            'global_fields': global_fields,
+            'document_list': self.workflow_manage.document_list,
+            'image_list': self.workflow_manage.image_list,
+            'audio_list': self.workflow_manage.audio_list,
+            'application_node_dict': self.context.get('application_node_dict')
+        }

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

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/6/7 14:43
+    @desc:
+"""
+from .impl import *

apps/application/flow/step_node/condition_node/compare/__init__.py

@@ -0,0 +1,30 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/6/7 14:43
+    @desc:
+"""
+
+from .contain_compare import *
+from .equal_compare import *
+from .ge_compare import *
+from .gt_compare import *
+from .is_not_null_compare import *
+from .is_not_true import IsNotTrueCompare
+from .is_null_compare import *
+from .is_true import IsTrueCompare
+from .le_compare import *
+from .len_equal_compare import *
+from .len_ge_compare import *
+from .len_gt_compare import *
+from .len_le_compare import *
+from .len_lt_compare import *
+from .lt_compare import *
+from .not_contain_compare import *
+
+compare_handle_list = [GECompare(), GTCompare(), ContainCompare(), EqualCompare(), LTCompare(), LECompare(),
+                       LenLECompare(), LenGECompare(), LenEqualCompare(), LenGTCompare(), LenLTCompare(),
+                       IsNullCompare(),
+                       IsNotNullCompare(), NotContainCompare(), IsTrueCompare(), IsNotTrueCompare()]

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

@@ -0,0 +1,20 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： compare.py
+    @date：2024/6/7 14:37
+    @desc:
+"""
+from abc import abstractmethod
+from typing import List
+
+
+class Compare:
+    @abstractmethod
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        pass
+
+    @abstractmethod
+    def compare(self, source_value, compare, target_value):
+        pass

apps/application/flow/step_node/condition_node/compare/contain_compare.py

@@ -0,0 +1,23 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： contain_compare.py
+    @date：2024/6/11 10:02
+    @desc:
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class ContainCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'contain':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        if isinstance(source_value, str):
+            return str(target_value) in source_value
+        return any([str(item) == str(target_value) for item in source_value])

apps/application/flow/step_node/condition_node/compare/equal_compare.py

@@ -0,0 +1,21 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： equal_compare.py
+    @date：2024/6/7 14:44
+    @desc:
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class EqualCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'eq':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        return str(source_value) == str(target_value)

apps/application/flow/step_node/condition_node/compare/ge_compare.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： lt_compare.py
+    @date：2024/6/11 9:52
+    @desc: 大于比较器
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class GECompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'ge':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return float(source_value) >= float(target_value)
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/gt_compare.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： lt_compare.py
+    @date：2024/6/11 9:52
+    @desc: 大于比较器
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class GTCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'gt':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return float(source_value) > float(target_value)
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/is_not_null_compare.py

@@ -0,0 +1,21 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： is_not_null_compare.py
+    @date：2024/6/28 10:45
+    @desc:
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare import Compare
+
+
+class IsNotNullCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'is_not_null':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        return source_value is not None and len(source_value) > 0

apps/application/flow/step_node/condition_node/compare/is_not_true.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： is_not_true.py
+    @date：2025/4/7 13:44
+    @desc:
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare import Compare
+
+
+class IsNotTrueCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'is_not_true':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return source_value is False
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/is_null_compare.py

@@ -0,0 +1,21 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： is_null_compare.py
+    @date：2024/6/28 10:45
+    @desc:
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare import Compare
+
+
+class IsNullCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'is_null':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        return source_value is None or len(source_value) == 0

apps/application/flow/step_node/condition_node/compare/is_true.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： IsTrue.py
+    @date：2025/4/7 13:38
+    @desc:
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare import Compare
+
+
+class IsTrueCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'is_true':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return source_value is True
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/le_compare.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： lt_compare.py
+    @date：2024/6/11 9:52
+    @desc: 小于比较器
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class LECompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'le':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return float(source_value) <= float(target_value)
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/len_equal_compare.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： equal_compare.py
+    @date：2024/6/7 14:44
+    @desc:
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class LenEqualCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'len_eq':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return len(source_value) == int(target_value)
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/len_ge_compare.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： lt_compare.py
+    @date：2024/6/11 9:52
+    @desc: 大于比较器
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class LenGECompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'len_ge':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return len(source_value) >= int(target_value)
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/len_gt_compare.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： lt_compare.py
+    @date：2024/6/11 9:52
+    @desc: 大于比较器
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class LenGTCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'len_gt':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return len(source_value) > int(target_value)
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/len_le_compare.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： lt_compare.py
+    @date：2024/6/11 9:52
+    @desc: 小于比较器
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class LenLECompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'len_le':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return len(source_value) <= int(target_value)
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/len_lt_compare.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： lt_compare.py
+    @date：2024/6/11 9:52
+    @desc: 小于比较器
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class LenLTCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'len_lt':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return len(source_value) < int(target_value)
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/lt_compare.py

@@ -0,0 +1,24 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： lt_compare.py
+    @date：2024/6/11 9:52
+    @desc: 小于比较器
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class LTCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'lt':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        try:
+            return float(source_value) < float(target_value)
+        except Exception as e:
+            return False

apps/application/flow/step_node/condition_node/compare/not_contain_compare.py

@@ -0,0 +1,23 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： contain_compare.py
+    @date：2024/6/11 10:02
+    @desc:
+"""
+from typing import List
+
+from application.flow.step_node.condition_node.compare.compare import Compare
+
+
+class NotContainCompare(Compare):
+
+    def support(self, node_id, fields: List[str], source_value, compare, target_value):
+        if compare == 'not_contain':
+            return True
+
+    def compare(self, source_value, compare, target_value):
+        if isinstance(source_value, str):
+            return str(target_value) not in source_value
+        return not any([str(item) == str(target_value) for item in source_value])

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

@@ -0,0 +1,39 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： i_condition_node.py
+    @date：2024/6/7 9:54
+    @desc:
+"""
+from typing import Type
+
+from django.utils.translation import gettext_lazy as _
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode
+from common.util.field_message import ErrMessage
+
+
+class ConditionSerializer(serializers.Serializer):
+    compare = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Comparator")))
+    value = serializers.CharField(required=True, error_messages=ErrMessage.char(_("value")))
+    field = serializers.ListField(required=True, error_messages=ErrMessage.char(_("Fields")))
+
+
+class ConditionBranchSerializer(serializers.Serializer):
+    id = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Branch id")))
+    type = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Branch Type")))
+    condition = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Condition or|and")))
+    conditions = ConditionSerializer(many=True)
+
+
+class ConditionNodeParamsSerializer(serializers.Serializer):
+    branch = ConditionBranchSerializer(many=True)
+
+
+class IConditionNode(INode):
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return ConditionNodeParamsSerializer
+
+    type = 'condition-node'

apps/application/flow/step_node/condition_node/impl/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py
+    @date：2024/6/11 15:35
+    @desc:
+"""
+from .base_condition_node import BaseConditionNode

apps/application/flow/step_node/condition_node/impl/base_condition_node.py

@@ -0,0 +1,62 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： base_condition_node.py
+    @date：2024/6/7 11:29
+    @desc:
+"""
+from typing import List
+
+from application.flow.i_step_node import NodeResult
+from application.flow.step_node.condition_node.compare import compare_handle_list
+from application.flow.step_node.condition_node.i_condition_node import IConditionNode
+
+
+class BaseConditionNode(IConditionNode):
+    def save_context(self, details, workflow_manage):
+        self.context['branch_id'] = details.get('branch_id')
+        self.context['branch_name'] = details.get('branch_name')
+
+    def execute(self, **kwargs) -> NodeResult:
+        branch_list = self.node_params_serializer.data['branch']
+        branch = self._execute(branch_list)
+        r = NodeResult({'branch_id': branch.get('id'), 'branch_name': branch.get('type')}, {})
+        return r
+
+    def _execute(self, branch_list: List):
+        for branch in branch_list:
+            if self.branch_assertion(branch):
+                return branch
+
+    def branch_assertion(self, branch):
+        condition_list = [self.assertion(row.get('field'), row.get('compare'), row.get('value')) for row in
+                          branch.get('conditions')]
+        condition = branch.get('condition')
+        return all(condition_list) if condition == 'and' else any(condition_list)
+
+    def assertion(self, field_list: List[str], compare: str, value):
+        try:
+            value = self.workflow_manage.generate_prompt(value)
+        except Exception as e:
+            pass
+        field_value = None
+        try:
+            field_value = self.workflow_manage.get_reference_field(field_list[0], field_list[1:])
+        except  Exception as e:
+            pass
+        for compare_handler in compare_handle_list:
+            if compare_handler.support(field_list[0], field_list[1:], field_value, compare, value):
+                return compare_handler.compare(field_value, compare, value)
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            'run_time': self.context.get('run_time'),
+            'branch_id': self.context.get('branch_id'),
+            'branch_name': self.context.get('branch_name'),
+            'type': self.node.type,
+            'status': self.status,
+            'err_message': self.err_message
+        }

apps/application/flow/step_node/direct_reply_node/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py
+    @date：2024/6/11 17:50
+    @desc:
+"""
+from .impl import *

apps/application/flow/step_node/direct_reply_node/i_reply_node.py

@@ -0,0 +1,48 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： i_reply_node.py
+    @date：2024/6/11 16:25
+    @desc:
+"""
+from typing import Type
+
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.exception.app_exception import AppApiException
+from common.util.field_message import ErrMessage
+from django.utils.translation import gettext_lazy as _
+
+
+class ReplyNodeParamsSerializer(serializers.Serializer):
+    reply_type = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Response Type")))
+    fields = serializers.ListField(required=False, error_messages=ErrMessage.list(_("Reference Field")))
+    content = serializers.CharField(required=False, allow_blank=True, allow_null=True,
+                                    error_messages=ErrMessage.char(_("Direct answer content")))
+    is_result = serializers.BooleanField(required=False, error_messages=ErrMessage.boolean(_('Whether to return content')))
+
+    def is_valid(self, *, raise_exception=False):
+        super().is_valid(raise_exception=True)
+        if self.data.get('reply_type') == 'referencing':
+            if 'fields' not in self.data:
+                raise AppApiException(500, _("Reference field cannot be empty"))
+            if len(self.data.get('fields')) < 2:
+                raise AppApiException(500, _("Reference field error"))
+        else:
+            if 'content' not in self.data or self.data.get('content') is None:
+                raise AppApiException(500, _("Content cannot be empty"))
+
+
+class IReplyNode(INode):
+    type = 'reply-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return ReplyNodeParamsSerializer
+
+    def _run(self):
+        return self.execute(**self.node_params_serializer.data, **self.flow_params_serializer.data)
+
+    def execute(self, reply_type, stream, fields=None, content=None, **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/direct_reply_node/impl/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py
+    @date：2024/6/11 17:49
+    @desc:
+"""
+from .base_reply_node import *

apps/application/flow/step_node/direct_reply_node/impl/base_reply_node.py

@@ -0,0 +1,45 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： base_reply_node.py
+    @date：2024/6/11 17:25
+    @desc:
+"""
+from typing import List
+
+from application.flow.i_step_node import NodeResult
+from application.flow.step_node.direct_reply_node.i_reply_node import IReplyNode
+
+
+class BaseReplyNode(IReplyNode):
+    def save_context(self, details, workflow_manage):
+        self.context['answer'] = details.get('answer')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
+
+    def execute(self, reply_type, stream, fields=None, content=None, **kwargs) -> NodeResult:
+        if reply_type == 'referencing':
+            result = self.get_reference_content(fields)
+        else:
+            result = self.generate_reply_content(content)
+        return NodeResult({'answer': result}, {})
+
+    def generate_reply_content(self, prompt):
+        return self.workflow_manage.generate_prompt(prompt)
+
+    def get_reference_content(self, fields: List[str]):
+        return str(self.workflow_manage.get_reference_field(
+            fields[0],
+            fields[1:]))
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            'run_time': self.context.get('run_time'),
+            'type': self.node.type,
+            'answer': self.context.get('answer'),
+            'status': self.status,
+            'err_message': self.err_message
+        }

apps/application/flow/step_node/document_extract_node/__init__.py

@@ -0,0 +1 @@
+from .impl import *

apps/application/flow/step_node/document_extract_node/i_document_extract_node.py

@@ -0,0 +1,28 @@
+# coding=utf-8
+
+from typing import Type
+
+from django.utils.translation import gettext_lazy as _
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.field_message import ErrMessage
+
+
+class DocumentExtractNodeSerializer(serializers.Serializer):
+    document_list = serializers.ListField(required=False, error_messages=ErrMessage.list(_("document")))
+
+
+class IDocumentExtractNode(INode):
+    type = 'document-extract-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return DocumentExtractNodeSerializer
+
+    def _run(self):
+        res = self.workflow_manage.get_reference_field(self.node_params_serializer.data.get('document_list')[0],
+                                                       self.node_params_serializer.data.get('document_list')[1:])
+        return self.execute(document=res, **self.flow_params_serializer.data)
+
+    def execute(self, document, chat_id, **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/document_extract_node/impl/__init__.py

@@ -0,0 +1 @@
+from .base_document_extract_node import BaseDocumentExtractNode

apps/application/flow/step_node/document_extract_node/impl/base_document_extract_node.py

@@ -0,0 +1,94 @@
+# coding=utf-8
+import io
+import mimetypes
+
+from django.core.files.uploadedfile import InMemoryUploadedFile
+from django.db.models import QuerySet
+
+from application.flow.i_step_node import NodeResult
+from application.flow.step_node.document_extract_node.i_document_extract_node import IDocumentExtractNode
+from dataset.models import File
+from dataset.serializers.document_serializers import split_handles, parse_table_handle_list, FileBufferHandle
+from dataset.serializers.file_serializers import FileSerializer
+
+
+def bytes_to_uploaded_file(file_bytes, file_name="file.txt"):
+    content_type, _ = mimetypes.guess_type(file_name)
+    if content_type is None:
+        # 如果未能识别，设置为默认的二进制文件类型
+        content_type = "application/octet-stream"
+    # 创建一个内存中的字节流对象
+    file_stream = io.BytesIO(file_bytes)
+
+    # 获取文件大小
+    file_size = len(file_bytes)
+
+    # 创建 InMemoryUploadedFile 对象
+    uploaded_file = InMemoryUploadedFile(
+        file=file_stream,
+        field_name=None,
+        name=file_name,
+        content_type=content_type,
+        size=file_size,
+        charset=None,
+    )
+    return uploaded_file
+
+
+splitter = '\n`-----------------------------------`\n'
+
+class BaseDocumentExtractNode(IDocumentExtractNode):
+    def save_context(self, details, workflow_manage):
+        self.context['content'] = details.get('content')
+
+
+    def execute(self, document, chat_id, **kwargs):
+        get_buffer = FileBufferHandle().get_buffer
+
+        self.context['document_list'] = document
+        content = []
+        if document is None or not isinstance(document, list):
+            return NodeResult({'content': ''}, {})
+
+        application = self.workflow_manage.work_flow_post_handler.chat_info.application
+
+        # doc文件中的图片保存
+        def save_image(image_list):
+            for image in image_list:
+                meta = {
+                    'debug': False if application.id else True,
+                    'chat_id': chat_id,
+                    'application_id': str(application.id) if application.id else None,
+                    'file_id': str(image.id)
+                }
+                file = bytes_to_uploaded_file(image.image, image.image_name)
+                FileSerializer(data={'file': file, 'meta': meta}).upload()
+
+        for doc in document:
+            file = QuerySet(File).filter(id=doc['file_id']).first()
+            buffer = io.BytesIO(file.get_byte().tobytes())
+            buffer.name = doc['name']  # this is the important line
+
+            for split_handle in (parse_table_handle_list + split_handles):
+                if split_handle.support(buffer, get_buffer):
+                    # 回到文件头
+                    buffer.seek(0)
+                    file_content = split_handle.get_content(buffer, save_image)
+                    content.append('### ' + doc['name'] + '\n' + file_content)
+                    break
+
+        return NodeResult({'content': splitter.join(content)}, {})
+
+    def get_details(self, index: int, **kwargs):
+        content = self.context.get('content', '').split(splitter)
+        # 不保存content全部内容，因为content内容可能会很大
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            'run_time': self.context.get('run_time'),
+            'type': self.node.type,
+            'content': [file_content[:500] for file_content in content],
+            'status': self.status,
+            'err_message': self.err_message,
+            'document_list': self.context.get('document_list')
+        }

apps/application/flow/step_node/form_node/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/11/4 14:48
+    @desc:
+"""
+from .impl import *

apps/application/flow/step_node/form_node/i_form_node.py

@@ -0,0 +1,35 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： i_form_node.py
+    @date：2024/11/4 14:48
+    @desc:
+"""
+from typing import Type
+
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.field_message import ErrMessage
+from django.utils.translation import gettext_lazy as _
+
+
+class FormNodeParamsSerializer(serializers.Serializer):
+    form_field_list = serializers.ListField(required=True, error_messages=ErrMessage.list(_("Form Configuration")))
+    form_content_format = serializers.CharField(required=True, error_messages=ErrMessage.char(_('Form output content')))
+    form_data = serializers.DictField(required=False, allow_null=True, error_messages=ErrMessage.dict(_("Form Data")))
+
+
+class IFormNode(INode):
+    type = 'form-node'
+    view_type = 'single_view'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return FormNodeParamsSerializer
+
+    def _run(self):
+        return self.execute(**self.node_params_serializer.data, **self.flow_params_serializer.data)
+
+    def execute(self, form_field_list, form_content_format, form_data, **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/form_node/impl/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/11/4 14:49
+    @desc:
+"""
+from .base_form_node import BaseFormNode

apps/application/flow/step_node/form_node/impl/base_form_node.py

@@ -0,0 +1,107 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： base_form_node.py
+    @date：2024/11/4 14:52
+    @desc:
+"""
+import json
+import time
+from typing import Dict, List
+
+from langchain_core.prompts import PromptTemplate
+
+from application.flow.common import Answer
+from application.flow.i_step_node import NodeResult
+from application.flow.step_node.form_node.i_form_node import IFormNode
+
+
+def write_context(step_variable: Dict, global_variable: Dict, node, workflow):
+    if step_variable is not None:
+        for key in step_variable:
+            node.context[key] = step_variable[key]
+        if workflow.is_result(node, NodeResult(step_variable, global_variable)) and 'result' in step_variable:
+            result = step_variable['result']
+            yield result
+            node.answer_text = result
+    node.context['run_time'] = time.time() - node.context['start_time']
+
+
+class BaseFormNode(IFormNode):
+    def save_context(self, details, workflow_manage):
+        form_data = details.get('form_data', None)
+        self.context['result'] = details.get('result')
+        self.context['form_content_format'] = details.get('form_content_format')
+        self.context['form_field_list'] = details.get('form_field_list')
+        self.context['run_time'] = details.get('run_time')
+        self.context['start_time'] = details.get('start_time')
+        self.context['form_data'] = form_data
+        self.context['is_submit'] = details.get('is_submit')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('result')
+        if form_data is not None:
+            for key in form_data:
+                self.context[key] = form_data[key]
+
+    def execute(self, form_field_list, form_content_format, form_data, **kwargs) -> NodeResult:
+        if form_data is not None:
+            self.context['is_submit'] = True
+            self.context['form_data'] = form_data
+            for key in form_data:
+                self.context[key] = form_data.get(key)
+        else:
+            self.context['is_submit'] = False
+        form_setting = {"form_field_list": form_field_list, "runtime_node_id": self.runtime_node_id,
+                        "chat_record_id": self.flow_params_serializer.data.get("chat_record_id"),
+                        "is_submit": self.context.get("is_submit", False)}
+        form = f'<form_rander>{json.dumps(form_setting, ensure_ascii=False)}</form_rander>'
+        context = self.workflow_manage.get_workflow_content()
+        form_content_format = self.workflow_manage.reset_prompt(form_content_format)
+        prompt_template = PromptTemplate.from_template(form_content_format, template_format='jinja2')
+        value = prompt_template.format(form=form, context=context)
+        return NodeResult(
+            {'result': value, 'form_field_list': form_field_list, 'form_content_format': form_content_format}, {},
+            _write_context=write_context)
+
+    def get_answer_list(self) -> List[Answer] | None:
+        form_content_format = self.context.get('form_content_format')
+        form_field_list = self.context.get('form_field_list')
+        form_setting = {"form_field_list": form_field_list, "runtime_node_id": self.runtime_node_id,
+                        "chat_record_id": self.flow_params_serializer.data.get("chat_record_id"),
+                        'form_data': self.context.get('form_data', {}),
+                        "is_submit": self.context.get("is_submit", False)}
+        form = f'<form_rander>{json.dumps(form_setting, ensure_ascii=False)}</form_rander>'
+        context = self.workflow_manage.get_workflow_content()
+        form_content_format = self.workflow_manage.reset_prompt(form_content_format)
+        prompt_template = PromptTemplate.from_template(form_content_format, template_format='jinja2')
+        value = prompt_template.format(form=form, context=context)
+        return [Answer(value, self.view_type, self.runtime_node_id, self.workflow_params['chat_record_id'], None,
+                       self.runtime_node_id, '')]
+
+    def get_details(self, index: int, **kwargs):
+        form_content_format = self.context.get('form_content_format')
+        form_field_list = self.context.get('form_field_list')
+        form_setting = {"form_field_list": form_field_list, "runtime_node_id": self.runtime_node_id,
+                        "chat_record_id": self.flow_params_serializer.data.get("chat_record_id"),
+                        'form_data': self.context.get('form_data', {}),
+                        "is_submit": self.context.get("is_submit", False)}
+        form = f'<form_rander>{json.dumps(form_setting, ensure_ascii=False)}</form_rander>'
+        context = self.workflow_manage.get_workflow_content()
+        form_content_format = self.workflow_manage.reset_prompt(form_content_format)
+        prompt_template = PromptTemplate.from_template(form_content_format, template_format='jinja2')
+        value = prompt_template.format(form=form, context=context)
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            "result": value,
+            "form_content_format": self.context.get('form_content_format'),
+            "form_field_list": self.context.get('form_field_list'),
+            'form_data': self.context.get('form_data'),
+            'start_time': self.context.get('start_time'),
+            'is_submit': self.context.get('is_submit'),
+            'run_time': self.context.get('run_time'),
+            'type': self.node.type,
+            'status': self.status,
+            'err_message': self.err_message
+        }

apps/application/flow/step_node/function_lib_node/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： __init__.py
+    @date：2024/8/8 17:45
+    @desc:
+"""
+from .impl import *

apps/application/flow/step_node/function_lib_node/i_function_lib_node.py

@@ -0,0 +1,48 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： i_function_lib_node.py
+    @date：2024/8/8 16:21
+    @desc:
+"""
+from typing import Type
+
+from django.db.models import QuerySet
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.field.common import ObjectField
+from common.util.field_message import ErrMessage
+from function_lib.models.function import FunctionLib
+from django.utils.translation import gettext_lazy as _
+
+
+class InputField(serializers.Serializer):
+    name = serializers.CharField(required=True, error_messages=ErrMessage.char(_('Variable Name')))
+    value = ObjectField(required=True, error_messages=ErrMessage.char(_("Variable Value")), model_type_list=[str, list])
+
+
+class FunctionLibNodeParamsSerializer(serializers.Serializer):
+    function_lib_id = serializers.UUIDField(required=True, error_messages=ErrMessage.uuid(_('Library ID')))
+    input_field_list = InputField(required=True, many=True)
+    is_result = serializers.BooleanField(required=False, error_messages=ErrMessage.boolean(_('Whether to return content')))
+
+    def is_valid(self, *, raise_exception=False):
+        super().is_valid(raise_exception=True)
+        f_lib = QuerySet(FunctionLib).filter(id=self.data.get('function_lib_id')).first()
+        if f_lib is None:
+            raise Exception(_('The function has been deleted'))
+
+
+class IFunctionLibNode(INode):
+    type = 'function-lib-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return FunctionLibNodeParamsSerializer
+
+    def _run(self):
+        return self.execute(**self.node_params_serializer.data, **self.flow_params_serializer.data)
+
+    def execute(self, function_lib_id, input_field_list, **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/function_lib_node/impl/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： __init__.py
+    @date：2024/8/8 17:48
+    @desc:
+"""
+from .base_function_lib_node import BaseFunctionLibNodeNode

apps/application/flow/step_node/function_lib_node/impl/base_function_lib_node.py

@@ -0,0 +1,150 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： base_function_lib_node.py
+    @date：2024/8/8 17:49
+    @desc:
+"""
+import json
+import time
+from typing import Dict
+
+from django.db.models import QuerySet
+from django.utils.translation import gettext as _
+
+from application.flow.i_step_node import NodeResult
+from application.flow.step_node.function_lib_node.i_function_lib_node import IFunctionLibNode
+from common.exception.app_exception import AppApiException
+from common.util.function_code import FunctionExecutor
+from common.util.rsa_util import rsa_long_decrypt
+from function_lib.models.function import FunctionLib
+from smartdoc.const import CONFIG
+
+function_executor = FunctionExecutor(CONFIG.get('SANDBOX'))
+
+
+def write_context(step_variable: Dict, global_variable: Dict, node, workflow):
+    if step_variable is not None:
+        for key in step_variable:
+            node.context[key] = step_variable[key]
+        if workflow.is_result(node, NodeResult(step_variable, global_variable)) and 'result' in step_variable:
+            result = str(step_variable['result']) + '\n'
+            yield result
+            node.answer_text = result
+    node.context['run_time'] = time.time() - node.context['start_time']
+
+
+def get_field_value(debug_field_list, name, is_required):
+    result = [field for field in debug_field_list if field.get('name') == name]
+    if len(result) > 0:
+        return result[-1]['value']
+    if is_required:
+        raise AppApiException(500, _('Field: {name} No value set').format(name=name))
+    return None
+
+
+def valid_reference_value(_type, value, name):
+    if _type == 'int':
+        instance_type = int | float
+    elif _type == 'float':
+        instance_type = float | int
+    elif _type == 'dict':
+        instance_type = dict
+    elif _type == 'array':
+        instance_type = list
+    elif _type == 'string':
+        instance_type = str
+    else:
+        raise Exception(_('Field: {name} Type: {_type} Value: {value} Unsupported types').format(name=name,
+                                                                                                 _type=_type))
+    if not isinstance(value, instance_type):
+        raise Exception(
+            _('Field: {name} Type: {_type} Value: {value} Type error').format(name=name, _type=_type,
+                                                                              value=value))
+
+
+def convert_value(name: str, value, _type, is_required, source, node):
+    if not is_required and (value is None or (isinstance(value, str) and len(value) == 0)):
+        return None
+    if not is_required and source == 'reference' and (value is None or len(value) == 0):
+        return None
+    if source == 'reference':
+        value = node.workflow_manage.get_reference_field(
+            value[0],
+            value[1:])
+        valid_reference_value(_type, value, name)
+        if _type == 'int':
+            return int(value)
+        if _type == 'float':
+            return float(value)
+        return value
+    try:
+        if _type == 'int':
+            return int(value)
+        if _type == 'float':
+            return float(value)
+        if _type == 'dict':
+            v = json.loads(value)
+            if isinstance(v, dict):
+                return v
+            raise Exception(_('type error'))
+        if _type == 'array':
+            v = json.loads(value)
+            if isinstance(v, list):
+                return v
+            raise Exception(_('type error'))
+        return value
+    except Exception as e:
+        raise Exception(
+            _('Field: {name} Type: {_type} Value: {value} Type error').format(name=name, _type=_type,
+                                                                              value=value))
+
+
+def valid_function(function_lib, user_id):
+    if function_lib is None:
+        raise Exception(_('Function does not exist'))
+    if function_lib.permission_type == 'PRIVATE' and str(function_lib.user_id) != str(user_id):
+        raise Exception(_('No permission to use this function {name}').format(name=function_lib.name))
+    if not function_lib.is_active:
+        raise Exception(_('Function {name} is unavailable').format(name=function_lib.name))
+
+
+class BaseFunctionLibNodeNode(IFunctionLibNode):
+    def save_context(self, details, workflow_manage):
+        self.context['result'] = details.get('result')
+        if self.node_params.get('is_result'):
+            self.answer_text = str(details.get('result'))
+
+    def execute(self, function_lib_id, input_field_list, **kwargs) -> NodeResult:
+        function_lib = QuerySet(FunctionLib).filter(id=function_lib_id).first()
+        valid_function(function_lib, self.flow_params_serializer.data.get('user_id'))
+        params = {field.get('name'): convert_value(field.get('name'), field.get('value'), field.get('type'),
+                                                   field.get('is_required'),
+                                                   field.get('source'), self)
+                  for field in
+                  [{'value': get_field_value(input_field_list, field.get('name'), field.get('is_required'),
+                                             ), **field}
+                   for field in
+                   function_lib.input_field_list]}
+
+        self.context['params'] = params
+        # 合并初始化参数
+        if function_lib.init_params is not None:
+            all_params = json.loads(rsa_long_decrypt(function_lib.init_params)) | params
+        else:
+            all_params = params
+        result = function_executor.exec_code(function_lib.code, all_params)
+        return NodeResult({'result': result}, {}, _write_context=write_context)
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            "result": self.context.get('result'),
+            "params": self.context.get('params'),
+            'run_time': self.context.get('run_time'),
+            'type': self.node.type,
+            'status': self.status,
+            'err_message': self.err_message
+        }

apps/application/flow/step_node/function_node/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/8/13 10:43
+    @desc:
+"""
+from .impl import *

apps/application/flow/step_node/function_node/i_function_node.py

@@ -0,0 +1,63 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： i_function_lib_node.py
+    @date：2024/8/8 16:21
+    @desc:
+"""
+import re
+from typing import Type
+
+from django.core import validators
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.exception.app_exception import AppApiException
+from common.field.common import ObjectField
+from common.util.field_message import ErrMessage
+from django.utils.translation import gettext_lazy as _
+from rest_framework.utils.formatting import lazy_format
+
+
+class InputField(serializers.Serializer):
+    name = serializers.CharField(required=True, error_messages=ErrMessage.char(_('Variable Name')))
+    is_required = serializers.BooleanField(required=True, error_messages=ErrMessage.boolean(_("Is this field required")))
+    type = serializers.CharField(required=True, error_messages=ErrMessage.char(_("type")), validators=[
+        validators.RegexValidator(regex=re.compile("^string|int|dict|array|float$"),
+                                  message=_("The field only supports string|int|dict|array|float"), code=500)
+    ])
+    source = serializers.CharField(required=True, error_messages=ErrMessage.char(_("source")), validators=[
+        validators.RegexValidator(regex=re.compile("^custom|reference$"),
+                                  message=_("The field only supports custom|reference"), code=500)
+    ])
+    value = ObjectField(required=True, error_messages=ErrMessage.char(_("Variable Value")), model_type_list=[str, list])
+
+    def is_valid(self, *, raise_exception=False):
+        super().is_valid(raise_exception=True)
+        is_required = self.data.get('is_required')
+        if is_required and self.data.get('value') is None:
+            message = lazy_format(_('{field}, this field is required.'), field=self.data.get("name"))
+            raise AppApiException(500, message)
+
+
+class FunctionNodeParamsSerializer(serializers.Serializer):
+    input_field_list = InputField(required=True, many=True)
+    code = serializers.CharField(required=True, error_messages=ErrMessage.char(_("function")))
+    is_result = serializers.BooleanField(required=False, error_messages=ErrMessage.boolean(_('Whether to return content')))
+
+    def is_valid(self, *, raise_exception=False):
+        super().is_valid(raise_exception=True)
+
+
+class IFunctionNode(INode):
+    type = 'function-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return FunctionNodeParamsSerializer
+
+    def _run(self):
+        return self.execute(**self.node_params_serializer.data, **self.flow_params_serializer.data)
+
+    def execute(self, input_field_list, code, **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/function_node/impl/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/8/13 11:19
+    @desc:
+"""
+from .base_function_node import BaseFunctionNodeNode

apps/application/flow/step_node/function_node/impl/base_function_node.py

@@ -0,0 +1,108 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： base_function_lib_node.py
+    @date：2024/8/8 17:49
+    @desc:
+"""
+import json
+import time
+
+from typing import Dict
+
+from application.flow.i_step_node import NodeResult
+from application.flow.step_node.function_node.i_function_node import IFunctionNode
+from common.exception.app_exception import AppApiException
+from common.util.function_code import FunctionExecutor
+from smartdoc.const import CONFIG
+
+function_executor = FunctionExecutor(CONFIG.get('SANDBOX'))
+
+
+def write_context(step_variable: Dict, global_variable: Dict, node, workflow):
+    if step_variable is not None:
+        for key in step_variable:
+            node.context[key] = step_variable[key]
+        if workflow.is_result(node, NodeResult(step_variable, global_variable)) and 'result' in step_variable:
+            result = str(step_variable['result']) + '\n'
+            yield result
+            node.answer_text = result
+    node.context['run_time'] = time.time() - node.context['start_time']
+
+
+def valid_reference_value(_type, value, name):
+    if _type == 'int':
+        instance_type = int | float
+    elif _type == 'float':
+        instance_type = float | int
+    elif _type == 'dict':
+        instance_type = dict
+    elif _type == 'array':
+        instance_type = list
+    elif _type == 'string':
+        instance_type = str
+    else:
+        raise Exception(500, f'字段:{name}类型:{_type} 不支持的类型')
+    if not isinstance(value, instance_type):
+        raise Exception(f'字段:{name}类型:{_type}值:{value}类型错误')
+
+
+def convert_value(name: str, value, _type, is_required, source, node):
+    if not is_required and (value is None or (isinstance(value, str) and len(value) == 0)):
+        return None
+    if source == 'reference':
+        value = node.workflow_manage.get_reference_field(
+            value[0],
+            value[1:])
+        valid_reference_value(_type, value, name)
+        if _type == 'int':
+            return int(value)
+        if _type == 'float':
+            return float(value)
+        return value
+    try:
+        if _type == 'int':
+            return int(value)
+        if _type == 'float':
+            return float(value)
+        if _type == 'dict':
+            v = json.loads(value)
+            if isinstance(v, dict):
+                return v
+            raise Exception("类型错误")
+        if _type == 'array':
+            v = json.loads(value)
+            if isinstance(v, list):
+                return v
+            raise Exception("类型错误")
+        return value
+    except Exception as e:
+        raise Exception(f'字段:{name}类型:{_type}值:{value}类型错误')
+
+
+class BaseFunctionNodeNode(IFunctionNode):
+    def save_context(self, details, workflow_manage):
+        self.context['result'] = details.get('result')
+        if self.node_params.get('is_result', False):
+            self.answer_text = str(details.get('result'))
+
+    def execute(self, input_field_list, code, **kwargs) -> NodeResult:
+        params = {field.get('name'): convert_value(field.get('name'), field.get('value'), field.get('type'),
+                                                   field.get('is_required'), field.get('source'), self)
+                  for field in input_field_list}
+        result = function_executor.exec_code(code, params)
+        self.context['params'] = params
+        return NodeResult({'result': result}, {}, _write_context=write_context)
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            "result": self.context.get('result'),
+            "params": self.context.get('params'),
+            'run_time': self.context.get('run_time'),
+            'type': self.node.type,
+            'status': self.status,
+            'err_message': self.err_message
+        }

apps/application/flow/step_node/image_generate_step_node/__init__.py

@@ -0,0 +1,3 @@
+# coding=utf-8
+
+from .impl import *

apps/application/flow/step_node/image_generate_step_node/i_image_generate_node.py

@@ -0,0 +1,45 @@
+# coding=utf-8
+
+from typing import Type
+
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.field_message import ErrMessage
+from django.utils.translation import gettext_lazy as _
+
+
+class ImageGenerateNodeSerializer(serializers.Serializer):
+    model_id = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Model id")))
+
+    prompt = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Prompt word (positive)")))
+
+    negative_prompt = serializers.CharField(required=False, error_messages=ErrMessage.char(_("Prompt word (negative)")),
+                                            allow_null=True, allow_blank=True, )
+    # 多轮对话数量
+    dialogue_number = serializers.IntegerField(required=False, default=0,
+                                               error_messages=ErrMessage.integer(_("Number of multi-round conversations")))
+
+    dialogue_type = serializers.CharField(required=False, default='NODE',
+                                          error_messages=ErrMessage.char(_("Conversation storage type")))
+
+    is_result = serializers.BooleanField(required=False, error_messages=ErrMessage.boolean(_('Whether to return content')))
+
+    model_params_setting = serializers.JSONField(required=False, default=dict,
+                                                 error_messages=ErrMessage.json(_("Model parameter settings")))
+
+
+class IImageGenerateNode(INode):
+    type = 'image-generate-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return ImageGenerateNodeSerializer
+
+    def _run(self):
+        return self.execute(**self.node_params_serializer.data, **self.flow_params_serializer.data)
+
+    def execute(self, model_id, prompt, negative_prompt, dialogue_number, dialogue_type, history_chat_record, chat_id,
+                model_params_setting,
+                chat_record_id,
+                **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/image_generate_step_node/impl/__init__.py

@@ -0,0 +1,3 @@
+# coding=utf-8
+
+from .base_image_generate_node import BaseImageGenerateNode

apps/application/flow/step_node/image_generate_step_node/impl/base_image_generate_node.py

@@ -0,0 +1,122 @@
+# coding=utf-8
+from functools import reduce
+from typing import List
+
+import requests
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
+
+from application.flow.i_step_node import NodeResult
+from application.flow.step_node.image_generate_step_node.i_image_generate_node import IImageGenerateNode
+from common.util.common import bytes_to_uploaded_file
+from dataset.serializers.file_serializers import FileSerializer
+from setting.models_provider.tools import get_model_instance_by_model_user_id
+
+
+class BaseImageGenerateNode(IImageGenerateNode):
+    def save_context(self, details, workflow_manage):
+        self.context['answer'] = details.get('answer')
+        self.context['question'] = details.get('question')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
+
+    def execute(self, model_id, prompt, negative_prompt, dialogue_number, dialogue_type, history_chat_record, chat_id,
+                model_params_setting,
+                chat_record_id,
+                **kwargs) -> NodeResult:
+        print(model_params_setting)
+        application = self.workflow_manage.work_flow_post_handler.chat_info.application
+        tti_model = get_model_instance_by_model_user_id(model_id, self.flow_params_serializer.data.get('user_id'),
+                                                        **model_params_setting)
+        history_message = self.get_history_message(history_chat_record, dialogue_number)
+        self.context['history_message'] = history_message
+        question = self.generate_prompt_question(prompt)
+        self.context['question'] = question
+        message_list = self.generate_message_list(question, history_message)
+        self.context['message_list'] = message_list
+        self.context['dialogue_type'] = dialogue_type
+        print(message_list)
+        image_urls = tti_model.generate_image(question, negative_prompt)
+        # 保存图片
+        file_urls = []
+        for image_url in image_urls:
+            file_name = 'generated_image.png'
+            file = bytes_to_uploaded_file(requests.get(image_url).content, file_name)
+            meta = {
+                'debug': False if application.id else True,
+                'chat_id': chat_id,
+                'application_id': str(application.id) if application.id else None,
+            }
+            file_url = FileSerializer(data={'file': file, 'meta': meta}).upload()
+            file_urls.append(file_url)
+        self.context['image_list'] = [{'file_id': path.split('/')[-1], 'url': path} for path in file_urls]
+        answer = ' '.join([f"![Image]({path})" for path in file_urls])
+        return NodeResult({'answer': answer, 'chat_model': tti_model, 'message_list': message_list,
+                           'image': [{'file_id': path.split('/')[-1], 'url': path} for path in file_urls],
+                           'history_message': history_message, 'question': question}, {})
+
+    def generate_history_ai_message(self, chat_record):
+        for val in chat_record.details.values():
+            if self.node.id == val['node_id'] and 'image_list' in val:
+                if val['dialogue_type'] == 'WORKFLOW':
+                    return chat_record.get_ai_message()
+                image_list = val['image_list']
+                return AIMessage(content=[
+                    *[{'type': 'image_url', 'image_url': {'url': f'{file_url}'}} for file_url in image_list]
+                ])
+        return chat_record.get_ai_message()
+
+    def get_history_message(self, history_chat_record, dialogue_number):
+        start_index = len(history_chat_record) - dialogue_number
+        history_message = reduce(lambda x, y: [*x, *y], [
+            [self.generate_history_human_message(history_chat_record[index]),
+             self.generate_history_ai_message(history_chat_record[index])]
+            for index in
+            range(start_index if start_index > 0 else 0, len(history_chat_record))], [])
+        return history_message
+
+    def generate_history_human_message(self, chat_record):
+
+        for data in chat_record.details.values():
+            if self.node.id == data['node_id'] and 'image_list' in data:
+                image_list = data['image_list']
+                if len(image_list) == 0 or data['dialogue_type'] == 'WORKFLOW':
+                    return HumanMessage(content=chat_record.problem_text)
+                return HumanMessage(content=data['question'])
+        return HumanMessage(content=chat_record.problem_text)
+
+    def generate_prompt_question(self, prompt):
+        return self.workflow_manage.generate_prompt(prompt)
+
+    def generate_message_list(self, question: str, history_message):
+        return [
+            *history_message,
+            question
+        ]
+
+    @staticmethod
+    def reset_message_list(message_list: List[BaseMessage], answer_text):
+        result = [{'role': 'user' if isinstance(message, HumanMessage) else 'ai', 'content': message.content} for
+                  message
+                  in
+                  message_list]
+        result.append({'role': 'ai', 'content': answer_text})
+        return result
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            'run_time': self.context.get('run_time'),
+            'history_message': [{'content': message.content, 'role': message.type} for message in
+                                (self.context.get('history_message') if self.context.get(
+                                    'history_message') is not None else [])],
+            'question': self.context.get('question'),
+            'answer': self.context.get('answer'),
+            'type': self.node.type,
+            'message_tokens': self.context.get('message_tokens'),
+            'answer_tokens': self.context.get('answer_tokens'),
+            'status': self.status,
+            'err_message': self.err_message,
+            'image_list': self.context.get('image_list'),
+            'dialogue_type': self.context.get('dialogue_type')
+        }

apps/application/flow/step_node/image_understand_step_node/__init__.py

@@ -0,0 +1,3 @@
+# coding=utf-8
+
+from .impl import *

apps/application/flow/step_node/image_understand_step_node/i_image_understand_node.py

@@ -0,0 +1,46 @@
+# coding=utf-8
+
+from typing import Type
+
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.field_message import ErrMessage
+from django.utils.translation import gettext_lazy as _
+
+
+class ImageUnderstandNodeSerializer(serializers.Serializer):
+    model_id = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Model id")))
+    system = serializers.CharField(required=False, allow_blank=True, allow_null=True,
+                                   error_messages=ErrMessage.char(_("Role Setting")))
+    prompt = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Prompt word")))
+    # 多轮对话数量
+    dialogue_number = serializers.IntegerField(required=True, error_messages=ErrMessage.integer(_("Number of multi-round conversations")))
+
+    dialogue_type = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Conversation storage type")))
+
+    is_result = serializers.BooleanField(required=False, error_messages=ErrMessage.boolean(_('Whether to return content')))
+
+    image_list = serializers.ListField(required=False, error_messages=ErrMessage.list(_("picture")))
+
+    model_params_setting = serializers.JSONField(required=False, default=dict,
+                                                 error_messages=ErrMessage.json(_("Model parameter settings")))
+
+
+class IImageUnderstandNode(INode):
+    type = 'image-understand-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return ImageUnderstandNodeSerializer
+
+    def _run(self):
+        res = self.workflow_manage.get_reference_field(self.node_params_serializer.data.get('image_list')[0],
+                                                       self.node_params_serializer.data.get('image_list')[1:])
+        return self.execute(image=res, **self.node_params_serializer.data, **self.flow_params_serializer.data)
+
+    def execute(self, model_id, system, prompt, dialogue_number, dialogue_type, history_chat_record, stream, chat_id,
+                model_params_setting,
+                chat_record_id,
+                image,
+                **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/image_understand_step_node/impl/__init__.py

@@ -0,0 +1,3 @@
+# coding=utf-8
+
+from .base_image_understand_node import BaseImageUnderstandNode

apps/application/flow/step_node/image_understand_step_node/impl/base_image_understand_node.py

@@ -0,0 +1,224 @@
+# coding=utf-8
+import base64
+import os
+import time
+from functools import reduce
+from typing import List, Dict
+
+from django.db.models import QuerySet
+from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage, AIMessage
+
+from application.flow.i_step_node import NodeResult, INode
+from application.flow.step_node.image_understand_step_node.i_image_understand_node import IImageUnderstandNode
+from dataset.models import File
+from setting.models_provider.tools import get_model_instance_by_model_user_id
+from imghdr import what
+
+
+def _write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow, answer: str):
+    chat_model = node_variable.get('chat_model')
+    message_tokens = node_variable['usage_metadata']['output_tokens'] if 'usage_metadata' in node_variable else 0
+    answer_tokens = chat_model.get_num_tokens(answer)
+    node.context['message_tokens'] = message_tokens
+    node.context['answer_tokens'] = answer_tokens
+    node.context['answer'] = answer
+    node.context['history_message'] = node_variable['history_message']
+    node.context['question'] = node_variable['question']
+    node.context['run_time'] = time.time() - node.context['start_time']
+    if workflow.is_result(node, NodeResult(node_variable, workflow_variable)):
+        node.answer_text = answer
+
+
+def write_context_stream(node_variable: Dict, workflow_variable: Dict, node: INode, workflow):
+    """
+    写入上下文数据 (流式)
+    @param node_variable:      节点数据
+    @param workflow_variable:  全局数据
+    @param node:               节点
+    @param workflow:           工作流管理器
+    """
+    response = node_variable.get('result')
+    answer = ''
+    for chunk in response:
+        answer += chunk.content
+        yield chunk.content
+    _write_context(node_variable, workflow_variable, node, workflow, answer)
+
+
+def write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow):
+    """
+    写入上下文数据
+    @param node_variable:      节点数据
+    @param workflow_variable:  全局数据
+    @param node:               节点实例对象
+    @param workflow:           工作流管理器
+    """
+    response = node_variable.get('result')
+    answer = response.content
+    _write_context(node_variable, workflow_variable, node, workflow, answer)
+
+
+def file_id_to_base64(file_id: str):
+    file = QuerySet(File).filter(id=file_id).first()
+    file_bytes = file.get_byte()
+    base64_image = base64.b64encode(file_bytes).decode("utf-8")
+    return [base64_image, what(None, file_bytes.tobytes())]
+
+
+class BaseImageUnderstandNode(IImageUnderstandNode):
+    def save_context(self, details, workflow_manage):
+        self.context['answer'] = details.get('answer')
+        self.context['question'] = details.get('question')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
+
+    def execute(self, model_id, system, prompt, dialogue_number, dialogue_type, history_chat_record, stream, chat_id,
+                model_params_setting,
+                chat_record_id,
+                image,
+                **kwargs) -> NodeResult:
+        # 处理不正确的参数
+        if image is None or not isinstance(image, list):
+            image = []
+        print(model_params_setting)
+        image_model = get_model_instance_by_model_user_id(model_id, self.flow_params_serializer.data.get('user_id'), **model_params_setting)
+        # 执行详情中的历史消息不需要图片内容
+        history_message = self.get_history_message_for_details(history_chat_record, dialogue_number)
+        self.context['history_message'] = history_message
+        question = self.generate_prompt_question(prompt)
+        self.context['question'] = question.content
+        # 生成消息列表, 真实的history_message
+        message_list = self.generate_message_list(image_model, system, prompt,
+                                                  self.get_history_message(history_chat_record, dialogue_number), image)
+        self.context['message_list'] = message_list
+        self.context['image_list'] = image
+        self.context['dialogue_type'] = dialogue_type
+        if stream:
+            r = image_model.stream(message_list)
+            return NodeResult({'result': r, 'chat_model': image_model, 'message_list': message_list,
+                               'history_message': history_message, 'question': question.content}, {},
+                              _write_context=write_context_stream)
+        else:
+            r = image_model.invoke(message_list)
+            return NodeResult({'result': r, 'chat_model': image_model, 'message_list': message_list,
+                               'history_message': history_message, 'question': question.content}, {},
+                              _write_context=write_context)
+
+    def get_history_message_for_details(self, history_chat_record, dialogue_number):
+        start_index = len(history_chat_record) - dialogue_number
+        history_message = reduce(lambda x, y: [*x, *y], [
+            [self.generate_history_human_message_for_details(history_chat_record[index]),
+             self.generate_history_ai_message(history_chat_record[index])]
+            for index in
+            range(start_index if start_index > 0 else 0, len(history_chat_record))], [])
+        return history_message
+
+    def generate_history_ai_message(self, chat_record):
+        for val in chat_record.details.values():
+            if self.node.id == val['node_id'] and 'image_list' in val:
+                if val['dialogue_type'] == 'WORKFLOW':
+                    return chat_record.get_ai_message()
+                return AIMessage(content=val['answer'])
+        return chat_record.get_ai_message()
+
+    def generate_history_human_message_for_details(self, chat_record):
+        for data in chat_record.details.values():
+            if self.node.id == data['node_id'] and 'image_list' in data:
+                image_list = data['image_list']
+                if len(image_list) == 0 or data['dialogue_type'] == 'WORKFLOW':
+                    return HumanMessage(content=chat_record.problem_text)
+                file_id_list = [image.get('file_id') for image in image_list]
+                return HumanMessage(content=[
+                    {'type': 'text', 'text': data['question']},
+                    *[{'type': 'image_url', 'image_url': {'url': f'/api/file/{file_id}'}} for file_id in file_id_list]
+
+                ])
+        return HumanMessage(content=chat_record.problem_text)
+
+    def get_history_message(self, history_chat_record, dialogue_number):
+        start_index = len(history_chat_record) - dialogue_number
+        history_message = reduce(lambda x, y: [*x, *y], [
+            [self.generate_history_human_message(history_chat_record[index]),
+             self.generate_history_ai_message(history_chat_record[index])]
+            for index in
+            range(start_index if start_index > 0 else 0, len(history_chat_record))], [])
+        return history_message
+
+    def generate_history_human_message(self, chat_record):
+
+        for data in chat_record.details.values():
+            if self.node.id == data['node_id'] and 'image_list' in data:
+                image_list = data['image_list']
+                if len(image_list) == 0 or data['dialogue_type'] == 'WORKFLOW':
+                    return HumanMessage(content=chat_record.problem_text)
+                image_base64_list = [file_id_to_base64(image.get('file_id')) for image in image_list]
+                return HumanMessage(
+                    content=[
+                        {'type': 'text', 'text': data['question']},
+                        *[{'type': 'image_url', 'image_url': {'url': f'data:image/{base64_image[1]};base64,{base64_image[0]}'}} for
+                          base64_image in image_base64_list]
+                    ])
+        return HumanMessage(content=chat_record.problem_text)
+
+    def generate_prompt_question(self, prompt):
+        return HumanMessage(self.workflow_manage.generate_prompt(prompt))
+
+    def generate_message_list(self, image_model, system: str, prompt: str, history_message, image):
+        if image is not None and len(image) > 0:
+            # 处理多张图片
+            images = []
+            for img in image:
+                file_id = img['file_id']
+                file = QuerySet(File).filter(id=file_id).first()
+                image_bytes = file.get_byte()
+                base64_image = base64.b64encode(image_bytes).decode("utf-8")
+                image_format = what(None, image_bytes.tobytes())
+                images.append({'type': 'image_url', 'image_url': {'url': f'data:image/{image_format};base64,{base64_image}'}})
+            messages = [HumanMessage(
+                content=[
+                    {'type': 'text', 'text': self.workflow_manage.generate_prompt(prompt)},
+                    *images
+                ])]
+        else:
+            messages = [HumanMessage(self.workflow_manage.generate_prompt(prompt))]
+
+        if system is not None and len(system) > 0:
+            return [
+                SystemMessage(self.workflow_manage.generate_prompt(system)),
+                *history_message,
+                *messages
+            ]
+        else:
+            return [
+                *history_message,
+                *messages
+            ]
+
+    @staticmethod
+    def reset_message_list(message_list: List[BaseMessage], answer_text):
+        result = [{'role': 'user' if isinstance(message, HumanMessage) else 'ai', 'content': message.content} for
+                  message
+                  in
+                  message_list]
+        result.append({'role': 'ai', 'content': answer_text})
+        return result
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            'run_time': self.context.get('run_time'),
+            'system': self.node_params.get('system'),
+            'history_message': [{'content': message.content, 'role': message.type} for message in
+                                (self.context.get('history_message') if self.context.get(
+                                    'history_message') is not None else [])],
+            'question': self.context.get('question'),
+            'answer': self.context.get('answer'),
+            'type': self.node.type,
+            'message_tokens': self.context.get('message_tokens'),
+            'answer_tokens': self.context.get('answer_tokens'),
+            'status': self.status,
+            'err_message': self.err_message,
+            'image_list': self.context.get('image_list'),
+            'dialogue_type': self.context.get('dialogue_type')
+        }

apps/application/flow/step_node/mcp_node/__init__.py

@@ -0,0 +1,3 @@
+# coding=utf-8
+
+from .impl import *

apps/application/flow/step_node/mcp_node/i_mcp_node.py

@@ -0,0 +1,35 @@
+# coding=utf-8
+
+from typing import Type
+
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.field_message import ErrMessage
+from django.utils.translation import gettext_lazy as _
+
+
+class McpNodeSerializer(serializers.Serializer):
+    mcp_servers = serializers.JSONField(required=True,
+                                        error_messages=ErrMessage.char(_("Mcp servers")))
+
+    mcp_server = serializers.CharField(required=True,
+                                       error_messages=ErrMessage.char(_("Mcp server")))
+
+    mcp_tool = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Mcp tool")))
+
+    tool_params = serializers.DictField(required=True,
+                                        error_messages=ErrMessage.char(_("Tool parameters")))
+
+
+class IMcpNode(INode):
+    type = 'mcp-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return McpNodeSerializer
+
+    def _run(self):
+        return self.execute(**self.node_params_serializer.data, **self.flow_params_serializer.data)
+
+    def execute(self, mcp_servers, mcp_server, mcp_tool, tool_params, **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/mcp_node/impl/__init__.py

@@ -0,0 +1,3 @@
+# coding=utf-8
+
+from .base_mcp_node import BaseMcpNode

apps/application/flow/step_node/mcp_node/impl/base_mcp_node.py

@@ -0,0 +1,61 @@
+# coding=utf-8
+import asyncio
+import json
+from typing import List
+
+from langchain_mcp_adapters.client import MultiServerMCPClient
+
+from application.flow.i_step_node import NodeResult
+from application.flow.step_node.mcp_node.i_mcp_node import IMcpNode
+
+
+class BaseMcpNode(IMcpNode):
+    def save_context(self, details, workflow_manage):
+        self.context['result'] = details.get('result')
+        self.context['tool_params'] = details.get('tool_params')
+        self.context['mcp_tool'] = details.get('mcp_tool')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('result')
+
+    def execute(self, mcp_servers, mcp_server, mcp_tool, tool_params, **kwargs) -> NodeResult:
+        servers = json.loads(mcp_servers)
+        params = json.loads(json.dumps(tool_params))
+        params = self.handle_variables(params)
+
+        async def call_tool(s, session, t, a):
+            async with MultiServerMCPClient(s) as client:
+                s = await client.sessions[session].call_tool(t, a)
+                return s
+
+        res = asyncio.run(call_tool(servers, mcp_server, mcp_tool, params))
+        return NodeResult(
+            {'result': [content.text for content in res.content], 'tool_params': params, 'mcp_tool': mcp_tool}, {})
+
+    def handle_variables(self, tool_params):
+        # 处理参数中的变量
+        for k, v in tool_params.items():
+            if type(v) == str:
+                tool_params[k] = self.workflow_manage.generate_prompt(tool_params[k])
+            if type(v) == dict:
+                self.handle_variables(v)
+            if (type(v) == list) and (type(v[0]) == str):
+                tool_params[k] = self.get_reference_content(v)
+        return tool_params
+
+    def get_reference_content(self, fields: List[str]):
+        return str(self.workflow_manage.get_reference_field(
+            fields[0],
+            fields[1:]))
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            'run_time': self.context.get('run_time'),
+            'status': self.status,
+            'err_message': self.err_message,
+            'type': self.node.type,
+            'mcp_tool': self.context.get('mcp_tool'),
+            'tool_params': self.context.get('tool_params'),
+            'result': self.context.get('result'),
+        }

apps/application/flow/step_node/question_node/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py
+    @date：2024/6/11 15:30
+    @desc:
+"""
+from .impl import *

apps/application/flow/step_node/question_node/i_question_node.py

@@ -0,0 +1,42 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： i_chat_node.py
+    @date：2024/6/4 13:58
+    @desc:
+"""
+from typing import Type
+
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.field_message import ErrMessage
+from django.utils.translation import gettext_lazy as _
+
+
+class QuestionNodeSerializer(serializers.Serializer):
+    model_id = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Model id")))
+    system = serializers.CharField(required=False, allow_blank=True, allow_null=True,
+                                   error_messages=ErrMessage.char(_("Role Setting")))
+    prompt = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Prompt word")))
+    # 多轮对话数量
+    dialogue_number = serializers.IntegerField(required=True, error_messages=ErrMessage.integer(_("Number of multi-round conversations")))
+
+    is_result = serializers.BooleanField(required=False, error_messages=ErrMessage.boolean(_('Whether to return content')))
+    model_params_setting = serializers.DictField(required=False, error_messages=ErrMessage.integer(_("Model parameter settings")))
+
+
+class IQuestionNode(INode):
+    type = 'question-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return QuestionNodeSerializer
+
+    def _run(self):
+        return self.execute(**self.node_params_serializer.data, **self.flow_params_serializer.data)
+
+    def execute(self, model_id, system, prompt, dialogue_number, history_chat_record, stream, chat_id, chat_record_id,
+                model_params_setting=None,
+                **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/question_node/impl/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py
+    @date：2024/6/11 15:35
+    @desc:
+"""
+from .base_question_node import BaseQuestionNode

apps/application/flow/step_node/question_node/impl/base_question_node.py

@@ -0,0 +1,159 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： base_question_node.py
+    @date：2024/6/4 14:30
+    @desc:
+"""
+import re
+import time
+from functools import reduce
+from typing import List, Dict
+
+from django.db.models import QuerySet
+from langchain.schema import HumanMessage, SystemMessage
+from langchain_core.messages import BaseMessage
+
+from application.flow.i_step_node import NodeResult, INode
+from application.flow.step_node.question_node.i_question_node import IQuestionNode
+from setting.models import Model
+from setting.models_provider import get_model_credential
+from setting.models_provider.tools import get_model_instance_by_model_user_id
+
+
+def _write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow, answer: str):
+    chat_model = node_variable.get('chat_model')
+    message_tokens = chat_model.get_num_tokens_from_messages(node_variable.get('message_list'))
+    answer_tokens = chat_model.get_num_tokens(answer)
+    node.context['message_tokens'] = message_tokens
+    node.context['answer_tokens'] = answer_tokens
+    node.context['answer'] = answer
+    node.context['history_message'] = node_variable['history_message']
+    node.context['question'] = node_variable['question']
+    node.context['run_time'] = time.time() - node.context['start_time']
+    if workflow.is_result(node, NodeResult(node_variable, workflow_variable)):
+        node.answer_text = answer
+
+
+def write_context_stream(node_variable: Dict, workflow_variable: Dict, node: INode, workflow):
+    """
+    写入上下文数据 (流式)
+    @param node_variable:      节点数据
+    @param workflow_variable:  全局数据
+    @param node:               节点
+    @param workflow:           工作流管理器
+    """
+    response = node_variable.get('result')
+    answer = ''
+    for chunk in response:
+        answer += chunk.content
+        yield chunk.content
+    _write_context(node_variable, workflow_variable, node, workflow, answer)
+
+
+def write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow):
+    """
+    写入上下文数据
+    @param node_variable:      节点数据
+    @param workflow_variable:  全局数据
+    @param node:               节点实例对象
+    @param workflow:           工作流管理器
+    """
+    response = node_variable.get('result')
+    answer = response.content
+    _write_context(node_variable, workflow_variable, node, workflow, answer)
+
+
+def get_default_model_params_setting(model_id):
+    model = QuerySet(Model).filter(id=model_id).first()
+    credential = get_model_credential(model.provider, model.model_type, model.model_name)
+    model_params_setting = credential.get_model_params_setting_form(
+        model.model_name).get_default_form_data()
+    return model_params_setting
+
+
+class BaseQuestionNode(IQuestionNode):
+    def save_context(self, details, workflow_manage):
+        self.context['run_time'] = details.get('run_time')
+        self.context['question'] = details.get('question')
+        self.context['answer'] = details.get('answer')
+        self.context['message_tokens'] = details.get('message_tokens')
+        self.context['answer_tokens'] = details.get('answer_tokens')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
+
+    def execute(self, model_id, system, prompt, dialogue_number, history_chat_record, stream, chat_id, chat_record_id,
+                model_params_setting=None,
+                **kwargs) -> NodeResult:
+        if model_params_setting is None:
+            model_params_setting = get_default_model_params_setting(model_id)
+        chat_model = get_model_instance_by_model_user_id(model_id, self.flow_params_serializer.data.get('user_id'),
+                                                         **model_params_setting)
+        history_message = self.get_history_message(history_chat_record, dialogue_number)
+        self.context['history_message'] = history_message
+        question = self.generate_prompt_question(prompt)
+        self.context['question'] = question.content
+        system = self.workflow_manage.generate_prompt(system)
+        self.context['system'] = system
+        message_list = self.generate_message_list(system, prompt, history_message)
+        self.context['message_list'] = message_list
+        if stream:
+            r = chat_model.stream(message_list)
+            return NodeResult({'result': r, 'chat_model': chat_model, 'message_list': message_list,
+                               'history_message': history_message, 'question': question.content}, {},
+                              _write_context=write_context_stream)
+        else:
+            r = chat_model.invoke(message_list)
+            return NodeResult({'result': r, 'chat_model': chat_model, 'message_list': message_list,
+                               'history_message': history_message, 'question': question.content}, {},
+                              _write_context=write_context)
+
+    @staticmethod
+    def get_history_message(history_chat_record, dialogue_number):
+        start_index = len(history_chat_record) - dialogue_number
+        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))], [])
+        for message in history_message:
+            if isinstance(message.content, str):
+                message.content = re.sub('<form_rander>[\d\D]*?<\/form_rander>', '', message.content)
+        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:
+            return [SystemMessage(self.workflow_manage.generate_prompt(system)), *history_message,
+                    HumanMessage(self.workflow_manage.generate_prompt(prompt))]
+        else:
+            return [*history_message, HumanMessage(self.workflow_manage.generate_prompt(prompt))]
+
+    @staticmethod
+    def reset_message_list(message_list: List[BaseMessage], answer_text):
+        result = [{'role': 'user' if isinstance(message, HumanMessage) else 'ai', 'content': message.content} for
+                  message
+                  in
+                  message_list]
+        result.append({'role': 'ai', 'content': answer_text})
+        return result
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            'run_time': self.context.get('run_time'),
+            'system': self.context.get('system'),
+            'history_message': [{'content': message.content, 'role': message.type} for message in
+                                (self.context.get('history_message') if self.context.get(
+                                    'history_message') is not None else [])],
+            'question': self.context.get('question'),
+            'answer': self.context.get('answer'),
+            'type': self.node.type,
+            'message_tokens': self.context.get('message_tokens'),
+            'answer_tokens': self.context.get('answer_tokens'),
+            'status': self.status,
+            'err_message': self.err_message
+        }

apps/application/flow/step_node/reranker_node/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： __init__.py
+    @date：2024/9/4 11:37
+    @desc:
+"""
+from .impl import *

apps/application/flow/step_node/reranker_node/i_reranker_node.py

@@ -0,0 +1,60 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： i_reranker_node.py
+    @date：2024/9/4 10:40
+    @desc:
+"""
+from typing import Type
+
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.field_message import ErrMessage
+from django.utils.translation import gettext_lazy as _
+
+
+class RerankerSettingSerializer(serializers.Serializer):
+    # 需要查询的条数
+    top_n = serializers.IntegerField(required=True,
+                                     error_messages=ErrMessage.integer(_("Reference segment number")))
+    # 相似度 0-1之间
+    similarity = serializers.FloatField(required=True, max_value=2, min_value=0,
+                                        error_messages=ErrMessage.float(_("Reference segment number")))
+    max_paragraph_char_number = serializers.IntegerField(required=True,
+                                                         error_messages=ErrMessage.float(_("Maximum number of words in a quoted segment")))
+
+
+class RerankerStepNodeSerializer(serializers.Serializer):
+    reranker_setting = RerankerSettingSerializer(required=True)
+
+    question_reference_address = serializers.ListField(required=True)
+    reranker_model_id = serializers.UUIDField(required=True)
+    reranker_reference_list = serializers.ListField(required=True, child=serializers.ListField(required=True))
+
+    def is_valid(self, *, raise_exception=False):
+        super().is_valid(raise_exception=True)
+
+
+class IRerankerNode(INode):
+    type = 'reranker-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return RerankerStepNodeSerializer
+
+    def _run(self):
+        question = self.workflow_manage.get_reference_field(
+            self.node_params_serializer.data.get('question_reference_address')[0],
+            self.node_params_serializer.data.get('question_reference_address')[1:])
+        reranker_list = [self.workflow_manage.get_reference_field(
+            reference[0],
+            reference[1:]) for reference in
+            self.node_params_serializer.data.get('reranker_reference_list')]
+        return self.execute(**self.node_params_serializer.data, question=str(question),
+
+                            reranker_list=reranker_list)
+
+    def execute(self, question, reranker_setting, reranker_list, reranker_model_id,
+                **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/reranker_node/impl/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： __init__.py
+    @date：2024/9/4 11:39
+    @desc:
+"""
+from .base_reranker_node import *

apps/application/flow/step_node/reranker_node/impl/base_reranker_node.py

@@ -0,0 +1,106 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： base_reranker_node.py
+    @date：2024/9/4 11:41
+    @desc:
+"""
+from typing import List
+
+from langchain_core.documents import Document
+
+from application.flow.i_step_node import NodeResult
+from application.flow.step_node.reranker_node.i_reranker_node import IRerankerNode
+from setting.models_provider.tools import get_model_instance_by_model_user_id
+
+
+def merge_reranker_list(reranker_list, result=None):
+    if result is None:
+        result = []
+    for document in reranker_list:
+        if isinstance(document, list):
+            merge_reranker_list(document, result)
+        elif isinstance(document, dict):
+            content = document.get('title', '') + document.get('content', '')
+            title = document.get("title")
+            dataset_name = document.get("dataset_name")
+            document_name = document.get('document_name')
+            result.append(
+                Document(page_content=str(document) if len(content) == 0 else content,
+                         metadata={'title': title, 'dataset_name': dataset_name, 'document_name': document_name}))
+        else:
+            result.append(Document(page_content=str(document), metadata={}))
+    return result
+
+
+def filter_result(document_list: List[Document], max_paragraph_char_number, top_n, similarity):
+    use_len = 0
+    result = []
+    for index in range(len(document_list)):
+        document = document_list[index]
+        if use_len >= max_paragraph_char_number or index >= top_n or document.metadata.get(
+                'relevance_score') < similarity:
+            break
+        content = document.page_content[0:max_paragraph_char_number - use_len]
+        use_len = use_len + len(content)
+        result.append({'page_content': content, 'metadata': document.metadata})
+    return result
+
+
+def reset_result_list(result_list: List[Document], document_list: List[Document]):
+    r = []
+    document_list = document_list.copy()
+    for result in result_list:
+        filter_result_list = [document for document in document_list if document.page_content == result.page_content]
+        if len(filter_result_list) > 0:
+            item = filter_result_list[0]
+            document_list.remove(item)
+            r.append(Document(page_content=item.page_content,
+                              metadata={**item.metadata, 'relevance_score': result.metadata.get('relevance_score')}))
+        else:
+            r.append(result)
+    return r
+
+
+class BaseRerankerNode(IRerankerNode):
+    def save_context(self, details, workflow_manage):
+        self.context['document_list'] = details.get('document_list', [])
+        self.context['question'] = details.get('question')
+        self.context['run_time'] = details.get('run_time')
+        self.context['result_list'] = details.get('result_list')
+        self.context['result'] = details.get('result')
+
+    def execute(self, question, reranker_setting, reranker_list, reranker_model_id,
+                **kwargs) -> NodeResult:
+        documents = merge_reranker_list(reranker_list)
+        top_n = reranker_setting.get('top_n', 3)
+        self.context['document_list'] = [{'page_content': document.page_content, 'metadata': document.metadata} for
+                                         document in documents]
+        self.context['question'] = question
+        reranker_model = get_model_instance_by_model_user_id(reranker_model_id,
+                                                             self.flow_params_serializer.data.get('user_id'),
+                                                             top_n=top_n)
+        result = reranker_model.compress_documents(
+            documents,
+            question)
+        similarity = reranker_setting.get('similarity', 0.6)
+        max_paragraph_char_number = reranker_setting.get('max_paragraph_char_number', 5000)
+        result = reset_result_list(result, documents)
+        r = filter_result(result, max_paragraph_char_number, top_n, similarity)
+        return NodeResult({'result_list': r, 'result': ''.join([item.get('page_content') for item in r])}, {})
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            "index": index,
+            'document_list': self.context.get('document_list'),
+            "question": self.context.get('question'),
+            'run_time': self.context.get('run_time'),
+            'type': self.node.type,
+            'reranker_setting': self.node_params_serializer.data.get('reranker_setting'),
+            'result_list': self.context.get('result_list'),
+            'result': self.context.get('result'),
+            'status': self.status,
+            'err_message': self.err_message
+        }

apps/application/flow/step_node/search_dataset_node/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py
+    @date：2024/6/11 15:30
+    @desc:
+"""
+from .impl import *

apps/application/flow/step_node/search_dataset_node/i_search_dataset_node.py

@@ -0,0 +1,79 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： i_search_dataset_node.py
+    @date：2024/6/3 17:52
+    @desc:
+"""
+import re
+from typing import Type
+
+from django.core import validators
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.common import flat_map
+from common.util.field_message import ErrMessage
+from django.utils.translation import gettext_lazy as _
+
+
+class DatasetSettingSerializer(serializers.Serializer):
+    # 需要查询的条数
+    top_n = serializers.IntegerField(required=True,
+                                     error_messages=ErrMessage.integer(_("Reference segment number")))
+    # 相似度 0-1之间
+    similarity = serializers.FloatField(required=True, max_value=2, min_value=0,
+                                        error_messages=ErrMessage.float(_('similarity')))
+    search_mode = serializers.CharField(required=True, validators=[
+        validators.RegexValidator(regex=re.compile("^embedding|keywords|blend$"),
+                                  message=_("The type only supports embedding|keywords|blend"), code=500)
+    ], error_messages=ErrMessage.char(_("Retrieval Mode")))
+    max_paragraph_char_number = serializers.IntegerField(required=True,
+                                                         error_messages=ErrMessage.float(_("Maximum number of words in a quoted segment")))
+
+
+class SearchDatasetStepNodeSerializer(serializers.Serializer):
+    # 需要查询的数据集id列表
+    dataset_id_list = serializers.ListField(required=True, child=serializers.UUIDField(required=True),
+                                            error_messages=ErrMessage.list(_("Dataset id list")))
+    dataset_setting = DatasetSettingSerializer(required=True)
+
+    question_reference_address = serializers.ListField(required=True)
+
+    def is_valid(self, *, raise_exception=False):
+        super().is_valid(raise_exception=True)
+
+
+def get_paragraph_list(chat_record, node_id):
+    return flat_map([chat_record.details[key].get('paragraph_list', []) for key in chat_record.details if
+                     (chat_record.details[
+                          key].get('type', '') == 'search-dataset-node') and chat_record.details[key].get(
+                         'paragraph_list', []) is not None and key == node_id])
+
+
+class ISearchDatasetStepNode(INode):
+    type = 'search-dataset-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return SearchDatasetStepNodeSerializer
+
+    def _run(self):
+        question = self.workflow_manage.get_reference_field(
+            self.node_params_serializer.data.get('question_reference_address')[0],
+            self.node_params_serializer.data.get('question_reference_address')[1:])
+        exclude_paragraph_id_list = []
+        if self.flow_params_serializer.data.get('re_chat', False):
+            history_chat_record = self.flow_params_serializer.data.get('history_chat_record', [])
+            paragraph_id_list = [p.get('id') for p in flat_map(
+                [get_paragraph_list(chat_record, self.runtime_node_id) for chat_record in history_chat_record if
+                 chat_record.problem_text == question])]
+            exclude_paragraph_id_list = list(set(paragraph_id_list))
+
+        return self.execute(**self.node_params_serializer.data, question=str(question),
+                            exclude_paragraph_id_list=exclude_paragraph_id_list)
+
+    def execute(self, dataset_id_list, dataset_setting, question,
+                exclude_paragraph_id_list=None,
+                **kwargs) -> NodeResult:
+        pass

apps/application/flow/step_node/search_dataset_node/impl/__init__.py

@@ -0,0 +1,9 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py
+    @date：2024/6/11 15:35
+    @desc:
+"""
+from .base_search_dataset_node import BaseSearchDatasetNode

apps/application/flow/step_node/search_dataset_node/impl/base_search_dataset_node.py

@@ -0,0 +1,146 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： base_search_dataset_node.py
+    @date：2024/6/4 11:56
+    @desc:
+"""
+import os
+from typing import List, Dict
+
+from django.db.models import QuerySet
+from django.db import connection
+from application.flow.i_step_node import NodeResult
+from application.flow.step_node.search_dataset_node.i_search_dataset_node import ISearchDatasetStepNode
+from common.config.embedding_config import VectorStore
+from common.db.search import native_search
+from common.util.file_util import get_file_content
+from dataset.models import Document, Paragraph, DataSet
+from embedding.models import SearchMode
+from setting.models_provider.tools import get_model_instance_by_model_user_id
+from smartdoc.conf import PROJECT_DIR
+
+
+def get_embedding_id(dataset_id_list):
+    dataset_list = QuerySet(DataSet).filter(id__in=dataset_id_list)
+    if len(set([dataset.embedding_mode_id for dataset in dataset_list])) > 1:
+        raise Exception("关联知识库的向量模型不一致，无法召回分段。")
+    if len(dataset_list) == 0:
+        raise Exception("知识库设置错误,请重新设置知识库")
+    return dataset_list[0].embedding_mode_id
+
+
+def get_none_result(question):
+    return NodeResult(
+        {'paragraph_list': [], 'is_hit_handling_method': [], 'question': question, 'data': '',
+         'directly_return': ''}, {})
+
+
+def reset_title(title):
+    if title is None or len(title.strip()) == 0:
+        return ""
+    else:
+        return f"#### {title}\n"
+
+
+class BaseSearchDatasetNode(ISearchDatasetStepNode):
+    def save_context(self, details, workflow_manage):
+        result = details.get('paragraph_list', [])
+        dataset_setting = self.node_params_serializer.data.get('dataset_setting')
+        directly_return = '\n'.join(
+            [f"{paragraph.get('title', '')}:{paragraph.get('content')}" for paragraph in result if
+             paragraph.get('is_hit_handling_method')])
+        self.context['paragraph_list'] = result
+        self.context['question'] = details.get('question')
+        self.context['run_time'] = details.get('run_time')
+        self.context['is_hit_handling_method_list'] = [row for row in result if row.get('is_hit_handling_method')]
+        self.context['data'] = '\n'.join(
+            [f"{paragraph.get('title', '')}:{paragraph.get('content')}" for paragraph in
+             result])[0:dataset_setting.get('max_paragraph_char_number', 5000)]
+        self.context['directly_return'] = directly_return
+
+    def execute(self, dataset_id_list, dataset_setting, question,
+                exclude_paragraph_id_list=None,
+                **kwargs) -> NodeResult:
+        self.context['question'] = question
+        if len(dataset_id_list) == 0:
+            return get_none_result(question)
+        model_id = get_embedding_id(dataset_id_list)
+        embedding_model = get_model_instance_by_model_user_id(model_id, self.flow_params_serializer.data.get('user_id'))
+        embedding_value = embedding_model.embed_query(question)
+        vector = VectorStore.get_embedding_vector()
+        exclude_document_id_list = [str(document.id) for document in
+                                    QuerySet(Document).filter(
+                                        dataset_id__in=dataset_id_list,
+                                        is_active=False)]
+        embedding_list = vector.query(question, embedding_value, dataset_id_list, exclude_document_id_list,
+                                      exclude_paragraph_id_list, True, dataset_setting.get('top_n'),
+                                      dataset_setting.get('similarity'), SearchMode(dataset_setting.get('search_mode')))
+        # 手动关闭数据库连接
+        connection.close()
+        if embedding_list is None:
+            return get_none_result(question)
+        paragraph_list = self.list_paragraph(embedding_list, vector)
+        result = [self.reset_paragraph(paragraph, embedding_list) for paragraph in paragraph_list]
+        result = sorted(result, key=lambda p: p.get('similarity'), reverse=True)
+        return NodeResult({'paragraph_list': result,
+                           'is_hit_handling_method_list': [row for row in result if row.get('is_hit_handling_method')],
+                           'data': '\n'.join(
+                               [f"{reset_title(paragraph.get('title', ''))}{paragraph.get('content')}" for paragraph in
+                                result])[0:dataset_setting.get('max_paragraph_char_number', 5000)],
+                           'directly_return': '\n'.join(
+                               [paragraph.get('content') for paragraph in
+                                result if
+                                paragraph.get('is_hit_handling_method')]),
+                           'question': question},
+
+                          {})
+
+    @staticmethod
+    def reset_paragraph(paragraph: Dict, embedding_list: List):
+        filter_embedding_list = [embedding for embedding in embedding_list if
+                                 str(embedding.get('paragraph_id')) == str(paragraph.get('id'))]
+        if filter_embedding_list is not None and len(filter_embedding_list) > 0:
+            find_embedding = filter_embedding_list[-1]
+            return {
+                **paragraph,
+                'similarity': find_embedding.get('similarity'),
+                'is_hit_handling_method': find_embedding.get('similarity') > paragraph.get(
+                    'directly_return_similarity') and paragraph.get('hit_handling_method') == 'directly_return',
+                'update_time': paragraph.get('update_time').strftime("%Y-%m-%d %H:%M:%S"),
+                'create_time': paragraph.get('create_time').strftime("%Y-%m-%d %H:%M:%S"),
+                'id': str(paragraph.get('id')),
+                'dataset_id': str(paragraph.get('dataset_id')),
+                'document_id': str(paragraph.get('document_id'))
+            }
+
+    @staticmethod
+    def list_paragraph(embedding_list: List, vector):
+        paragraph_id_list = [row.get('paragraph_id') for row in embedding_list]
+        if paragraph_id_list is None or len(paragraph_id_list) == 0:
+            return []
+        paragraph_list = native_search(QuerySet(Paragraph).filter(id__in=paragraph_id_list),
+                                       get_file_content(
+                                           os.path.join(PROJECT_DIR, "apps", "application", 'sql',
+                                                        'list_dataset_paragraph_by_paragraph_id.sql')),
+                                       with_table_name=True)
+        # 如果向量库中存在脏数据 直接删除
+        if len(paragraph_list) != len(paragraph_id_list):
+            exist_paragraph_list = [row.get('id') for row in paragraph_list]
+            for paragraph_id in paragraph_id_list:
+                if not exist_paragraph_list.__contains__(paragraph_id):
+                    vector.delete_by_paragraph_id(paragraph_id)
+        return paragraph_list
+
+    def get_details(self, index: int, **kwargs):
+        return {
+            'name': self.node.properties.get('stepName'),
+            'question': self.context.get('question'),
+            "index": index,
+            'run_time': self.context.get('run_time'),
+            'paragraph_list': self.context.get('paragraph_list'),
+            'type': self.node.type,
+            'status': self.status,
+            'err_message': self.err_message
+        }

apps/application/flow/step_node/speech_to_text_step_node/__init__.py

@@ -0,0 +1,3 @@
+# coding=utf-8
+
+from .impl import *

apps/application/flow/step_node/speech_to_text_step_node/i_speech_to_text_node.py

@@ -0,0 +1,38 @@
+# coding=utf-8
+
+from typing import Type
+
+from rest_framework import serializers
+
+from application.flow.i_step_node import INode, NodeResult
+from common.util.field_message import ErrMessage
+from django.utils.translation import gettext_lazy as _
+
+
+class SpeechToTextNodeSerializer(serializers.Serializer):
+    stt_model_id = serializers.CharField(required=True, error_messages=ErrMessage.char(_("Model id")))
+
+    is_result = serializers.BooleanField(required=False, error_messages=ErrMessage.boolean(_('Whether to return content')))
+
+    audio_list = serializers.ListField(required=True, error_messages=ErrMessage.list(_("The audio file cannot be empty")))
+
+
+class ISpeechToTextNode(INode):
+    type = 'speech-to-text-node'
+
+    def get_node_params_serializer_class(self) -> Type[serializers.Serializer]:
+        return SpeechToTextNodeSerializer
+
+    def _run(self):
+        res = self.workflow_manage.get_reference_field(self.node_params_serializer.data.get('audio_list')[0],
+                                                       self.node_params_serializer.data.get('audio_list')[1:])
+        for audio in res:
+            if 'file_id' not in audio:
+                raise ValueError(_("Parameter value error: The uploaded audio lacks file_id, and the audio upload fails"))
+
+        return self.execute(audio=res, **self.node_params_serializer.data, **self.flow_params_serializer.data)
+
+    def execute(self, stt_model_id, chat_id,
+                audio,
+                **kwargs) -> NodeResult:
+        pass