diff --git a/.gitignore b/.gitignore index 3542d1e89..ebc98be1e 100644 --- a/.gitignore +++ b/.gitignore @@ -93,7 +93,7 @@ profile_default/ ipython_config.py # pyenv -.python-version +# .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. diff --git a/.python-version b/.python-version new file mode 100644 index 000000000..2c0733315 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/configs/dbgpt-local-llama-cpp-server.toml b/configs/dbgpt-local-llama-cpp-server.toml index 8a354cdf7..999a94c25 100644 --- a/configs/dbgpt-local-llama-cpp-server.toml +++ b/configs/dbgpt-local-llama-cpp-server.toml @@ -29,6 +29,13 @@ provider = "llama.cpp.server" # path = "the-model-path-in-the-local-file-system" path = "/data/models/qwen2.5-coder-0.5b-instruct-q4_k_m.gguf" +[[models.llms]] +name = "DeepSeek-R1-Distill-Qwen-1.5B" +provider = "llama.cpp.server" +# https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-1.5B-GGUF +path = "/data/models/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf" + + [[models.embeddings]] name = "BAAI/bge-large-zh-v1.5" provider = "hf" diff --git a/configs/dbgpt-local-llama-cpp.toml b/configs/dbgpt-local-llama-cpp.toml new file mode 100644 index 000000000..0f6b88071 --- /dev/null +++ b/configs/dbgpt-local-llama-cpp.toml @@ -0,0 +1,46 @@ +[system] +# Load language from environment variable(It is set by the hook) +language = "${env:DBGPT_LANG:-zh}" +api_keys = [] +encrypt_key = "your_secret_key" + +# Server Configurations +[service.web] +host = "0.0.0.0" +port = 5670 + +[service.web.database] +type = "sqlite" +path = "pilot/meta_data/dbgpt.db" + +[rag.storage] +[rag.storage.vector] +type = "Chroma" +persist_path = "pilot/data" + +# Model Configurations +[models] +[[models.llms]] +name = "Qwen2.5-Coder-0.5B-Instruct" +# Please make sure install DB-GPT with '--extra llama_cpp' extra +provider = "llama.cpp" +# If not provided, the model will be downloaded from the Hugging Face model hub +# uncomment the following line to specify the model path in the local file system +# path = "the-model-path-in-the-local-file-system" +path = "/data/models/qwen2.5-coder-0.5b-instruct-q4_k_m.gguf" + +[[models.llms]] +name = "DeepSeek-R1-Distill-Qwen-1.5B" +provider = "llama.cpp" +# https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-1.5B-GGUF +path = "/data/models/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf" + + +[[models.embeddings]] +name = "BAAI/bge-large-zh-v1.5" +provider = "hf" +# If not provided, the model will be downloaded from the Hugging Face model hub +# uncomment the following line to specify the model path in the local file system +# path = "the-model-path-in-the-local-file-system" +path = "/data/models/bge-large-zh-v1.5" + diff --git a/configs/dbgpt-local-vllm.toml b/configs/dbgpt-local-vllm.toml new file mode 100644 index 000000000..eb0442897 --- /dev/null +++ b/configs/dbgpt-local-vllm.toml @@ -0,0 +1,47 @@ +[system] +# Load language from environment variable(It is set by the hook) +language = "${env:DBGPT_LANG:-zh}" +api_keys = [] +encrypt_key = "your_secret_key" + +# Server Configurations +[service.web] +host = "0.0.0.0" +port = 5670 + +[service.web.database] +type = "sqlite" +path = "pilot/meta_data/dbgpt.db" + +[rag.storage] +[rag.storage.vector] +type = "Chroma" +persist_path = "pilot/data" + +# Model Configurations +[models] +[[models.llms]] +name = "Qwen2.5-Coder-0.5B-Instruct" +provider = "vllm" +# If not provided, the model will be downloaded from the Hugging Face model hub +# uncomment the following line to specify the model path in the local file system +# path = "the-model-path-in-the-local-file-system" +path = "/data/models/Qwen2.5-Coder-0.5B-Instruct" +# dtype="float32" + +# [[models.llms]] +# name = "DeepSeek-R1-Distill-Qwen-1.5B" +# provider = "vllm" +# # If not provided, the model will be downloaded from the Hugging Face model hub +# # uncomment the following line to specify the model path in the local file system +# # path = "the-model-path-in-the-local-file-system" +# path = "/data/models/DeepSeek-R1-Distill-Qwen-1.5B" + +[[models.embeddings]] +name = "BAAI/bge-large-zh-v1.5" +provider = "hf" +# If not provided, the model will be downloaded from the Hugging Face model hub +# uncomment the following line to specify the model path in the local file system +# path = "the-model-path-in-the-local-file-system" +path = "/data/models/bge-large-zh-v1.5" + diff --git a/packages/dbgpt-app/src/dbgpt_app/scene/base_chat.py b/packages/dbgpt-app/src/dbgpt_app/scene/base_chat.py index a6a3d0ff4..24362d543 100644 --- a/packages/dbgpt-app/src/dbgpt_app/scene/base_chat.py +++ b/packages/dbgpt-app/src/dbgpt_app/scene/base_chat.py @@ -33,7 +33,7 @@ from dbgpt_app.scene.operators.app_operator import ( from dbgpt_serve.conversation.serve import Serve as ConversationServe from dbgpt_serve.prompt.service.service import Service as PromptService -from .exceptions import BaseAppException +from .exceptions import BaseAppException, ContextAppException logger = logging.getLogger(__name__) CFG = Config() @@ -306,9 +306,13 @@ class BaseChat(ABC): view_msg = msg async for output in self.call_streaming_operator(payload): # Plugin research in result generation - msg = self.prompt_template.output_parser.parse_model_stream_resp_ex( - output, 0 + model_output = ( + self.prompt_template.output_parser.parse_model_stream_resp_ex( + output, + text_output=False, + ) ) + msg = model_output.gen_text_with_thinking() view_msg = self.stream_plugin_call(msg) view_msg = view_msg.replace("\n", "\\n") yield view_msg @@ -345,7 +349,7 @@ class BaseChat(ABC): self.message_adjust() span.end() except BaseAppException as e: - self.current_message.add_view_message(e.view) + self.current_message.add_view_message(e.get_ui_error()) span.end(metadata={"error": str(e)}) except Exception as e: view_message = f"ERROR! {str(e)}" @@ -367,9 +371,10 @@ class BaseChat(ABC): with root_tracer.start_span("BaseChat.invoke_worker_manager.generate"): model_output = await self.call_llm_operator(payload) - ai_response_text = self.prompt_template.output_parser.parse_model_nostream_resp( - model_output, self.prompt_template.sep + parsed_output = self.prompt_template.output_parser.parse_model_nostream_resp( + model_output, text_output=False ) + ai_response_text = parsed_output.text prompt_define_response = ( self.prompt_template.output_parser.parse_prompt_response(ai_response_text) ) @@ -380,21 +385,35 @@ class BaseChat(ABC): prompt_define_response ), } - with root_tracer.start_span("BaseChat.do_action", metadata=metadata): - result = await blocking_func_to_async( - self._executor, self.do_action, prompt_define_response + try: + with root_tracer.start_span("BaseChat.do_action", metadata=metadata): + result = await blocking_func_to_async( + self._executor, self.do_action, prompt_define_response + ) + + speak_to_user = self.get_llm_speak(prompt_define_response) + view_message = await blocking_func_to_async( + self._executor, + self.prompt_template.output_parser.parse_view_response, + speak_to_user, + result, + prompt_define_response, ) + if parsed_output.has_thinking: + view_message = parsed_output.gen_text_with_thinking( + new_text=view_message + ) + return ai_response_text, view_message.replace("\n", "\\n") + except BaseAppException as e: + raise ContextAppException(e.message, e.view, model_output) from e - speak_to_user = self.get_llm_speak(prompt_define_response) - - view_message = await blocking_func_to_async( - self._executor, - self.prompt_template.output_parser.parse_view_response, - speak_to_user, - result, - prompt_define_response, - ) - return ai_response_text, view_message.replace("\n", "\\n") + except Exception as e: + logger.error("model response parse failed！" + str(e)) + raise ContextAppException( + f"model response parse failed！{str(e)}\n {ai_response_text}", + f"ERROR! {str(e)}", + model_output, + ) @Deprecated(version="0.7.0", remove_version="0.8.0") async def get_llm_response(self): @@ -407,7 +426,7 @@ class BaseChat(ABC): ### output parse ai_response_text = ( self.prompt_template.output_parser.parse_model_nostream_resp( - model_output, self.prompt_template.sep + model_output ) ) ### model result deal diff --git a/packages/dbgpt-app/src/dbgpt_app/scene/chat_dashboard/out_parser.py b/packages/dbgpt-app/src/dbgpt_app/scene/chat_dashboard/out_parser.py index 057d23a4d..8201b6d0e 100644 --- a/packages/dbgpt-app/src/dbgpt_app/scene/chat_dashboard/out_parser.py +++ b/packages/dbgpt-app/src/dbgpt_app/scene/chat_dashboard/out_parser.py @@ -39,7 +39,7 @@ class ChatDashboardOutputParser(BaseOutputParser): return chart_items def parse_view_response(self, speak, data, prompt_response) -> str: - return json.dumps(data.prepare_dict()) + return json.dumps(data.prepare_dict(), ensure_ascii=False) @property def _type(self) -> str: diff --git a/packages/dbgpt-app/src/dbgpt_app/scene/exceptions.py b/packages/dbgpt-app/src/dbgpt_app/scene/exceptions.py index 9b4deb461..04081460f 100644 --- a/packages/dbgpt-app/src/dbgpt_app/scene/exceptions.py +++ b/packages/dbgpt-app/src/dbgpt_app/scene/exceptions.py @@ -2,6 +2,8 @@ import logging +from dbgpt.core import ModelOutput + logger = logging.getLogger(__name__) @@ -14,6 +16,10 @@ class BaseAppException(Exception): self.message = message self.view = view + def get_ui_error(self) -> str: + """Get UI Error""" + return self.view + class AppActionException(BaseAppException): """Exception for App Action.""" @@ -21,3 +27,18 @@ class AppActionException(BaseAppException): def __init__(self, message: str, view: str): """Exception for App Action""" super().__init__(message, view) + + +class ContextAppException(BaseAppException): + """Exception for App Context.""" + + def __init__(self, message: str, view: str, model_output: ModelOutput): + """Exception for App Context""" + super().__init__(message, view) + self.model_output: ModelOutput = model_output + + def get_ui_error(self) -> str: + """Get UI Error""" + if self.model_output.has_thinking: + return self.model_output.gen_text_with_thinking(new_text=self.view) + return self.view diff --git a/packages/dbgpt-app/src/dbgpt_app/static/web/404.html b/packages/dbgpt-app/src/dbgpt_app/static/web/404.html index ed090d748..227c0ca2b 100644 --- a/packages/dbgpt-app/src/dbgpt_app/static/web/404.html +++ b/packages/dbgpt-app/src/dbgpt_app/static/web/404.html @@ -1 +1 @@ -
\ No newline at end of file +
\ No newline at end of file diff --git a/packages/dbgpt-app/src/dbgpt_app/static/web/404/index.html b/packages/dbgpt-app/src/dbgpt_app/static/web/404/index.html index ed090d748..227c0ca2b 100644 --- a/packages/dbgpt-app/src/dbgpt_app/static/web/404/index.html +++ b/packages/dbgpt-app/src/dbgpt_app/static/web/404/index.html @@ -1 +1 @@ -
\ No newline at end of file +
\ No newline at end of file diff --git a/packages/dbgpt-app/src/dbgpt_app/static/web/_next/data/-o_at1DkHM-yUWJ5R7Yyw/construct/prompt/add.json b/packages/dbgpt-app/src/dbgpt_app/static/web/_next/data/Dojpbz19K63B2OOVa8USw/construct/prompt/add.json similarity index 100% rename from packages/dbgpt-app/src/dbgpt_app/static/web/_next/data/-o_at1DkHM-yUWJ5R7Yyw/construct/prompt/add.json rename to packages/dbgpt-app/src/dbgpt_app/static/web/_next/data/Dojpbz19K63B2OOVa8USw/construct/prompt/add.json diff --git a/packages/dbgpt-app/src/dbgpt_app/static/web/_next/data/-o_at1DkHM-yUWJ5R7Yyw/construct/prompt/edit.json b/packages/dbgpt-app/src/dbgpt_app/static/web/_next/data/Dojpbz19K63B2OOVa8USw/construct/prompt/edit.json similarity index 100% rename from packages/dbgpt-app/src/dbgpt_app/static/web/_next/data/-o_at1DkHM-yUWJ5R7Yyw/construct/prompt/edit.json rename to packages/dbgpt-app/src/dbgpt_app/static/web/_next/data/Dojpbz19K63B2OOVa8USw/construct/prompt/edit.json diff --git 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Knowledge",path:"Path",model_manage:"Models",create_model:"Create Model",model_select_tips:"Please select a model",language_select_tips:"Please select a language",submit:"Submit",close:"Close",stop_model:"Stop Model",stop_model_success:"Stop model success",start_model:"Start Model",start_model_success:"Start model success",stop_and_delete_model:"Stop and Delete Model",stop_and_delete_model_success:"Stop and delete model success",confirm_start_model:"Are you sure you want to start the model?, model name:",confirm_stop_model:"Are you sure you want to stop the model?, model name:",confirm_stop_and_delete_model:"Are you sure you want to stop and delete the model?, model name:",worker_type_select_tips:"Please select a worker type",model_select_worker_type:"Select worker type",download_model_tip:"Please download model first.",start_model_failed:"Start model failed",provider_select_tips:"Please select a provider",model_select_provider:"Please select a provider",model_please_input_name:"Please input the name",model_select_or_input_model:"Select or input model name",model_deploy_name:"Model Name",Plugins:"Plugins",try_again:"Try again",no_data:"No data",Open_Sidebar:"Unfold",verify:"Verify",cancel:"Cancel",Edit_Success:"Edit Success",Add:"Add",Add_Success:"Add Success",Error_Message:"Something Error",Please_Input:"Please Input",Prompt_Info_Scene:"Scene",Prompt_Info_Sub_Scene:"Sub Scene",Prompt_Info_Name:"Name",Prompt_Info_Content:"Content",Public:"Public",Private:"Private",Lowest:"Lowest",Missed:"Missed",Lost:"Lost",Incorrect:"Incorrect",Verbose:"Verbose",Best:"Best",Rating:"Rating",Q_A_Category:"Q&A Category",Q_A_Rating:"Q&A Rating",feed_back_desc:"0: No results\n1: Results exist, but they are irrelevant, the question is not understood\n2: Results exist, the question is understood, but it indicates that the question cannot be answered\n3: Results exist, the question is understood, and an answer is given, but the answer is incorrect\n4: Results exist, the question is understood, the answer is correct, but it is verbose and lacks a summary\n5: Results exist, the question is understood, the answer is correct, the reasoning is correct, and a summary is provided, concise and to the point\n",input_count:"Total input",input_unit:"characters",Click_Select:"Click&Select",Quick_Start:"Quick Start",Select_Plugins:"Select Plugins",Search:"Search",Update_From_Github:"Upload From Github",Reset:"Reset",Upload:"Upload",Market_Plugins:"Market Plugin",My_Plugins:"My Plugins",Del_Knowledge_Tips:"Do you want delete the Space",Del_Document_Tips:"Do you want delete the Document",Tips:"Tips",Limit_Upload_File_Count_Tips:"Only one file can be uploaded at a time",To_Plugin_Market:"Go to the Plugin Market",Summary:"Summary",stacked_column_chart:"Stacked Column",column_chart:"Column",percent_stacked_column_chart:"Percent Stacked Column",grouped_column_chart:"Grouped Column",time_column:"Time Column",pie_chart:"Pie",line_chart:"Line",area_chart:"Area",stacked_area_chart:"Stacked Area",scatter_plot:"Scatter",bubble_chart:"Bubble",stacked_bar_chart:"Stacked Bar",bar_chart:"Bar",percent_stacked_bar_chart:"Percent Stacked Bar",grouped_bar_chart:"Grouped Bar",water_fall_chart:"Waterfall",table:"Table",multi_line_chart:"Multi Line",multi_measure_column_chart:"Multi Measure Column",multi_measure_line_chart:"Multi Measure Line",Advices:"Advices",Retry:"Retry",Load_more:"load more",new_chat:"New Chat",choice_agent_tip:"Please choose an agent",no_context_tip:"Please enter your question",Terminal:"Terminal",used_apps:"Used Apps",app_in_mind:"Don't have an app in mind? to",explore:"Explore",Discover_more:"Discove more",sdk_insert:"SDK Insert",my_apps:"My Apps",awel_flow:"AWEL Flow",save:"Save",add_node:"Add Node",no_node:"No Node",connect_warning:"Nodes cannot be connected",flow_modal_title:"Save Flow",flow_name:"Flow Name",flow_description:"Flow Description",flow_name_required:"Please enter the flow name",flow_description_required:"Please enter the flow description",save_flow_success:"Save flow success",delete_flow_confirm:"Are you sure you want to delete this flow?",related_nodes:"Related Nodes",add_resource:"Add Resource",team_modal:"Work Modal",App:"App",resource_name:"Resource Name",resource_type:"Resource Type",resource_value:"Value",resource_dynamic:"Dynamic",Please_input_the_work_modal:"Please select the work modal",available_resources:" Available Resources",edit_new_applications:"Edit new applications",collect:"Collect",collected:"Collected",create:"Create",Agents:"Agents",edit_application:"edit application",add_application:"add application",app_name:"App Name",input_app_name:"Please enter the application name",LLM_strategy:"LLM Strategy",LLM_strategy_value:"LLM Strategy Value",please_select_LLM_strategy:"Please select LLM strategy",please_select_LLM_strategy_value:"Please select LLM strategy value",resource:"Resource",operators:"Operators",Chinese:"Chinese",English:"English",docs:"Docs",apps:"All Apps",please_enter_the_keywords:"Please enter the keywords",input_tip:"Please select the model and enter the description to start quickly",create_app:"Create App",copy_url:"Click the Copy Share link",double_click_open:"Double click on Nail nail to open",construct:" Construct App",chat_online:"Chat",recommend_apps:"Recommend",all_apps:"All",latest_apps:"Latest",my_collected_apps:"Collected",collect_success:"Collect success",cancel_success:"Cancel success",published:"Published",unpublished:"Unpublished",start_chat:"Chat",native_app:"Native app",temperature:"Temperature",max_new_tokens:"Max output tokens",create_flow:"Create flow",update:"Update",native_type:"App type",refreshSuccess:"Refresh Success",Download:"Download",app_type_select:"Please select app type",please_select_param:"Please select parameters",please_select_model:"Please select model",please_input_temperature:"Please input the temperature value",please_input_max_new_tokens:"Please input the max new tokens",select_workflow:"Select workflow",please_select_workflow:"Please select workflow",recommended_questions:"Recommended questions",question:"Question",please_input_recommended_questions:"Please input recommendation question",is_effective:"Whether to enable",add_question:"Add question",update_success:"Update successful",update_failed:"Update failed",please_select_prompt:"Please select a prompt",details:"Details",choose:"Choose",please_choose:"Please choose",want_delete:"Are you sure delete it?",success:"Success",input_parameter:"Input parameter",output_structure:"Output structure",User_input:"User input",LLM_test:"LLM test",Output_verification:"Output verification",select_scene:"Please select a scene",select_type:"Please select a type",Please_complete_the_input_parameters:"Please complete the input parameters",Please_fill_in_the_user_input:"Please fill in the user input",help:"I can help you:",Refresh_status:"Refresh status",Recall_test:"Recall test",synchronization:"One-key synchronization",Synchronization_initiated:"Synchronization has been initiated, please wait",Edit_document:"Edit document",Document_name:"Document name",Correlation_problem:"Correlation problem",Add_problem:"Add problem",New_knowledge_base:"New knowledge base",yuque:"yuque document",Get_yuque_document:"Get the contents of the Sparrow document",document_url:"Document address",input_document_url:"Please enter the document address",Get_token:"Please obtain the team knowledge base token first",Reference_link:"Reference link",document_token:"Document token",input_document_token:"Please enter document token",input_question:"Please enter a question",detail:"Detail",Manual_entry:"Manual entry",Data_content:"Data content",Main_content:"Main content",Auxiliary_data:"Auxiliary data",enter_question_first:"Please enter the question first",Update_successfully:"Update successfully",Create_successfully:"Create successfully",Update_failure:"Update failure",Create_failure:"Create failure",View_details:"View details",All:"All",Please_input_prompt_name:"Please input prompt name",Copy_Btn:"Copy",Delete_Btn:"Delete",publish:"Publish",unpublish:"Unpublished",publish_desc:"Are you sure about releasing the application?",unPublish_desc:"Are you sure you want to unpublish this application?",published_success:"Published Success",dbgpts_community:"DBGPTS Community",community_dbgpts:"Community DBGPTS",my_dbgpts:"My DBGPTS",Refresh_dbgpts:"Refresh from the community Git repository",workflow:"Workflow",resources:"Resources",app:"App"}},zh:{translation:{dialog_list:"对话列表",delete_chat:"删除会话",delete_chat_confirm:"您确认要删除会话吗？",input_tips:"可以问我任何问题，shift + Enter 换行",sent:"发送",answer_again:"重新回答",feedback_tip:"描述一下具体问题或更优的答案",thinking:"正在思考中",stop_replying:"停止回复",erase_memory:"清除记忆",copy_success:"复制成功",copy_failed:"复制失败",copy_nothing:"内容复制为空",file_tip:"文件上传后无法更改",chat_online:"在线对话",assistant:"平台小助手",model_tip:"当前应用暂不支持模型选择",temperature_tip:"当前应用暂不支持温度配置",max_new_tokens_tip:"当前应用暂不支持max_new_tokens配置",extend_tip:"当前应用暂不支持拓展配置",Upload_Data_Successfully:"文件上传成功",Upload_Data_Failed:"文件上传失败",Upload_Data:"上传数据",Code_Editor:"代码编辑器",Open_Code_Editor:"打开代码编辑器",Export_Flow_Success:"导出工作流成功",Import_Flow_Success:"导入工作流成功",Import:"导入",Export:"导出",Import_Flow:"导入工作流",Export_Flow:"导出工作流",Select_File:"选择文件",Save_After_Import:"导入后保存",Export_File_Type:"文件类型",Export_File_Format:"文件格式",Yes:"是",No:"否",Please_Add_Nodes_First:"请先添加节点",Add_Global_Variable_of_Flow:"添加 Flow 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个向量",recall_score:"召回分数",Set_a_threshold_score:"设置相似向量检索的阈值分数",recall_type:"召回类型",model:"模型",A_model_used:"用于创建文本或其他数据的矢量表示的模型",Automatic:"自动切片",Process:"切片处理",Automatic_desc:"自动设置分割和预处理规则。",chunk_size:"块大小",The_size_of_the_data_chunks:"处理中使用的数据块的大小",chunk_overlap:"块重叠",The_amount_of_overlap:"相邻数据块之间的重叠量",scene:"场景",A_contextual_parameter:"用于定义使用提示的设置或环境的上下文参数",template:"模板",structure_or_format:"预定义的提示结构或格式，有助于确保人工智能系统生成与所需风格或语气一致的响应。",max_token:"最大令牌",max_iteration:"最大迭代",concurrency_limit:"并发限制",The_maximum_number_of_tokens:"提示中允许的最大标记或单词数",Theme:"主题",database_type:"数据库类型",edit_database:"编辑数据源",add_database:"添加数据源",update_success:"更新成功",update_failed:"更新失败",create_success:"创建成功",create_failed:"创建失败",please_select_database_type:"请选择数据库类型",select_database_type:"选择数据库类型",description:"描述",input_description:"请输入描述",Port:"端口",Username:"用户名",Password:"密码",Remark:"备注",Edit:"编辑",Database:"数据库",Data_Source:"数据中心",Close_Sidebar:"收起",Show_Sidebar:"展开",language:"语言",choose_model:"请选择一个模型",data_center_desc:"DB-GPT支持数据库交互和基于文档的对话，它还提供了一个用户友好的数据中心管理界面。",create_database:"创建数据库",create_knowledge:"创建知识库",create_flow:"创建工作流",path:"路径",model_manage:"模型管理",create_model:"创建模型",model_select_tips:"请选择一个模型",submit:"提交",stop_model:"停止模型",stop_model_success:"模型停止成功",start_model:"启动模型",start_model_success:"启动模型成功",stop_and_delete_model:"停止并删除模型",stop_and_delete_model_success:"停止并删除模型成功",confirm_start_model:"确定要启动模型? 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i.MANDATORY_NEWLINE:return"\n";case i.SINGLE_INDENT:return this.indentation.getSingleIndent();default:return e}}}let nX=e=>e===i.SPACE||e===i.SINGLE_INDENT,nK=e=>e===i.SPACE||e===i.SINGLE_INDENT||e===i.NEWLINE,nz="top-level";class nJ{indentTypes=[];constructor(e){this.indent=e}getSingleIndent(){return this.indent}getLevel(){return this.indentTypes.length}increaseTopLevel(){this.indentTypes.push(nz)}increaseBlockLevel(){this.indentTypes.push("block-level")}decreaseTopLevel(){this.indentTypes.length>0&&m(this.indentTypes)===nz&&this.indentTypes.pop()}decreaseBlockLevel(){for(;this.indentTypes.length>0;){let e=this.indentTypes.pop();if(e!==nz)break}}}class nq extends nj{length=0;trailingSpace=!1;constructor(e){super(new nJ("")),this.expressionWidth=e}add(...e){if(e.forEach(e=>this.addToLength(e)),this.length>this.expressionWidth)throw new nQ;super.add(...e)}addToLength(e){if("string"==typeof e)this.length+=e.length,this.trailingSpace=!1;else if(e===i.MANDATORY_NEWLINE||e===i.NEWLINE)throw 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t=this.formatInlineExpression(e.children);t?(this.layout.add(e.openParen),this.layout.add(...t.getLayoutItems()),this.layout.add(i.NO_SPACE,e.closeParen,i.SPACE)):(this.layout.add(e.openParen,i.NEWLINE),nC(this.cfg)?(this.layout.add(i.INDENT),this.layout=this.formatSubExpression(e.children)):(this.layout.indentation.increaseBlockLevel(),this.layout.add(i.INDENT),this.layout=this.formatSubExpression(e.children),this.layout.indentation.decreaseBlockLevel()),this.layout.add(i.NEWLINE,i.INDENT,e.closeParen,i.SPACE))}formatBetweenPredicate(e){this.layout.add(this.showKw(e.betweenKw),i.SPACE),this.layout=this.formatSubExpression(e.expr1),this.layout.add(i.NO_SPACE,i.SPACE,this.showNonTabularKw(e.andKw),i.SPACE),this.layout=this.formatSubExpression(e.expr2),this.layout.add(i.SPACE)}formatCaseExpression(e){this.formatNode(e.caseKw),this.layout.indentation.increaseBlockLevel(),this.layout=this.formatSubExpression(e.expr),this.layout=this.formatSubExpression(e.clauses),this.layout.indentation.decreaseBlockLevel(),this.layout.add(i.NEWLINE,i.INDENT),this.formatNode(e.endKw)}formatCaseWhen(e){this.layout.add(i.NEWLINE,i.INDENT),this.formatNode(e.whenKw),this.layout=this.formatSubExpression(e.condition),this.formatNode(e.thenKw),this.layout=this.formatSubExpression(e.result)}formatCaseElse(e){this.layout.add(i.NEWLINE,i.INDENT),this.formatNode(e.elseKw),this.layout=this.formatSubExpression(e.result)}formatClause(e){this.isOnelineClause(e)?this.formatClauseInOnelineStyle(e):nC(this.cfg)?this.formatClauseInTabularStyle(e):this.formatClauseInIndentedStyle(e)}isOnelineClause(e){return this.dialectCfg.onelineClauses[e.nameKw.text]}formatClauseInIndentedStyle(e){this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e.nameKw),i.NEWLINE),this.layout.indentation.increaseTopLevel(),this.layout.add(i.INDENT),this.layout=this.formatSubExpression(e.children),this.layout.indentation.decreaseTopLevel()}formatClauseInOnelineStyle(e){this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e.nameKw),i.SPACE),this.layout=this.formatSubExpression(e.children)}formatClauseInTabularStyle(e){this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e.nameKw),i.SPACE),this.layout.indentation.increaseTopLevel(),this.layout=this.formatSubExpression(e.children),this.layout.indentation.decreaseTopLevel()}formatSetOperation(e){this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e.nameKw),i.NEWLINE),this.layout.add(i.INDENT),this.layout=this.formatSubExpression(e.children)}formatLimitClause(e){this.withComments(e.limitKw,()=>{this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e.limitKw))}),this.layout.indentation.increaseTopLevel(),nC(this.cfg)?this.layout.add(i.SPACE):this.layout.add(i.NEWLINE,i.INDENT),e.offset&&(this.layout=this.formatSubExpression(e.offset),this.layout.add(i.NO_SPACE,",",i.SPACE)),this.layout=this.formatSubExpression(e.count),this.layout.indentation.decreaseTopLevel()}formatAllColumnsAsterisk(e){this.layout.add("*",i.SPACE)}formatLiteral(e){this.layout.add(e.text,i.SPACE)}formatIdentifier(e){this.layout.add(e.text,i.SPACE)}formatParameter(e){this.layout.add(this.params.get(e),i.SPACE)}formatOperator({text:e}){this.cfg.denseOperators||this.dialectCfg.alwaysDenseOperators.includes(e)?this.layout.add(i.NO_SPACE,e):":"===e?this.layout.add(i.NO_SPACE,e,i.SPACE):this.layout.add(e,i.SPACE)}formatComma(e){this.inline?this.layout.add(i.NO_SPACE,",",i.SPACE):this.layout.add(i.NO_SPACE,",",i.NEWLINE,i.INDENT)}withComments(e,t){this.formatComments(e.leadingComments),t(),this.formatComments(e.trailingComments)}formatComments(e){e&&e.forEach(e=>{e.type===o.line_comment?this.formatLineComment(e):this.formatBlockComment(e)})}formatLineComment(e){P(e.precedingWhitespace||"")?this.layout.add(i.NEWLINE,i.INDENT,e.text,i.MANDATORY_NEWLINE,i.INDENT):this.layout.getLayoutItems().length>0?this.layout.add(i.NO_NEWLINE,i.SPACE,e.text,i.MANDATORY_NEWLINE,i.INDENT):this.layout.add(e.text,i.MANDATORY_NEWLINE,i.INDENT)}formatBlockComment(e){this.isMultilineBlockComment(e)?(this.splitBlockComment(e.text).forEach(e=>{this.layout.add(i.NEWLINE,i.INDENT,e)}),this.layout.add(i.NEWLINE,i.INDENT)):this.layout.add(e.text,i.SPACE)}isMultilineBlockComment(e){return P(e.text)||P(e.precedingWhitespace||"")}isDocComment(e){let t=e.split(/\n/);return/^\/\*\*?$/.test(t[0])&&t.slice(1,t.length-1).every(e=>/^\s*\*/.test(e))&&/^\s*\*\/$/.test(m(t))}splitBlockComment(e){return this.isDocComment(e)?e.split(/\n/).map(e=>/^\s*\*/.test(e)?" "+e.replace(/^\s*/,""):e):e.split(/\n/).map(e=>e.replace(/^\s*/,""))}formatSubExpression(e){return new n0({cfg:this.cfg,dialectCfg:this.dialectCfg,params:this.params,layout:this.layout,inline:this.inline}).format(e)}formatInlineExpression(e){let t=this.params.getPositionalParameterIndex();try{return new n0({cfg:this.cfg,dialectCfg:this.dialectCfg,params:this.params,layout:new nq(this.cfg.expressionWidth),inline:!0}).format(e)}catch(e){if(e instanceof nQ){this.params.setPositionalParameterIndex(t);return}throw e}}formatKeywordNode(e){switch(e.tokenType){case r.RESERVED_JOIN:return this.formatJoin(e);case r.AND:case r.OR:case r.XOR:return this.formatLogicalOperator(e);default:return this.formatKeyword(e)}}formatJoin(e){nC(this.cfg)?(this.layout.indentation.decreaseTopLevel(),this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e),i.SPACE),this.layout.indentation.increaseTopLevel()):this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e),i.SPACE)}formatKeyword(e){this.layout.add(this.showKw(e),i.SPACE)}formatLogicalOperator(e){"before"===this.cfg.logicalOperatorNewline?nC(this.cfg)?(this.layout.indentation.decreaseTopLevel(),this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e),i.SPACE),this.layout.indentation.increaseTopLevel()):this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e),i.SPACE):this.layout.add(this.showKw(e),i.NEWLINE,i.INDENT)}showKw(e){var t;return f(t=e.tokenType)||t===r.RESERVED_CLAUSE||t===r.RESERVED_SELECT||t===r.RESERVED_SET_OPERATION||t===r.RESERVED_JOIN||t===r.LIMIT?function(e,t){if("standard"===t)return e;let n=[];return e.length>=10&&e.includes(" ")&&([e,...n]=e.split(" ")),(e="tabularLeft"===t?e.padEnd(9," "):e.padStart(9," "))+["",...n].join(" ")}(this.showNonTabularKw(e),this.cfg.indentStyle):this.showNonTabularKw(e)}showNonTabularKw(e){switch(this.cfg.keywordCase){case"preserve":return g(e.raw);case"upper":return e.text;case"lower":return e.text.toLowerCase()}}}class n1{constructor(e,t){this.dialect=e,this.cfg=t,this.params=new nL(this.cfg.params)}format(e){let t=this.parse(e),n=this.formatAst(t),r=this.postFormat(n);return r.trimEnd()}parse(e){return(function(e){let t={},n=new nG(n=>[...e.tokenize(n,t).map(nv).map(nP).map(ny).map(nD),c(n.length)]),r=new n$(nW.fromCompiled(nV),{lexer:n});return{parse:(e,n)=>{t=n;let{results:o}=r.feed(e);if(1===o.length)return o[0];if(0===o.length)throw Error("Parse error: Invalid SQL");throw Error(`Parse error: Ambiguous grammar ${JSON.stringify(o,void 0,2)}`)}}})(this.dialect.tokenizer).parse(e,this.cfg.paramTypes||{})}formatAst(e){return e.map(e=>this.formatStatement(e)).join("\n".repeat(this.cfg.linesBetweenQueries+1))}formatStatement(e){let t=new n0({cfg:this.cfg,dialectCfg:this.dialect.formatOptions,params:this.params,layout:new nj(new nJ(nm(this.cfg)))}).format(e.children);return e.hasSemicolon&&(this.cfg.newlineBeforeSemicolon?t.add(i.NEWLINE,";"):t.add(i.NO_NEWLINE,";")),t.toString()}postFormat(e){if(this.cfg.tabulateAlias&&(e=function(e){let t=e.split("\n"),n=[];for(let e=0;e({line:e,matches:e.match(/(^.*?\S) (AS )?(\S+,?$)/i)})).map(({line:e,matches:t})=>t?{precedingText:t[1],as:t[2],alias:t[3]}:{precedingText:e}),i=L(o.map(({precedingText:e})=>e.replace(/\s*,\s*$/,"")));n=[...n,...r=o.map(({precedingText:e,as:t,alias:n})=>e+(n?" 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\ No newline at end of file diff --git a/packages/dbgpt-core/pyproject.toml b/packages/dbgpt-core/pyproject.toml index 21a568555..0b3cb84cd 100644 --- a/packages/dbgpt-core/pyproject.toml +++ b/packages/dbgpt-core/pyproject.toml @@ -116,7 +116,8 @@ llama_cpp = [ "llama-cpp-python" ] llama_cpp_server = [ - "llama-cpp-server-py" + "llama-cpp-server-py-core>=0.1.3", + "llama-cpp-server-py>=0.1.3" ] proxy_ollama = ["ollama"] proxy_zhipuai = ["zhipuai>=2.1.5"] diff --git a/packages/dbgpt-core/src/dbgpt/agent/util/llm/llm_client.py b/packages/dbgpt-core/src/dbgpt/agent/util/llm/llm_client.py index b21e52e61..73f70214d 100644 --- a/packages/dbgpt-core/src/dbgpt/agent/util/llm/llm_client.py +++ b/packages/dbgpt-core/src/dbgpt/agent/util/llm/llm_client.py @@ -5,7 +5,7 @@ import logging import traceback from typing import Any, Callable, Dict, Optional, Union -from dbgpt.core import LLMClient, ModelRequestContext +from dbgpt.core import LLMClient, ModelOutput, ModelRequestContext from dbgpt.core.interface.output_parser import BaseOutputParser from dbgpt.util.error_types import LLMChatError from dbgpt.util.tracer import root_tracer @@ -198,8 +198,7 @@ class AIWrapper: try: model_request = _build_model_request(payload) str_prompt = model_request.messages_to_string() - model_output = None - sep = "########S#E#P#########" + model_output: Optional[ModelOutput] = None async for output in self._llm_client.generate_stream(model_request.copy()): # type: ignore # noqa model_output = output if memory and stream_out: @@ -209,20 +208,15 @@ class AIWrapper: "sender": sender, "receiver": "?", "model": llm_model, - "markdown": self._output_parser.parse_model_nostream_resp( - model_output, sep - ), + "markdown": model_output.gen_text_with_thinking(), } - await memory.push_message( conv_id, temp_message, ) if not model_output: raise ValueError("LLM generate stream is null!") - parsed_output = self._output_parser.parse_model_nostream_resp( - model_output, sep - ) + parsed_output = model_output.gen_text_with_thinking() parsed_output = parsed_output.strip().replace("\\n", "\n") if verbose: diff --git a/packages/dbgpt-core/src/dbgpt/core/awel/flow/base.py b/packages/dbgpt-core/src/dbgpt/core/awel/flow/base.py index 616dcc27f..b69475726 100644 --- a/packages/dbgpt-core/src/dbgpt/core/awel/flow/base.py +++ b/packages/dbgpt-core/src/dbgpt/core/awel/flow/base.py @@ -346,7 +346,7 @@ class Parameter(BaseDynamic, TypeMetadata, Serializable): ..., description="The label to display in UI", examples=["OpenAI API Key"] ) name: str = Field( - ..., description="The name of the parameter", examples=["apk_key"] + ..., description="The name of the parameter", examples=["api_key"] ) is_list: bool = Field( default=False, diff --git a/packages/dbgpt-core/src/dbgpt/core/interface/llm.py b/packages/dbgpt-core/src/dbgpt/core/interface/llm.py index 3077ccae7..f3112ce8b 100644 --- a/packages/dbgpt-core/src/dbgpt/core/interface/llm.py +++ b/packages/dbgpt-core/src/dbgpt/core/interface/llm.py @@ -6,7 +6,18 @@ import logging import time from abc import ABC, abstractmethod from dataclasses import asdict, dataclass, field -from typing import Any, AsyncIterator, Coroutine, Dict, List, Optional, Tuple, Union +from enum import Enum +from typing import ( + Any, + AsyncIterator, + Coroutine, + Dict, + List, + Literal, + Optional, + Tuple, + Union, +) from cachetools import TTLCache @@ -14,6 +25,7 @@ from dbgpt._private.pydantic import BaseModel, model_to_dict from dbgpt.core.interface.message import ModelMessage, ModelMessageRoleType from dbgpt.util import BaseParameters from dbgpt.util.annotations import PublicAPI +from dbgpt.util.i18n_utils import _ from dbgpt.util.model_utils import GPUInfo logger = logging.getLogger(__name__) @@ -115,6 +127,141 @@ class ModelInferenceMetrics: return asdict(self) +MEDIA_DATA_TYPE = Union[str, bytes] + + +@dataclass +class MediaObject: + """Media object for the model output or model request.""" + + data: MEDIA_DATA_TYPE = field(metadata={"help": _("The media data")}) + format: str = field(default="text", metadata={"help": _("The format of the media")}) + + +class MediaContentType(str, Enum): + """The media content type.""" + + TEXT = "text" + THINKING = "thinking" + IMAGE = "image" + AUDIO = "audio" + VIDEO = "video" + + +@dataclass +class MediaContent: + """Media content for the model output or model request. + + Examples: + .. code-block:: python + + simple_text = MediaContent( + type="text", + object=MediaObject( + data="Hello, world!", + format="text", + ) + ) + thinking_text = MediaContent( + type="thinking", + object=MediaObject( + data="Thinking...", + format="text", + ) + ) + + url_image1 = MediaContent( + type="image", + object=MediaObject( + data="https://example.com/image.jpg", + format="url", + ) + ) + # Url with image type: 'image/jpeg' + url_image2 = MediaContent( + type="image", + object=MediaObject( + data="https://example.com/image.jpg", + format="url@image/jpeg", + ) + ) + + # With image type: 'image/jpeg' + base64_image1 = MediaContent( + type="image", + object=MediaObject( + data="base64_string", + format="base64@image/jpeg", + ) + ) + # No image type + base64_image2 = MediaContent( + type="image", + object=MediaObject( + data="base64_string", + format="base64", + ) + ) + + # Video + url_video1 = MediaContent( + type="video", + object=MediaObject( + data="https://example.com/video.mp4", + format="url", + ) + ) + url_video2 = MediaContent( + type="video", + object=MediaObject( + data="https://example.com/video.mp4", + format="url@video/mp4", + ) + ) + binary_video = MediaContent( + type="video", + object=MediaObject( + data=b"binary_data", + format="binary@video/mp4", + ) + ) + binary_audio = MediaContent( + type="audio", + object=MediaObject( + data=b"binary_data", + format="binary@audio/mpeg", + ) + ) + """ + + object: MediaObject = field(metadata={"help": _("The media object")}) + type: Literal["text", "thinking", "image", "audio", "video"] = field( + default="text", metadata={"help": _("The type of the model media content")} + ) + + @classmethod + def build_text(cls, text: str) -> "MediaContent": + """Create a MediaContent object from text.""" + return cls(type="text", object=MediaObject(data=text, format="text")) + + @classmethod + def build_thinking(cls, text: str) -> "MediaContent": + """Create a MediaContent object from thinking.""" + return cls(type="thinking", object=MediaObject(data=text, format="text")) + + def get_text(self) -> str: + """Get the text.""" + if self.type == MediaContentType.TEXT: + return self.object.data + raise ValueError("The content type is not text") + + def get_thinking(self) -> str: + """Get the thinking.""" + if self.type == MediaContentType.THINKING: + return self.object.data + raise ValueError("The content type is not thinking") + + @dataclass @PublicAPI(stability="beta") class ModelRequestContext: @@ -154,9 +301,9 @@ class ModelRequestContext: @dataclass @PublicAPI(stability="beta") class ModelOutput: - """A class to represent the output of a LLM.""" "" + """A class to represent the output of a LLM.""" - text: str + content: Union[MediaContent, List[MediaContent]] """The generated text.""" error_code: int """The error code of the model inference. If the model inference is successful, @@ -168,6 +315,32 @@ class ModelOutput: metrics: Optional[ModelInferenceMetrics] = None """Some metrics for model inference""" + def __init__( + self, + error_code: int, + text: Optional[str] = None, + content: Optional[MediaContent] = None, + **kwargs, + ): + if text is not None and content is not None: + raise ValueError("Cannot pass both text and content") + elif text is not None: + self.content = MediaContent.build_text(text) + elif content is not None: + self.content = content + else: + raise ValueError("Must pass either text or content") + self.error_code = error_code + for k, v in kwargs.items(): + if k in [ + "incremental", + "model_context", + "finish_reason", + "usage", + "metrics", + ]: + setattr(self, k, v) + def to_dict(self) -> Dict: """Convert the model output to dict.""" return asdict(self) @@ -177,6 +350,124 @@ class ModelOutput: """Check if the model inference is successful.""" return self.error_code == 0 + @property + def has_text(self) -> bool: + """Check if the model output has text content.""" + if isinstance(self.content, MediaContent): + return self.content.type == MediaContentType.TEXT + elif isinstance(self.content, list): + return any(c.type == MediaContentType.TEXT for c in self.content) + return False + + @property + def text(self) -> str: + """The generated text.""" + if isinstance(self.content, MediaContent): + return self.content.get_text() + elif isinstance(self.content, list) and all( + isinstance(c, MediaContent) for c in self.content + ): + text_content = [c for c in self.content if c.type == MediaContentType.TEXT] + if not text_content: + raise ValueError("There is no text content") + # Return the last text content + return text_content[-1].get_text() + raise ValueError("The content is not text") + + @property + def has_thinking(self) -> bool: + """Check if the model output has thinking content.""" + if isinstance(self.content, MediaContent): + return self.content.type == MediaContentType.THINKING + elif isinstance(self.content, list) and self.content: + return any(c.type == MediaContentType.THINKING for c in self.content) + else: + return False + + @property + def thinking_text(self) -> Optional[str]: + """The reasoning content.""" + if not self.content: + return None + if isinstance(self.content, MediaContent): + if self.content.type == MediaContentType.THINKING: + return self.content.get_thinking() + return None + elif isinstance(self.content, list) and all( + isinstance(c, MediaContent) for c in self.content + ): + # In most cases, just one thinking content + thinking_content = [ + c for c in self.content if c.type == MediaContentType.THINKING + ] + if not thinking_content: + return None + # Return the last thinking content + return thinking_content[-1].get_thinking() + return None + + def gen_text_with_thinking(self, new_text: Optional[str] = None) -> str: + from dbgpt.vis.tags.vis_thinking import VisThinking + + msg = "" + if self.has_thinking: + msg = self.thinking_text or "" + msg = VisThinking().sync_display(content=msg) + msg += "\n" + if new_text: + msg += new_text + elif self.has_text: + msg += self.text or "" + return msg + + @text.setter + def text(self, value: str): + """Set the generated text.""" + if not isinstance(value, str): + raise ValueError("text must be a string") + # Build a new MediaContent object and assign it to content + self.content = MediaContent( + type="text", + object=MediaObject(data=value, format="text"), + ) + + @classmethod + def build_thinking(cls, thinking: str, error_code: int = 0) -> "ModelOutput": + """Create a ModelOutput object from thinking.""" + return cls( + error_code=error_code, + content=MediaContent.build_thinking(thinking), + ) + + @classmethod + def build( + cls, + text: Optional[str] = None, + thinking: Optional[str] = None, + error_code: int = 0, + usage: Optional[Dict[str, Any]] = None, + finish_reason: Optional[str] = None, + ) -> "ModelOutput": + if thinking and text: + # Has thinking and text + content = [ + # First thinking + MediaContent.build_thinking(thinking), + MediaContent.build_text(text), + ] + elif text: + # Only text + content = MediaContent.build_text(text) + else: + # Build a empty thinking content + content = MediaContent.build_thinking(thinking) + return cls( + error_code=error_code, + content=content, + usage=usage, + finish_reason=finish_reason, + ) + _ModelMessageType = Union[List[ModelMessage], List[Dict[str, Any]]] diff --git a/packages/dbgpt-core/src/dbgpt/core/interface/output_parser.py b/packages/dbgpt-core/src/dbgpt/core/interface/output_parser.py index 3a4be1289..b1f142e9c 100644 --- a/packages/dbgpt-core/src/dbgpt/core/interface/output_parser.py +++ b/packages/dbgpt-core/src/dbgpt/core/interface/output_parser.py @@ -74,7 +74,7 @@ class BaseOutputParser(MapOperator[ModelOutput, Any], ABC): """ self.data_schema = data_schema - def __post_process_code(self, code): + def _post_process_code(self, code): sep = "\n```" if sep in code: blocks = code.split(sep) @@ -84,67 +84,61 @@ class BaseOutputParser(MapOperator[ModelOutput, Any], ABC): code = sep.join(blocks) return code - def parse_model_stream_resp_ex(self, chunk: ResponseTye, skip_echo_len): + def parse_model_stream_resp_ex( + self, data: ModelOutput, text_output: bool = True, skip_echo_len: int = 0 + ): """Parse the output of an LLM call. Args: - chunk (ResponseTye): The output of an LLM call. + chunk (ModelOutput): The output of an LLM call. skip_echo_len (int): The length of the prompt to skip. """ - data = _parse_model_response(chunk) + # data = _parse_model_response(chunk) # TODO: Multi mode output handler, rewrite this for multi model, use adapter # mode. - model_context = data.get("model_context") + model_context = data.model_context has_echo = False if model_context and "prompt_echo_len_char" in model_context: prompt_echo_len_char = int(model_context.get("prompt_echo_len_char", -1)) has_echo = bool(model_context.get("echo", False)) if prompt_echo_len_char != -1: skip_echo_len = prompt_echo_len_char + if not text_output: + # TODO, process code: self._post_process_code(output) + return data - if data.get("error_code", 0) == 0: + if data.success: if has_echo: - # TODO Judging from model_context - output = data["text"][skip_echo_len:].strip() + output = data.text[skip_echo_len:].strip() else: output = data["text"].strip() - - output = self.__post_process_code(output) + output = self._post_process_code(output) return output else: output = data["text"] + f" (error_code: {data['error_code']})" return output - def parse_model_nostream_resp(self, response: ResponseTye, sep: str): + def parse_model_nostream_resp( + self, response: ModelOutput, text_output: bool = True + ) -> Union[str, ModelOutput]: """Parse the output of an LLM call.""" - resp_obj_ex = _parse_model_response(response) - if isinstance(resp_obj_ex, str): - resp_obj_ex = json.loads(resp_obj_ex) - if resp_obj_ex["error_code"] == 0: - all_text = resp_obj_ex["text"] - # Parse the returned text to get the AI reply part - tmp_resp = all_text.split(sep) - last_index = -1 - for i in range(len(tmp_resp)): - if tmp_resp[i].find("assistant:") != -1: - last_index = i - ai_response = tmp_resp[last_index] - ai_response = ai_response.replace("assistant:", "") - ai_response = ai_response.replace("Assistant:", "") - ai_response = ai_response.replace("ASSISTANT:", "") - ai_response = ai_response.replace("\\_", "_") - ai_response = ai_response.replace("\\*", "*") - ai_response = ai_response.replace("\t", "") - - # ai_response = ai_response.strip().replace("\\n", " ").replace("\n", " ") - # print("un_stream ai response:", ai_response) - return ai_response - else: + if response is None: + raise ValueError("Internal error! The response is None.") + if not isinstance(response, ModelOutput): raise ValueError( - f"Model server error!code={resp_obj_ex['error_code']}, error msg is " - f"{resp_obj_ex['text']}" + "Internal error! The response type is not ModelOutput, but " + f"{type(response)}" ) + if not response.success: + raise ValueError( + f"Model server error!code={response.error_code}, error msg is " + f"{response.text}" + ) + if text_output: + return response.text + else: + return response def _illegal_json_ends(self, s): temp_json = s @@ -279,7 +273,7 @@ class BaseOutputParser(MapOperator[ModelOutput, Any], ABC): if self.current_dag_context.streaming_call: return self.parse_model_stream_resp_ex(input_value, 0) else: - return self.parse_model_nostream_resp(input_value, "#####################") + return self.parse_model_nostream_resp(input_value) def _parse_model_response(response: ResponseTye): @@ -330,9 +324,9 @@ class SQLOutputParser(BaseOutputParser): """Create a new SQL output parser.""" super().__init__(is_stream_out=is_stream_out, **kwargs) - def parse_model_nostream_resp(self, response: ResponseTye, sep: str): + def parse_model_nostream_resp(self, response: ResponseTye): """Parse the output of an LLM call.""" - model_out_text = super().parse_model_nostream_resp(response, sep) + model_out_text = super().parse_model_nostream_resp(response) clean_str = super().parse_prompt_response(model_out_text) return json.loads(clean_str, strict=True) @@ -372,11 +366,11 @@ class SQLListOutputParser(BaseOutputParser): """Create a new SQL list output parser.""" super().__init__(is_stream_out=is_stream_out, **kwargs) - def parse_model_nostream_resp(self, response: ResponseTye, sep: str): + def parse_model_nostream_resp(self, response: ResponseTye): """Parse the output of an LLM call.""" from dbgpt.util.json_utils import find_json_objects - model_out_text = super().parse_model_nostream_resp(response, sep) + model_out_text = super().parse_model_nostream_resp(response) json_objects = find_json_objects(model_out_text) json_count = len(json_objects) if json_count < 1: diff --git a/packages/dbgpt-core/src/dbgpt/experimental/intent/base.py b/packages/dbgpt-core/src/dbgpt/experimental/intent/base.py index 3256c6761..d86d3abd2 100644 --- a/packages/dbgpt-core/src/dbgpt/experimental/intent/base.py +++ b/packages/dbgpt-core/src/dbgpt/experimental/intent/base.py @@ -178,9 +178,7 @@ class BaseIntentDetection(ABC): model_request = ModelRequest.build_request(model, messages=[model_messages]) model_output = await self.llm_client.generate(model_request) output_parser = BaseOutputParser() - str_out = output_parser.parse_model_nostream_resp( - model_output, "#########################" - ) + str_out = output_parser.parse_model_nostream_resp(model_output) json_out = output_parser.parse_prompt_response(str_out) dict_out = json.loads(json_out) return response_schema.model_validate(dict_out) diff --git a/packages/dbgpt-core/src/dbgpt/model/adapter/proxy_adapter.py b/packages/dbgpt-core/src/dbgpt/model/adapter/proxy_adapter.py index b70cdbc4b..c1dba1372 100644 --- a/packages/dbgpt-core/src/dbgpt/model/adapter/proxy_adapter.py +++ b/packages/dbgpt-core/src/dbgpt/model/adapter/proxy_adapter.py @@ -66,7 +66,7 @@ class ProxyLLMModelAdapter(LLMModelAdapter): if not dynamic_llm_client_class: dynamic_llm_client_class = self.get_llm_client_class(params) logger.info( - f"Load model from params: {params}, llm client class: " + f"Load model from params: {params}llm client class: " f"{dynamic_llm_client_class}" ) proxy_llm_client = dynamic_llm_client_class.new_client(params) diff --git a/packages/dbgpt-core/src/dbgpt/model/cluster/worker/default_worker.py b/packages/dbgpt-core/src/dbgpt/model/cluster/worker/default_worker.py index 0b6ef3476..e9e2bde4e 100644 --- a/packages/dbgpt-core/src/dbgpt/model/cluster/worker/default_worker.py +++ b/packages/dbgpt-core/src/dbgpt/model/cluster/worker/default_worker.py @@ -445,31 +445,40 @@ class DefaultModelWorker(ModelWorker): ): finish_reason = None usage = None - error_code = 0 if isinstance(output, dict): finish_reason = output.get("finish_reason") usage = output.get("usage") - output = output["text"] if finish_reason is not None: logger.info(f"finish_reason: {finish_reason}") + error_code = output.get("error_code", 0) + model_output = ModelOutput.build( + output["text"], + thinking=None, + error_code=error_code, + usage=usage, + finish_reason=finish_reason, + ) elif isinstance(output, ModelOutput): finish_reason = output.finish_reason usage = output.usage - error_code = output.error_code - output = output.text - incremental_output = output[len(previous_response) :] + model_output = output + elif isinstance(output, str): + # Output is string + model_output = ModelOutput.build(output) + else: + raise ValueError(f"Invalid output type: {type(output)}") + current_output = "" + if model_output.has_thinking: + current_output = model_output.thinking_text or "" + if model_output.has_text: + current_output += model_output.text + incremental_output = current_output[len(previous_response) :] print(incremental_output, end="", flush=True) metrics = _new_metrics_from_model_output(last_metrics, is_first_generate, usage) - model_output = ModelOutput( - text=output, - error_code=error_code, - model_context=model_context, - finish_reason=finish_reason, - usage=usage, - metrics=metrics, - ) - return model_output, incremental_output, output, metrics + model_output.metrics = metrics + model_output.model_context = model_context + return model_output, incremental_output, current_output, metrics def _handle_exception(self, e): # Check if the exception is a torch.cuda.CudaError and if torch was imported. diff --git a/packages/dbgpt-core/src/dbgpt/model/llm/llama_cpp/llama_cpp.py b/packages/dbgpt-core/src/dbgpt/model/llm/llama_cpp/llama_cpp.py index 3eaf6e3bd..1050556b9 100644 --- a/packages/dbgpt-core/src/dbgpt/model/llm/llama_cpp/llama_cpp.py +++ b/packages/dbgpt-core/src/dbgpt/model/llm/llama_cpp/llama_cpp.py @@ -4,7 +4,7 @@ Fork from text-generation-webui https://github.com/oobabooga/text-generation-web import logging import re -from typing import Dict +from typing import Dict, Optional import llama_cpp import torch @@ -13,6 +13,8 @@ from dbgpt.core import ModelOutput from dbgpt.model.adapter.llama_cpp_py_adapter import LlamaCppModelParameters from dbgpt.model.utils.llm_utils import parse_model_request +from ...utils.parse_utils import ParsedChatMessage, parse_chat_message + logger = logging.getLogger(__name__) if torch.cuda.is_available() and not torch.version.hip: @@ -124,10 +126,30 @@ class LlamaCppModel: ) text = "" + usage = None + msg = ParsedChatMessage() + finish_reason: Optional[str] = None for r in completion_chunks: if not r.get("choices"): continue - if r["choices"][0]["delta"].get("content") is not None: - content = r["choices"][0]["delta"]["content"] + delta = r["choices"][0]["delta"] + if delta.get("content") is not None: + content = delta["content"] text += content - yield ModelOutput(text=text, error_code=0) + msg, _ = parse_chat_message( + content, + extract_reasoning=True, + is_streaming=True, + streaming_state=msg, + ) + finish_reason = delta.get("finish_reason") + if text: + if hasattr(r, "usage") and r.usage is not None: + usage = r.usage.dict() + yield ModelOutput.build( + msg.content, + msg.reasoning_content, + error_code=0, + usage=usage, + finish_reason=finish_reason, + ) diff --git a/packages/dbgpt-core/src/dbgpt/model/llm/llama_cpp/llama_cpp_server.py b/packages/dbgpt-core/src/dbgpt/model/llm/llama_cpp/llama_cpp_server.py index 249149316..10e59d009 100644 --- a/packages/dbgpt-core/src/dbgpt/model/llm/llama_cpp/llama_cpp_server.py +++ b/packages/dbgpt-core/src/dbgpt/model/llm/llama_cpp/llama_cpp_server.py @@ -17,6 +17,8 @@ from typing import Dict, Optional from dbgpt.core import ModelOutput +from ...utils.parse_utils import ParsedChatMessage, parse_chat_message + logger = logging.getLogger(__name__) try: @@ -76,6 +78,7 @@ def chat_generate_stream( ): req = _build_chat_completion_request(params, stream=True) text = "" + msg = ParsedChatMessage() for r in model.stream_chat_completion(req): if len(r.choices) == 0: continue @@ -86,16 +89,17 @@ def chat_generate_stream( finish_reason = _parse_finish_reason(r.choices[0].finish_reason) if content is not None: - content = r.choices[0].delta.content text += content - yield ModelOutput( - text=text, error_code=0, finish_reason=finish_reason, usage=r.usage - ) - elif text and content is None: - # Last response is empty, return the text - yield ModelOutput( - text=text, error_code=0, finish_reason=finish_reason, usage=r.usage + msg, _ = parse_chat_message( + content, extract_reasoning=True, is_streaming=True, streaming_state=msg ) + yield ModelOutput.build( + msg.content, + msg.reasoning_content, + error_code=0, + finish_reason=finish_reason, + usage=r.usage, + ) def _build_chat_completion_request( @@ -160,13 +164,14 @@ def generate( if not resp.choices or not resp.choices[0].message: raise ValueError("Response can't be empty") content = resp.choices[0].message.content - return ModelOutput( - text=content, + msg = parse_chat_message(content, extract_reasoning=True) + return ModelOutput.build( + msg.content, + msg.reasoning_content, error_code=0, finish_reason=_parse_finish_reason(resp.choices[0].finish_reason), usage=resp.usage, ) - else: req = _build_completion_request(params, stream=False) resp = model.completion(req) diff --git a/packages/dbgpt-core/src/dbgpt/model/llm/llm_out/hf_chat_llm.py b/packages/dbgpt-core/src/dbgpt/model/llm/llm_out/hf_chat_llm.py index 39a4e5ac9..000094092 100644 --- a/packages/dbgpt-core/src/dbgpt/model/llm/llm_out/hf_chat_llm.py +++ b/packages/dbgpt-core/src/dbgpt/model/llm/llm_out/hf_chat_llm.py @@ -4,6 +4,10 @@ from threading import Thread import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer +from dbgpt.core import ModelOutput + +from ...utils.parse_utils import ParsedChatMessage, parse_chat_message + logger = logging.getLogger(__name__) @@ -58,11 +62,21 @@ def huggingface_chat_generate_stream( generate_kwargs = {"input_ids": input_ids, **base_kwargs} thread = Thread(target=model.generate, kwargs=generate_kwargs) thread.start() - out = "" + text = "" + usage = None + msg = ParsedChatMessage() for new_text in streamer: - out += new_text + text += new_text + msg, _ = parse_chat_message( + new_text, extract_reasoning=True, is_streaming=True, streaming_state=msg + ) if custom_stop_words: for stop_word in custom_stop_words: - if out.endswith(stop_word): - out = out[: -len(stop_word)] - yield out + if text.endswith(stop_word): + text = text[: -len(stop_word)] + yield ModelOutput.build( + msg.content, + msg.reasoning_content, + error_code=0, + usage=usage, + ) diff --git a/packages/dbgpt-core/src/dbgpt/model/llm/llm_out/vllm_llm.py b/packages/dbgpt-core/src/dbgpt/model/llm/llm_out/vllm_llm.py index 926281e26..e183d85da 100644 --- a/packages/dbgpt-core/src/dbgpt/model/llm/llm_out/vllm_llm.py +++ b/packages/dbgpt-core/src/dbgpt/model/llm/llm_out/vllm_llm.py @@ -5,6 +5,10 @@ from vllm import AsyncLLMEngine from vllm.sampling_params import SamplingParams from vllm.utils import random_uuid +from dbgpt.core import ModelOutput + +from ...utils.parse_utils import _DEFAULT_THINK_START_TOKEN, parse_chat_message + _IS_BENCHMARK = os.getenv("DB_GPT_MODEL_BENCHMARK", "False").lower() == "true" @@ -26,6 +30,8 @@ async def generate_stream( # use_beam_search = params.get("use_beam_search", False) best_of = params.get("best_of", None) stop_str = params.get("stop", None) + think_start_token = params.get("think_start_token", _DEFAULT_THINK_START_TOKEN) + # think_end_token = params.get("think_end_token", _DEFAULT_THINK_END_TOKEN) stop_token_ids = params.get("stop_token_ids", None) or [] if tokenizer.eos_token_id is not None: @@ -68,8 +74,11 @@ async def generate_stream( best_of=best_of, **gen_params, ) + # vocab = tokenizer.get_vocab() results_generator = model.generate(prompt, sampling_params, request_id) + usage = None + finish_reason = None async for request_output in results_generator: prompt = request_output.prompt if echo: @@ -93,9 +102,18 @@ async def generate_stream( if len(request_output.outputs) == 1 else [output.finish_reason for output in request_output.outputs] ) - yield { - "text": text_outputs, - "error_code": 0, - "usage": usage, - "finish_reason": finish_reason, - } + if text_outputs: + # Tempora + if prompt.rstrip().endswith(think_start_token): + text_outputs = think_start_token + "\n" + text_outputs + msg = parse_chat_message( + text_outputs, + extract_reasoning=True, + ) + yield ModelOutput.build( + msg.content, + msg.reasoning_content, + error_code=0, + usage=usage, + finish_reason=finish_reason, + ) diff --git a/packages/dbgpt-core/src/dbgpt/model/proxy/llms/chatgpt.py b/packages/dbgpt-core/src/dbgpt/model/proxy/llms/chatgpt.py index 49a46890a..a7cfe57ee 100755 --- a/packages/dbgpt-core/src/dbgpt/model/proxy/llms/chatgpt.py +++ b/packages/dbgpt-core/src/dbgpt/model/proxy/llms/chatgpt.py @@ -1,4 +1,3 @@ -import importlib.metadata as metadata import logging from concurrent.futures import Executor from dataclasses import dataclass, field @@ -108,7 +107,7 @@ async def chatgpt_generate_stream( parameters=[ Parameter.build_from( label=_("OpenAI API Key"), - name="apk_key", + name="api_key", type=str, optional=True, default=None, @@ -154,9 +153,6 @@ class OpenAILLMClient(ProxyLLMClient): "Could not import python package: openai " "Please install openai by command `pip install openai" ) from exc - self._openai_version = metadata.version("openai") - self._openai_less_then_v1 = not self._openai_version >= "1.0.0" - self.check_sdk_version(self._openai_version) self._init_params = OpenAIParameters( api_type=self._resolve_env_vars(api_type), @@ -177,15 +173,9 @@ class OpenAILLMClient(ProxyLLMClient): self._openai_kwargs = openai_kwargs or {} super().__init__(model_names=[model_alias], context_length=context_length) - if self._openai_less_then_v1: - from dbgpt.model.utils.chatgpt_utils import _initialize_openai - - _initialize_openai(self._init_params) - - if not self._openai_less_then_v1: - # Prepare openai client and cache default headers - # It will block the main thread in some cases - _ = self.client.default_headers + # Prepare openai client and cache default headers + # It will block the main thread in some cases + _ = self.client.default_headers @classmethod def param_class(cls) -> Type[OpenAICompatibleDeployModelParameters]: @@ -231,21 +221,8 @@ class OpenAILLMClient(ProxyLLMClient): """ return chatgpt_generate_stream - def check_sdk_version(self, version: str) -> None: - """Check the sdk version of the client. - - Raises: - ValueError: If check failed. - """ - pass - @property def client(self) -> "ClientType": - if self._openai_less_then_v1: - raise ValueError( - "Current model (Load by OpenAILLMClient) require " - "openai.__version__>=1.0.0" - ) if self._client is None: from dbgpt.model.utils.chatgpt_utils import _build_openai_client @@ -266,10 +243,7 @@ class OpenAILLMClient(ProxyLLMClient): ) -> Dict[str, Any]: payload = {"stream": stream} model = request.model or self.default_model - if self._openai_less_then_v1 and self._api_type == "azure": - payload["engine"] = model - else: - payload["model"] = model + payload["model"] = model # Apply openai kwargs for k, v in self._openai_kwargs.items(): payload[k] = v @@ -292,14 +266,10 @@ class OpenAILLMClient(ProxyLLMClient): messages = request.to_common_messages() payload = self._build_request(request) logger.info( - f"Send request to openai({self._openai_version}), payload: {payload}\n\n " - f"messages:\n{messages}" + f"Send request to openai, payload: {payload}\n\n messages:\n{messages}" ) try: - if self._openai_less_then_v1: - return await self.generate_less_then_v1(messages, payload) - else: - return await self.generate_v1(messages, payload) + return await self.generate_v1(messages, payload) except Exception as e: return ModelOutput( text=f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}", @@ -315,15 +285,10 @@ class OpenAILLMClient(ProxyLLMClient): messages = request.to_common_messages() payload = self._build_request(request, stream=True) logger.info( - f"Send request to openai({self._openai_version}), payload: {payload}\n\n " - f"messages:\n{messages}" + f"Send request to openai, payload: {payload}\n\n messages:\n{messages}" ) - if self._openai_less_then_v1: - async for r in self.generate_stream_less_then_v1(messages, payload): - yield r - else: - async for r in self.generate_stream_v1(messages, payload): - yield r + async for r in self.generate_stream_v1(messages, payload): + yield r async def generate_v1( self, messages: List[Dict[str, Any]], payload: Dict[str, Any] @@ -331,21 +296,13 @@ class OpenAILLMClient(ProxyLLMClient): chat_completion = await self.client.chat.completions.create( messages=messages, **payload ) + reasoning_content = "" + message_obj = chat_completion.choices[0].message + if hasattr(message_obj, "reasoning_content"): + reasoning_content = message_obj.reasoning_content text = chat_completion.choices[0].message.content usage = chat_completion.usage.dict() - return ModelOutput(text=text, error_code=0, usage=usage) - - async def generate_less_then_v1( - self, messages: List[Dict[str, Any]], payload: Dict[str, Any] - ) -> ModelOutput: - import openai - - chat_completion = await openai.ChatCompletion.acreate( - messages=messages, **payload - ) - text = chat_completion.choices[0].message.content - usage = chat_completion.usage.to_dict() - return ModelOutput(text=text, error_code=0, usage=usage) + return ModelOutput.build(text, reasoning_content, usage=usage) async def generate_stream_v1( self, messages: List[Dict[str, Any]], payload: Dict[str, Any] @@ -354,31 +311,23 @@ class OpenAILLMClient(ProxyLLMClient): messages=messages, **payload ) text = "" + reasoning_content = "" + usage = None async for r in chat_completion: if len(r.choices) == 0: continue # Check for empty 'choices' issue in Azure GPT-4o responses if r.choices[0] is not None and r.choices[0].delta is None: continue + delta_obj = r.choices[0].delta + if hasattr(delta_obj, "reasoning_content"): + reasoning_content += delta_obj.reasoning_content or "" if r.choices[0].delta.content is not None: - content = r.choices[0].delta.content - text += content - yield ModelOutput(text=text, error_code=0) - - async def generate_stream_less_then_v1( - self, messages: List[Dict[str, Any]], payload: Dict[str, Any] - ) -> AsyncIterator[ModelOutput]: - import openai - - res = await openai.ChatCompletion.acreate(messages=messages, **payload) - text = "" - async for r in res: - if not r.get("choices"): - continue - if r["choices"][0]["delta"].get("content") is not None: - content = r["choices"][0]["delta"]["content"] - text += content - yield ModelOutput(text=text, error_code=0) + text += r.choices[0].delta.content + if text or reasoning_content: + if hasattr(r, "usage") and r.usage is not None: + usage = r.usage.dict() + yield ModelOutput.build(text, reasoning_content, usage=usage) async def models(self) -> List[ModelMetadata]: model_metadata = ModelMetadata( diff --git a/packages/dbgpt-core/src/dbgpt/model/proxy/llms/tongyi.py b/packages/dbgpt-core/src/dbgpt/model/proxy/llms/tongyi.py index a778fb51d..d229f55c6 100644 --- a/packages/dbgpt-core/src/dbgpt/model/proxy/llms/tongyi.py +++ b/packages/dbgpt-core/src/dbgpt/model/proxy/llms/tongyi.py @@ -62,12 +62,12 @@ class TongyiDeployModelParameters(OpenAICompatibleDeployModelParameters): ) -def tongyi_generate_stream( +async def tongyi_generate_stream( model: ProxyModel, tokenizer, params, device, context_len=2048 ): client: TongyiLLMClient = model.proxy_llm_client request = parse_model_request(params, client.default_model, stream=True) - for r in client.sync_generate_stream(request): + async for r in client.generate_stream(request): yield r diff --git a/packages/dbgpt-core/src/dbgpt/model/utils/chatgpt_utils.py b/packages/dbgpt-core/src/dbgpt/model/utils/chatgpt_utils.py index c5c524488..c8e68f36d 100644 --- a/packages/dbgpt-core/src/dbgpt/model/utils/chatgpt_utils.py +++ b/packages/dbgpt-core/src/dbgpt/model/utils/chatgpt_utils.py @@ -97,42 +97,6 @@ def _initialize_openai_v1(init_params: OpenAIParameters): return openai_params, api_type, api_version, api_azure_deployment -def _initialize_openai(params: OpenAIParameters): - try: - import openai - except ImportError as exc: - raise ValueError( - "Could not import python package: openai " - "Please install openai by command `pip install openai` " - ) from exc - - api_type = params.api_type or os.getenv("OPENAI_API_TYPE", "open_ai") - - api_base = params.api_base or os.getenv( - "OPENAI_API_TYPE", - os.getenv("AZURE_OPENAI_ENDPOINT") if api_type == "azure" else None, - ) - api_key = params.api_key or os.getenv( - "OPENAI_API_KEY", - os.getenv("AZURE_OPENAI_KEY") if api_type == "azure" else None, - ) - api_version = params.api_version or os.getenv("OPENAI_API_VERSION") - - if not api_base and params.full_url: - # Adapt previous proxy_server_url configuration - api_base = params.full_url.split("/chat/completions")[0] - if api_type: - openai.api_type = api_type - if api_base: - openai.api_base = api_base - if api_key: - openai.api_key = api_key - if api_version: - openai.api_version = api_version - if params.proxies: - openai.proxy = params.proxies - - def _build_openai_client(init_params: OpenAIParameters) -> Tuple[str, ClientType]: import httpx diff --git a/packages/dbgpt-core/src/dbgpt/model/utils/parse_utils.py b/packages/dbgpt-core/src/dbgpt/model/utils/parse_utils.py new file mode 100644 index 000000000..7172d4dcc --- /dev/null +++ b/packages/dbgpt-core/src/dbgpt/model/utils/parse_utils.py @@ -0,0 +1,425 @@ +import json +import re +from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union + +_DEFAULT_THINK_START_TOKEN = "" +_DEFAULT_THINK_END_TOKEN = "" + + +class StreamingEvent(NamedTuple): + """Streaming event type, representing various events in the streaming parsing + process + """ + + type: str + """Event type: 'reasoning_start', 'reasoning_content', 'reasoning_end', + 'tool_call_start', 'tool_call_content', 'tool_call_end', 'content' + """ + content: str # Event content + + +class ParsedChatMessage: + """Universal chat message structure""" + + def __init__(self): + self.role: str = "assistant" + self.content: str = "" + self.reasoning_content: str = "" + self.tool_calls: List[Dict[str, Any]] = [] + # Streaming state tracking + self.streaming_state: Dict[str, Any] = { + "in_reasoning": False, + "reasoning_pattern": None, + "in_tool_call": False, + "tool_call_pattern": None, + # No longer using buffers, output events directly + } + + +def string_strip(s: str) -> str: + """Remove leading and trailing whitespace from a string""" + return s.strip() if s else "" + + +def parse_json_tool_calls( + tool_calls_text: str, + default_name: Optional[str] = None, + function_regex: Optional[re.Pattern] = None, + close_regex: Optional[re.Pattern] = None, +) -> List[Dict[str, Any]]: + """Parse JSON format tool calls""" + tool_calls = [] + + # Use default regex if not provided + if function_regex is None: + function_regex = re.compile( + r'function\s*(?:name)?\s*[:=]?\s*["\']?([^"\'\n]+)["\']?\s*\n```(?:json)?\s*\n' # noqa + ) + + if close_regex is None: + close_regex = re.compile(r"```\s*") + + current_pos = 0 + remaining_text = tool_calls_text + + while current_pos < len(tool_calls_text): + # Find the beginning of function call + function_match = function_regex.search(remaining_text) + if not function_match: + break + + function_name = function_match.group(1).strip() + start_json = function_match.end() + + # Find the end of JSON content + close_match = close_regex.search(remaining_text[start_json:]) + if not close_match: + break + + json_content = remaining_text[start_json : start_json + close_match.start()] + + try: + args = json.loads(json_content) + tool_call = {"name": function_name or default_name or "", "arguments": args} + tool_calls.append(tool_call) + except json.JSONDecodeError: + # If JSON parsing fails, try to extract raw text + tool_call = { + "name": function_name or default_name or "", + "arguments": json_content.strip(), + } + tool_calls.append(tool_call) + + # Update position to continue searching + current_pos += start_json + close_match.end() + remaining_text = tool_calls_text[current_pos:] + + return tool_calls + + +def process_streaming_chunk( + chunk: str, + msg: ParsedChatMessage, + reasoning_patterns: List[Dict[str, str]], + tool_call_patterns: List[Dict[str, str]], + extract_tool_calls: bool = True, +) -> List[StreamingEvent]: + """ + Process a single chunk of streaming message, return a list of parsed events + + Parameters: + chunk: Current received text block + msg: ParsedChatMessage object to update + reasoning_patterns: List of reasoning patterns + tool_call_patterns: List of tool call patterns + extract_tool_calls: Whether to extract tool calls + + Returns: + List of events, each representing a parsed part of content + """ + state = msg.streaming_state + events = [] + remaining_chunk = chunk + + while remaining_chunk: + # Currently in reasoning content + if state["in_reasoning"]: + end_marker = state["reasoning_pattern"]["end"] + if end_marker in remaining_chunk: + end_idx = remaining_chunk.find(end_marker) + # Output reasoning content event + if end_idx > 0: + reasoning_part = remaining_chunk[:end_idx] + events.append( + StreamingEvent(type="reasoning_content", content=reasoning_part) + ) + # Append reasoning content instead of replacing + msg.reasoning_content += reasoning_part + + # Output reasoning end event + events.append(StreamingEvent(type="reasoning_end", content="")) + + # Reset reasoning state + state["in_reasoning"] = False + state["reasoning_pattern"] = None + + # Process remaining content + remaining_chunk = remaining_chunk[end_idx + len(end_marker) :] + else: + # The entire chunk is reasoning content + events.append( + StreamingEvent(type="reasoning_content", content=remaining_chunk) + ) + # Similarly, append instead of replace + msg.reasoning_content += remaining_chunk + remaining_chunk = "" + continue + + # Currently in tool call + if state["in_tool_call"] and extract_tool_calls: + end_marker = state["tool_call_pattern"]["end"] + if end_marker in remaining_chunk: + end_idx = remaining_chunk.find(end_marker) + # Output tool call content event + if end_idx > 0: + tool_call_part = remaining_chunk[:end_idx] + events.append( + StreamingEvent(type="tool_call_content", content=tool_call_part) + ) + + # Store content before end marker temporarily for parsing + tool_call_text = ( + state.get("tool_call_text", "") + remaining_chunk[:end_idx] + ) + + # Try to parse tool call content + function_regex_patterns = [ + re.compile( + r"<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n" + ), + re.compile(r"function\s*:\s*([^\n]+)\n```json\n"), + re.compile(r'function\s*name\s*=\s*"([^"]+)"\n```json\n'), + ] + + close_regex_patterns = [ + re.compile(r"```\s*<｜tool▁call▁end｜>"), + re.compile(r"```\s*"), + ] + + # Try to parse tool calls + for func_regex in function_regex_patterns: + for close_regex in close_regex_patterns: + tool_calls = parse_json_tool_calls( + tool_call_text, None, func_regex, close_regex + ) + if tool_calls: + msg.tool_calls.extend(tool_calls) + break + if msg.tool_calls: + break + + # Output tool call end event + events.append(StreamingEvent(type="tool_call_end", content="")) + + # Reset tool call state + state["in_tool_call"] = False + state["tool_call_pattern"] = None + state.pop("tool_call_text", None) + + # Process remaining content + remaining_chunk = remaining_chunk[end_idx + len(end_marker) :] + else: + # The entire chunk is tool call content + events.append( + StreamingEvent(type="tool_call_content", content=remaining_chunk) + ) + # Accumulate tool call text for later parsing + state["tool_call_text"] = ( + state.get("tool_call_text", "") + remaining_chunk + ) + remaining_chunk = "" + continue + + # Check for reasoning start markers + reasoning_start_found = False + for pattern in reasoning_patterns: + start_marker = pattern["start"] + if start_marker in remaining_chunk: + start_idx = remaining_chunk.find(start_marker) + + # Output regular content before the marker + if start_idx > 0: + content_part = remaining_chunk[:start_idx] + events.append(StreamingEvent(type="content", content=content_part)) + msg.content += content_part + + # Output reasoning start event + events.append(StreamingEvent(type="reasoning_start", content="")) + + # Set reasoning state + state["in_reasoning"] = True + state["reasoning_pattern"] = pattern + + # Process content after the marker + remaining_chunk = remaining_chunk[start_idx + len(start_marker) :] + reasoning_start_found = True + break + + if reasoning_start_found: + continue + + # Check for tool call start markers + tool_call_start_found = False + if extract_tool_calls: + for pattern in tool_call_patterns: + start_marker = pattern["start"] + if start_marker in remaining_chunk: + start_idx = remaining_chunk.find(start_marker) + + # Output regular content before the marker + if start_idx > 0: + content_part = remaining_chunk[:start_idx] + events.append( + StreamingEvent(type="content", content=content_part) + ) + msg.content += content_part + + # Output tool call start event + events.append(StreamingEvent(type="tool_call_start", content="")) + + # Set tool call state + state["in_tool_call"] = True + state["tool_call_pattern"] = pattern + state["tool_call_text"] = "" + + # Process content after the marker + remaining_chunk = remaining_chunk[start_idx + len(start_marker) :] + tool_call_start_found = True + break + + if tool_call_start_found: + continue + + # If no special markers, current chunk is regular content + events.append(StreamingEvent(type="content", content=remaining_chunk)) + msg.content += remaining_chunk + remaining_chunk = "" + + return events + + +def parse_chat_message( + input_text: str, + extract_reasoning: bool = True, + extract_tool_calls: bool = False, + is_streaming: bool = False, + reasoning_patterns: Optional[List[Dict[str, str]]] = None, + tool_call_patterns: Optional[List[Dict[str, str]]] = None, + streaming_state: Optional[ParsedChatMessage] = None, +) -> Union[ParsedChatMessage, Tuple[ParsedChatMessage, List[StreamingEvent]]]: + """ + Universal chat message parsing function + + Parameters: + input_text: Input text + extract_reasoning: Whether to extract reasoning content + extract_tool_calls: Whether to extract tool calls + is_streaming: Whether to process as streaming message + reasoning_patterns: Custom list of reasoning patterns, each pattern is a + dictionary containing start and end markers + tool_call_patterns: Custom list of tool call patterns, each pattern is a + dictionary containing start and end markers + streaming_state: State object passed in when processing streaming messages, + used to track progress + + Returns: + If is_streaming=False, returns the parsed ParsedChatMessage object + If is_streaming=True, returns (ParsedChatMessage, events) tuple, where events + is the list of parsed events + """ + # Default reasoning patterns + if reasoning_patterns is None: + reasoning_patterns = [ + {"start": _DEFAULT_THINK_START_TOKEN, "end": _DEFAULT_THINK_END_TOKEN}, + {"start": "", "end": ""}, + {"start": "<思考>", "end": ""}, + ] + + # Default tool call patterns + if tool_call_patterns is None: + tool_call_patterns = [ + {"start": "<｜tool▁calls▁begin｜>", "end": "<｜tool▁calls▁end｜>"}, + {"start": "<｜tool_calls_begin｜>", "end": "<｜tool_calls_end｜>"}, + {"start": "<｜tool calls begin｜>", "end": "<｜tool calls end｜>"}, + {"start": "<｜tool\\_calls\\_begin｜>", "end": "<｜tool\\_calls\\_end｜>"}, + {"start": "", "end": ""}, + {"start": "", "end": ""}, + ] + + # Streaming processing mode + if is_streaming: + # Use provided state or create new one + msg = streaming_state or ParsedChatMessage() + + # Process current text block and get events + events = process_streaming_chunk( + input_text, msg, reasoning_patterns, tool_call_patterns, extract_tool_calls + ) + + return msg, events + + # Non-streaming processing mode (original logic) + msg = ParsedChatMessage() + + # Parse reasoning content + reasoning_content = "" + content = input_text + + for pattern in reasoning_patterns: + start_marker = pattern["start"] + end_marker = pattern["end"] + + if start_marker in content and end_marker in content: + start_idx = content.find(start_marker) + end_idx = content.find(end_marker, start_idx + len(start_marker)) + + if start_idx >= 0 and end_idx >= 0: + reasoning_text = content[start_idx + len(start_marker) : end_idx] + reasoning_content = string_strip(reasoning_text) + + # Remove reasoning part from original content + if extract_reasoning: + content = content[:start_idx] + content[end_idx + len(end_marker) :] + break + + # Parse tool calls + tool_calls_text = "" + + if extract_tool_calls: + for pattern in tool_call_patterns: + start_marker = pattern["start"] + end_marker = pattern["end"] + + if start_marker in content and end_marker in content: + start_idx = content.find(start_marker) + end_idx = content.find(end_marker, start_idx + len(start_marker)) + + if start_idx >= 0 and end_idx >= 0: + tool_calls_text = content[start_idx + len(start_marker) : end_idx] + + # Remove tool call part from original content + content = content[:start_idx] + content[end_idx + len(end_marker) :] + break + + # Process function calls + function_regex_patterns = [ + re.compile( + r"<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n" + ), + re.compile(r"function\s*:\s*([^\n]+)\n```json\n"), + re.compile(r'function\s*name\s*=\s*"([^"]+)"\n```json\n'), + ] + + close_regex_patterns = [ + re.compile(r"```\s*<｜tool▁call▁end｜>"), + re.compile(r"```\s*"), + ] + + if tool_calls_text: + for func_regex in function_regex_patterns: + for close_regex in close_regex_patterns: + tool_calls = parse_json_tool_calls( + tool_calls_text, None, func_regex, close_regex + ) + if tool_calls: + msg.tool_calls = tool_calls + break + if msg.tool_calls: + break + + # Set final content + msg.content = string_strip(content) + if extract_reasoning: + msg.reasoning_content = reasoning_content + + return msg diff --git a/packages/dbgpt-core/src/dbgpt/model/utils/tests/__init__.py b/packages/dbgpt-core/src/dbgpt/model/utils/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/packages/dbgpt-core/src/dbgpt/model/utils/tests/test_parse_utils.py b/packages/dbgpt-core/src/dbgpt/model/utils/tests/test_parse_utils.py new file mode 100644 index 000000000..d339960ca --- /dev/null +++ b/packages/dbgpt-core/src/dbgpt/model/utils/tests/test_parse_utils.py @@ -0,0 +1,570 @@ +import pytest + +from ..parse_utils import parse_chat_message + + +# Non-streaming processing tests +def test_parse_deepseek_format(): + """Test parsing DeepSeek format messages""" + input_text = """I need to analyze the user's request and call the \ +appropriate function to get information. +The user is requesting weather information, I will call the weather API to get data. + +<｜tool▁calls▁begin｜> +<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather +```json +{ + "location": "Beijing", + "date": "2023-05-20" +} +``` +<｜tool▁call▁end｜> +<｜tool▁calls▁end｜>""" + + result = parse_chat_message(input_text, extract_tool_calls=True) + + assert result.role == "assistant" + assert "requesting weather information" in result.content + assert "analyze the user's request" in result.reasoning_content + assert len(result.tool_calls) == 1 + assert result.tool_calls[0]["name"] == "get_weather" + assert result.tool_calls[0]["arguments"]["location"] == "Beijing" + assert result.tool_calls[0]["arguments"]["date"] == "2023-05-20" + + +def test_parse_chinese_format(): + """Test parsing Chinese content messages""" + input_text = """我需要解析用户的请求并调用适当的函数来获取信息。 +用户请求的是天气信息，我将调用天气API获取数据。 + +<｜tool▁calls▁begin｜> +<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather +```json +{ + "location": "北京", + "date": "2023-05-20" +} +``` +<｜tool▁call▁end｜> +<｜tool▁calls▁end｜>""" + + result = parse_chat_message(input_text, extract_tool_calls=True) + + assert result.role == "assistant" + assert "用户请求的是天气信息" in result.content + assert "我需要解析用户的请求" in result.reasoning_content + assert len(result.tool_calls) == 1 + assert result.tool_calls[0]["name"] == "get_weather" + assert result.tool_calls[0]["arguments"]["location"] == "北京" + assert result.tool_calls[0]["arguments"]["date"] == "2023-05-20" + + +def test_parse_custom_format(): + """Test parsing custom format messages""" + input_text = """I need to check the current stock price for AAPL.\ + +I'll look up the stock price for Apple Inc. + + +function name="get_stock_price" +```json +{ + "symbol": "AAPL", + "market": "NASDAQ" +} +``` +""" + + custom_reasoning = [{"start": "", "end": ""}] + custom_tools = [{"start": "", "end": ""}] + + result = parse_chat_message( + input_text, + reasoning_patterns=custom_reasoning, + tool_call_patterns=custom_tools, + extract_tool_calls=True, + ) + + assert "I'll look up the stock price" in result.content + assert "I need to check the current stock price" in result.reasoning_content + assert len(result.tool_calls) == 1 + assert result.tool_calls[0]["name"] == "get_stock_price" + assert result.tool_calls[0]["arguments"]["symbol"] == "AAPL" + + +def test_extract_reasoning_option(): + """Test the extract_reasoning option""" + input_text = "This is reasoning contentThis is regular content" + + # Don't extract reasoning content + result1 = parse_chat_message(input_text, extract_reasoning=False) + assert ( + "This is reasoning contentThis is regular content" + == result1.content + ) + assert "" == result1.reasoning_content + + # Extract reasoning content + result2 = parse_chat_message(input_text, extract_reasoning=True) + assert "This is regular content" == result2.content + assert "This is reasoning content" == result2.reasoning_content + + +def test_extract_tool_calls_option(): + """Test the extract_tool_calls option""" + input_text = """I'll look up information for you. + +<｜tool▁calls▁begin｜> +<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_data +```json +{ + "query": "test" +} +``` +<｜tool▁call▁end｜> +<｜tool▁calls▁end｜>""" + + # Don't extract tool calls + result1 = parse_chat_message(input_text, extract_tool_calls=False) + assert input_text == result1.content + assert len(result1.tool_calls) == 0 + + # Extract tool calls + result2 = parse_chat_message(input_text, extract_tool_calls=True) + assert "I'll look up information for you." in result2.content + assert len(result2.tool_calls) == 1 + assert result2.tool_calls[0]["name"] == "get_data" + assert result2.tool_calls[0]["arguments"]["query"] == "test" + + +def test_extract_both_options(): + """Test both extract_reasoning and extract_tool_calls options together""" + input_text = """I need to search for dataI'll search for information. + +<｜tool▁calls▁begin｜> +<｜tool▁call▁begin｜>function<｜tool▁sep｜>search +```json +{ + "query": "test" +} +``` +<｜tool▁call▁end｜> +<｜tool▁calls▁end｜>""" + + # Extract neither + result1 = parse_chat_message( + input_text, extract_reasoning=False, extract_tool_calls=False + ) + assert input_text == result1.content + assert "" == result1.reasoning_content + assert len(result1.tool_calls) == 0 + + # Extract both + result2 = parse_chat_message( + input_text, extract_reasoning=True, extract_tool_calls=True + ) + assert "I'll search for information." in result2.content + assert "I need to search for data" == result2.reasoning_content + assert len(result2.tool_calls) == 1 + + # Extract only reasoning + result3 = parse_chat_message( + input_text, extract_reasoning=True, extract_tool_calls=False + ) + assert "<｜tool▁calls▁begin｜>" in result3.content + assert "I need to search for data" == result3.reasoning_content + assert len(result3.tool_calls) == 0 + + # Extract only tool calls + result4 = parse_chat_message( + input_text, extract_reasoning=False, extract_tool_calls=True + ) + assert ( + "I need to search for dataI'll search for information." + == result4.content + ) + assert "" == result4.reasoning_content + assert len(result4.tool_calls) == 1 + + +def test_multiple_reasoning_patterns(): + """Test different reasoning marker patterns""" + input_texts = [ + "Reasoning content 1Regular content", + "Reasoning content 2Regular content", + "<思考>推理内容3普通内容", + ] + + for i, text in enumerate(input_texts, 1): + result = parse_chat_message(text) + if i == 3: + assert "推理内容3" == result.reasoning_content + assert "普通内容" == result.content + else: + assert f"Reasoning content {i}" == result.reasoning_content + assert "Regular content" == result.content + + +def test_mixed_language_content(): + """Test parsing messages with mixed language content""" + input_text = """I need to analyze 用户的请求 and provide information. +I will help 查询天气 for the specified location. + +<｜tool▁calls▁begin｜> +<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather +```json +{ + "location": "Shanghai 上海", + "date": "2023-05-20" +} +``` +<｜tool▁call▁end｜> +<｜tool▁calls▁end｜>""" + + result = parse_chat_message(input_text, extract_tool_calls=True) + + assert "I will help 查询天气" in result.content + assert "I need to analyze 用户的请求" in result.reasoning_content + assert len(result.tool_calls) == 1 + assert result.tool_calls[0]["arguments"]["location"] == "Shanghai 上海" + + +def test_no_special_markers(): + """Test parsing plain text without any special markers""" + input_text = "This is a regular text with no special markers." + result = parse_chat_message(input_text) + + assert input_text == result.content + assert "" == result.reasoning_content + assert len(result.tool_calls) == 0 + + +def test_malformed_json(): + """Test handling of malformed JSON in tool calls""" + input_text = """<｜tool▁calls▁begin｜> +<｜tool▁call▁begin｜>function<｜tool▁sep｜>broken_function +```json +{ + "query": "test", + missing_quotes: value, +} +``` +<｜tool▁call▁end｜> +<｜tool▁calls▁end｜>""" + + result = parse_chat_message(input_text, extract_tool_calls=True) + + assert len(result.tool_calls) == 1 + assert result.tool_calls[0]["name"] == "broken_function" + assert isinstance( + result.tool_calls[0]["arguments"], str + ) # Should contain the raw string + + +# Streaming processing tests +def test_streaming_mode_reasoning(): + """Test processing reasoning content in streaming mode""" + chunks = [ + "I will help you ", + "with your query.", + "I need to analyze ", + "the user's request.", + "Processing your request now.", + ] + + msg = None + all_events = [] + + for chunk in chunks: + msg, events = parse_chat_message(chunk, is_streaming=True, streaming_state=msg) + all_events.extend(events) + + # Verify final message + assert "I will help you with your query.Processing your request now." == msg.content + assert "I need to analyze the user's request." == msg.reasoning_content + + # Verify event sequence + event_types = [e.type for e in all_events] + expected_types = [ + "content", # "I will help you " + "content", # "with your query." + "reasoning_start", # marker + "reasoning_content", # "I need to analyze " + "reasoning_content", # "the user's request." + "reasoning_end", # marker + "content", # "Processing your request now." + ] + + assert expected_types == event_types + + +def test_streaming_mode_tool_call(): + """Test processing tool calls in streaming mode""" + chunks = [ + "I will search ", + "for information.<｜tool▁calls▁begin｜>", + "<｜tool▁call▁begin｜>function<｜tool▁sep｜>search_data", + "\n```json\n{", + '"query": "test data",', + '"limit": 10', + "}\n```", + "<｜tool▁call▁end｜>", + "<｜tool▁calls▁end｜>", + "Search complete.", + ] + + msg = None + all_events = [] + + for chunk in chunks: + msg, events = parse_chat_message( + chunk, is_streaming=True, streaming_state=msg, extract_tool_calls=True + ) + all_events.extend(events) + + # Verify final message + assert "I will search for information.Search complete." == msg.content + assert len(msg.tool_calls) == 1 + assert msg.tool_calls[0]["name"] == "search_data" + assert msg.tool_calls[0]["arguments"]["query"] == "test data" + assert msg.tool_calls[0]["arguments"]["limit"] == 10 + + # Verify event sequence + event_types = [e.type for e in all_events] + assert "tool_call_start" in event_types + assert "tool_call_content" in event_types + assert "tool_call_end" in event_types + + +def test_streaming_mode_without_tool_calls(): + """Test streaming mode with extract_tool_calls=False""" + chunks = [ + "I will search for data", + ".<｜tool▁calls▁begin｜>", + "Tool call content", + "<｜tool▁calls▁end｜>", + "Search complete.", + ] + + msg = None + all_events = [] + + for chunk in chunks: + msg, events = parse_chat_message( + chunk, is_streaming=True, streaming_state=msg, extract_tool_calls=False + ) + all_events.extend(events) + + # Verify final message should include tool call markers + assert ( + "I will search for data.<｜tool▁calls▁begin｜>Tool call content<｜tool▁calls▁end｜>Search complete." # noqa + == msg.content + ) + assert len(msg.tool_calls) == 0 + + # Verify no tool call events + event_types = [e.type for e in all_events] + assert "tool_call_start" not in event_types + assert "tool_call_content" not in event_types + assert "tool_call_end" not in event_types + + +def test_streaming_mode_overlapping(): + """Test handling nested or overlapping markers in streaming mode""" + chunks = [ + "StartI need to ", + "analyzeMiddle<｜tool▁calls▁begin｜>", + "Tool call content<｜tool▁calls▁end｜>", + "End", + ] + + msg = None + all_events = [] + + for i, chunk in enumerate(chunks): + msg, events = parse_chat_message( + chunk, is_streaming=True, streaming_state=msg, extract_tool_calls=True + ) + if i == 0: + # First chunk contains reasoning content + assert "I need to " == msg.reasoning_content + all_events.extend(events) + + # Verify final message + assert "StartMiddleEnd" == msg.content + assert "I need to analyze" == msg.reasoning_content + + # Ensure correct event order + reasoning_start_indices = [ + i for i, e in enumerate(all_events) if e.type == "reasoning_start" + ] + reasoning_end_indices = [ + i for i, e in enumerate(all_events) if e.type == "reasoning_end" + ] + tool_start_indices = [ + i for i, e in enumerate(all_events) if e.type == "tool_call_start" + ] + tool_end_indices = [ + i for i, e in enumerate(all_events) if e.type == "tool_call_end" + ] + + # Verify nesting order + assert reasoning_start_indices[0] < reasoning_end_indices[0] + assert reasoning_end_indices[0] < tool_start_indices[0] + assert tool_start_indices[0] < tool_end_indices[0] + + +def test_incomplete_markers(): + """Test handling incomplete markers""" + # Test incomplete reasoning marker + chunks1 = [ + "StartReasoning", + "content", # No end marker + ] + + msg1 = None + for chunk in chunks1: + msg1, _ = parse_chat_message( + chunk, is_streaming=True, streaming_state=msg1, extract_tool_calls=False + ) + + # Verify reasoning content is recorded but state is still incomplete + assert "Reasoningcontent" == msg1.reasoning_content + assert msg1.streaming_state["in_reasoning"] is True + + # Test incomplete tool call marker + chunks2 = [ + "Start<｜tool▁calls▁begin｜>", + "Tool content", # No end marker + ] + + msg2 = None + for chunk in chunks2: + msg2, _ = parse_chat_message( + chunk, is_streaming=True, streaming_state=msg2, extract_tool_calls=True + ) + + # Verify tool call content is recorded but state is still incomplete + assert msg2.streaming_state["in_tool_call"] is True + assert "Tool content" in msg2.streaming_state.get("tool_call_text", "") + + +def test_multiple_special_sections(): + """Test handling multiple special sections""" + input_text = """Reasoning content 1Regular content 1 +<｜tool▁calls▁begin｜>Tool call content<｜tool▁calls▁end｜> +Regular content 2Reasoning content 2End""" + + result = parse_chat_message(input_text, extract_tool_calls=True) + + # Verify only first reasoning content is extracted + assert "Reasoning content 1" == result.reasoning_content + assert ( + "Regular content 1\n\nRegular content 2Reasoning content 2End" + == result.content + ) + + # Use streaming processing to handle multiple reasoning parts + chunks = [ + "Reasoning content 1Regular content 1\n", + "<｜tool▁calls▁begin｜>Tool call content<｜tool▁calls▁end｜>\n", + "Regular content 2Reasoning content 2End", + ] + + msg = None + all_events = [] + + for chunk in chunks: + msg, events = parse_chat_message( + chunk, is_streaming=True, streaming_state=msg, extract_tool_calls=True + ) + all_events.extend(events) + + # Verify event sequence contains two reasoning sections + reasoning_start_counts = sum(1 for e in all_events if e.type == "reasoning_start") + reasoning_end_counts = sum(1 for e in all_events if e.type == "reasoning_end") + + assert reasoning_start_counts == 2 + assert reasoning_end_counts == 2 + + # In streaming mode, reasoning content should accumulate + assert "Reasoning content 1Reasoning content 2" == msg.reasoning_content + + +def test_custom_streaming_patterns(): + """Test custom streaming pattern markers""" + custom_reasoning = [{"start": "{{thinking}}", "end": "{{/thinking}}"}] + custom_tools = [{"start": "{{tools}}", "end": "{{/tools}}"}] + + chunks = [ + "Start{{thinking}}I need to ", + "analyze{{/thinking}}Middle{{tools}}", + "Tool content{{/tools}}", + "End", + ] + + msg = None + all_events = [] + + for chunk in chunks: + msg, events = parse_chat_message( + chunk, + is_streaming=True, + streaming_state=msg, + reasoning_patterns=custom_reasoning, + tool_call_patterns=custom_tools, + extract_tool_calls=True, + ) + all_events.extend(events) + + # Verify final message + assert "StartMiddleEnd" == msg.content + assert "I need to analyze" == msg.reasoning_content + + # Verify event sequence + event_types = [e.type for e in all_events] + assert "reasoning_start" in event_types + assert "reasoning_content" in event_types + assert "reasoning_end" in event_types + assert "tool_call_start" in event_types + assert "tool_call_end" in event_types + + +def test_alternating_reasoning_and_tool_calls(): + """Test alternating between reasoning and tool calls in a single message""" + # Use streaming to capture all sections + chunks = [ + "First reasoning blockContent 1\n", + "<｜tool▁calls▁begin｜>Tool call 1<｜tool▁calls▁end｜>\n", + "Content 2Second reasoning block\n", + "<｜tool▁calls▁begin｜>Tool call 2<｜tool▁calls▁end｜>\n", + "Final content", + ] + + msg = None + all_events = [] + + for chunk in chunks: + msg, events = parse_chat_message( + chunk, is_streaming=True, streaming_state=msg, extract_tool_calls=True + ) + all_events.extend(events) + + # Verify content is parsed correctly - note the double newlines + assert "Content 1\n\nContent 2\n\nFinal content" == msg.content + assert "First reasoning blockSecond reasoning block" == msg.reasoning_content + + # Count events by type + event_counts = {} + for e in all_events: + event_counts[e.type] = event_counts.get(e.type, 0) + 1 + + assert event_counts.get("reasoning_start", 0) == 2 + assert event_counts.get("reasoning_end", 0) == 2 + assert event_counts.get("tool_call_start", 0) == 2 + assert event_counts.get("tool_call_end", 0) == 2 + + +if __name__ == "__main__": + # Run tests + pytest.main(["-v", "test_parse_utils.py"]) diff --git a/packages/dbgpt-core/src/dbgpt/util/utils.py b/packages/dbgpt-core/src/dbgpt/util/utils.py index ac5e616b7..a8939f007 100644 --- a/packages/dbgpt-core/src/dbgpt/util/utils.py +++ b/packages/dbgpt-core/src/dbgpt/util/utils.py @@ -150,7 +150,7 @@ def _build_logger( logger_dir = os.path.dirname(logger_filename) os.makedirs(logger_dir, exist_ok=True) handler = logging.handlers.TimedRotatingFileHandler( - logger_filename, when="D", utc=True + logger_filename, when="D", utc=True, encoding="utf-8" ) handler.setFormatter(formatter) @@ -167,9 +167,9 @@ def _build_logger( logging.getLogger(name).debug(f"Skipping non-logger: {name}") if redirect_stdio: - stdout_handler = logging.StreamHandler(sys.stdout) + stdout_handler = logging.StreamHandler(sys.stdout, encoding="utf-8") stdout_handler.setFormatter(formatter) - stderr_handler = logging.StreamHandler(sys.stderr) + stderr_handler = logging.StreamHandler(sys.stderr, encoding="utf-8") stderr_handler.setFormatter(formatter) root_logger = logging.getLogger() diff --git a/packages/dbgpt-core/src/dbgpt/vis/base.py b/packages/dbgpt-core/src/dbgpt/vis/base.py index 2c442bced..fbe7d5b8f 100644 --- a/packages/dbgpt-core/src/dbgpt/vis/base.py +++ b/packages/dbgpt-core/src/dbgpt/vis/base.py @@ -13,6 +13,19 @@ class Vis: """Return the prompt for the vis protocol.""" return None + def sync_generate_param(self, **kwargs) -> Optional[Dict[str, Any]]: + """Generate the parameters required by the vis protocol. + + Display corresponding content using vis protocol + + Args: + **kwargs: + + Returns: + vis protocol text + """ + return kwargs["content"] + async def generate_param(self, **kwargs) -> Optional[Dict[str, Any]]: """Generate the parameters required by the vis protocol. @@ -23,14 +36,18 @@ class Vis: Returns: vis protocol text """ - return kwargs["content"] + return self.sync_generate_param(**kwargs) + + def sync_display(self, **kwargs) -> Optional[str]: + """Display the content using the vis protocol.""" + content = json.dumps( + self.sync_generate_param(**kwargs), default=serialize, ensure_ascii=False + ) + return f"```{self.vis_tag()}\n{content}\n```" async def display(self, **kwargs) -> Optional[str]: """Display the content using the vis protocol.""" - content = json.dumps( - await self.generate_param(**kwargs), default=serialize, ensure_ascii=False - ) - return f"```{self.vis_tag()}\n{content}\n```" + return self.sync_display(**kwargs) @classmethod def vis_tag(cls) -> str: diff --git a/packages/dbgpt-core/src/dbgpt/vis/client.py b/packages/dbgpt-core/src/dbgpt/vis/client.py index 723993a01..138b8aeae 100644 --- a/packages/dbgpt-core/src/dbgpt/vis/client.py +++ b/packages/dbgpt-core/src/dbgpt/vis/client.py @@ -11,6 +11,7 @@ from .tags.vis_chart import VisChart from .tags.vis_code import VisCode from .tags.vis_dashboard import VisDashboard from .tags.vis_plugin import VisPlugin +from .tags.vis_thinking import VisThinking class VisClient: @@ -45,6 +46,7 @@ vis_client.register(VisAgentMessages) vis_client.register(VisPlugin) vis_client.register(VisAppLink) vis_client.register(VisApiResponse) +vis_client.register(VisThinking) def vis_name_change(vis_message: str) -> str: diff --git a/packages/dbgpt-core/src/dbgpt/vis/tags/vis_thinking.py b/packages/dbgpt-core/src/dbgpt/vis/tags/vis_thinking.py new file mode 100644 index 000000000..7b5407f04 --- /dev/null +++ b/packages/dbgpt-core/src/dbgpt/vis/tags/vis_thinking.py @@ -0,0 +1,24 @@ +from ..base import Vis + + +class VisThinking(Vis): + """VisThinking.""" + + @classmethod + async def build_message(cls, message: str) -> str: + vis = VisThinking() + return f"```{vis.vis_tag()}\n{message}\n```" + + def sync_display(self, **kwargs) -> str: + """Display the content using the vis protocol.""" + content = kwargs.get("content") + return f"```{self.vis_tag()}\n{content}\n```" + + @classmethod + def vis_tag(cls): + """Vis tag name. + + Returns: + str: The tag name associated with the visualization. + """ + return "vis-thinking" diff --git a/packages/dbgpt-ext/pyproject.toml b/packages/dbgpt-ext/pyproject.toml index e66a3ebd8..01aca97aa 100644 --- a/packages/dbgpt-ext/pyproject.toml +++ b/packages/dbgpt-ext/pyproject.toml @@ -72,6 +72,10 @@ storage_weaviate = ["weaviate-client"] storage_chromadb = ["chromadb>=0.4.22"] storage_elasticsearch = ["elasticsearch"] storage_obvector = ["pyobvector"] +proxy_tongyi = [ + # For tongyi proxy embedding model + "dashscope" +] [tool.uv] managed = true diff --git a/uv.lock b/uv.lock index 7d840c56a..e4b862e5d 100644 --- a/uv.lock +++ b/uv.lock @@ -1370,6 +1370,19 @@ wheels = [ { url = "https://pypi.tuna.tsinghua.edu.cn/packages/74/65/c162fbac63e867a055240b6600b92ef96c0eb7a1895312ac53c4be93d056/cymem-2.0.11-cp313-cp313-win_amd64.whl", hash = "sha256:25da111adf425c29af0cfd9fecfec1c71c8d82e2244a85166830a0817a66ada7", size = 39090 }, ] +[[package]] +name = "dashscope" +version = "1.22.1" +source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" } +dependencies = [ + { name = "aiohttp" }, + { name = "requests" }, + { name = "websocket-client" }, +] +wheels = [ + { url = "https://pypi.tuna.tsinghua.edu.cn/packages/fe/19/50bbf79851dcc96d125208f373b7817384715307717907f9e58f0c00348a/dashscope-1.22.1-py3-none-any.whl", hash = "sha256:3073f526980e39092cfdc3d57ad4541c3fa794c4318686a797daddeed6f751cf", size = 1276372 }, +] + [[package]] name = "dataclasses-json" version = "0.6.7" @@ -1480,6 +1493,7 @@ llama-cpp = [ ] llama-cpp-server = [ { name = "llama-cpp-server-py" }, + { name = "llama-cpp-server-py-core" }, ] proxy-anthropic = [ { name = "anthropic" }, @@ -1544,7 +1558,8 @@ requires-dist = [ { name = "jinja2", marker = "extra == 'simple-framework'" }, { name = "jsonschema", marker = "extra == 'framework'" }, { name = "llama-cpp-python", marker = "extra == 'llama-cpp'" }, - { name = "llama-cpp-server-py", marker = "extra == 'llama-cpp-server'" }, + { name = "llama-cpp-server-py", marker = "extra == 'llama-cpp-server'", specifier = ">=0.1.3" }, + { name = "llama-cpp-server-py-core", marker = "extra == 'llama-cpp-server'", specifier = ">=0.1.3" }, { name = "lyric-component-ts-transpiling", marker = "extra == 'code'", specifier = ">=0.1.6" }, { name = "lyric-js-worker", marker = "extra == 'code'", specifier = ">=0.1.6" }, { name = "lyric-py", marker = "extra == 'code'", specifier = ">=0.1.6" }, @@ -1872,6 +1887,9 @@ graph-rag = [ { name = "neo4j" }, { name = "networkx" }, ] +proxy-tongyi = [ + { name = "dashscope" }, +] rag = [ { name = "bs4" }, { name = "markdown" }, @@ -1908,6 +1926,7 @@ requires-dist = [ { name = "bs4", marker = "extra == 'rag'" }, { name = "chromadb", marker = "extra == 'storage-chromadb'", specifier = ">=0.4.22" }, { name = "clickhouse-connect", marker = "extra == 'datasource-clickhouse'" }, + { name = "dashscope", marker = "extra == 'proxy-tongyi'" }, { name = "dbgpt", editable = "packages/dbgpt-core" }, { name = "dbgpt-tugraph-plugins", marker = "extra == 'graph-rag'", specifier = ">=0.1.1" }, { name = "duckdb", marker = "extra == 'datasource-duckdb'" }, @@ -3812,18 +3831,18 @@ sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/a6/38/7a47b1fb1d83ea [[package]] name = "llama-cpp-server-py" -version = "0.1.2" +version = "0.1.3" source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" } dependencies = [ { name = "llama-cpp-server-py-core" }, ] -sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/45/e8/e4b7e5fdcfe56c97143dedb2b7f503df5675828dfb8ce58a6d8487bc370f/llama_cpp_server_py-0.1.2.tar.gz", hash = "sha256:295b7283e214f75c27ab6d40e218b9356342d1baabc2665aa9ef1f23f9a60905", size = 758 } +sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/81/ed/2f50f5730bcdf45427838e13f7e6dc975fc4b86b732b8f4041defb191819/llama_cpp_server_py-0.1.3.tar.gz", hash = "sha256:4cb1b6b0a898e2e66a5779fdba1f02b18823e452b994e8ce8365bd3c8180e1e4", size = 822 } [[package]] name = "llama-cpp-server-py-core" -version = "0.1.2" +version = "0.1.3" source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" } -sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/e9/af/068376aa50a4312f67121b06f3ff360c98a9692b5b7e99f3ea7e41989512/llama_cpp_server_py_core-0.1.2.tar.gz", hash = "sha256:5d1ca66d351286c7995ca47c4aaa7cb8d117120a35cfb52d97b5b45f34c7a9dc", size = 33850015 } +sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/2b/b0/8a543e5f3aa24b968ad3178b7f27d7ff05c24ea880eb04b1e116be9849bb/llama_cpp_server_py_core-0.1.3.tar.gz", hash = "sha256:3b753c521a655b54edef52909da30d40c68ba2af0083e9f4e59f9d558c00e487", size = 34000672 } [[package]] name = "lm-format-enforcer" diff --git a/web/components/chat/chat-content/config.tsx b/web/components/chat/chat-content/config.tsx index 990a18960..bfac7241b 100644 --- a/web/components/chat/chat-content/config.tsx +++ b/web/components/chat/chat-content/config.tsx @@ -18,6 +18,7 @@ import VisCode from './vis-code'; import VisConvertError from './vis-convert-error'; import VisDashboard from './vis-dashboard'; import VisPlugin from './vis-plugin'; +import { VisThinking } from './vis-thinking'; type MarkdownComponent = Parameters['0']['components']; @@ -140,6 +141,11 @@ const codeComponents = { return ; } }, + 'vis-thinking': ({ className, children }) => { + const content = String(children); + const _lang = className?.replace('language-', '') || 'javascript'; + return ; + }, }, defaultRenderer({ node, className, children, style, ...props }) { const content = String(children); diff --git a/web/components/chat/chat-content/vis-thinking.tsx b/web/components/chat/chat-content/vis-thinking.tsx new file mode 100644 index 000000000..96d254ad5 --- /dev/null +++ b/web/components/chat/chat-content/vis-thinking.tsx @@ -0,0 +1,36 @@ +import { DownOutlined, RightOutlined } from '@ant-design/icons'; +import React from 'react'; +import { useTranslation } from 'react-i18next'; + +interface Props { + content: string; +} + +export function VisThinking({ content }: Props) { + const { t } = useTranslation(); + const [expanded, setExpanded] = React.useState(true); // Control the expansion of the thinking process + // console.log("VisThinking", content) + return ( +
+
setExpanded(!expanded)} + > +
+ + {expanded ? : } + + {t('cot_title')} +
+
+ + {expanded && ( +
+
+ {content || ''} +
+
+ )} +
+ ); +} diff --git a/web/locales/en/chat.ts b/web/locales/en/chat.ts index 0461daf94..c6ae41062 100644 --- a/web/locales/en/chat.ts +++ b/web/locales/en/chat.ts @@ -19,4 +19,5 @@ export const ChatEn = { temperature_tip: 'The current application does not support temperature configuration', max_new_tokens_tip: 'The current application does not support max_new_tokens configuration', extend_tip: 'Extended configuration is not supported for the current application', + cot_title: 'Thoughts', }; diff --git a/web/locales/zh/chat.ts b/web/locales/zh/chat.ts index 2856fd737..08cb08bee 100644 --- a/web/locales/zh/chat.ts +++ b/web/locales/zh/chat.ts @@ -27,4 +27,5 @@ export const ChatZh: Resources['translation'] = { temperature_tip: '当前应用暂不支持温度配置', max_new_tokens_tip: '当前应用暂不支持max_new_tokens配置', extend_tip: '当前应用暂不支持拓展配置', + cot_title: '思考', } as const;