add plugin mode

pilot/commands/commands_load.py

@@ -1,29 +0,0 @@
-from typing import Optional
-
-from pilot.configs.config import Config
-from pilot.prompts.generator import PromptGenerator
-from pilot.prompts.prompt import build_default_prompt_generator
-
-
-class CommandsLoad:
-    """
-    Load Plugins Commands Info , help build system prompt!
-    """
-
-    def __init__(self) -> None:
-        self.command_registry = None
-
-    def getCommandInfos(
-        self, prompt_generator: Optional[PromptGenerator] = None
-    ) -> str:
-        cfg = Config()
-        if prompt_generator is None:
-            prompt_generator = build_default_prompt_generator()
-        for plugin in cfg.plugins:
-            if not plugin.can_handle_post_prompt():
-                continue
-            prompt_generator = plugin.post_prompt(prompt_generator)
-        self.prompt_generator = prompt_generator
-        command_infos = ""
-        command_infos += f"\n\n{prompt_generator.commands()}"
-        return command_infos

pilot/conversation.py

@@ -263,6 +263,14 @@ conv_qa_prompt_template = """ 基于以下已知的信息, 专业、简要的回
 # """
 default_conversation = conv_one_shot
 
+
+chat_mode_title = {
+    "sql_generate_diagnostics": get_lang_text("sql_analysis_and_diagnosis"),
+    "chat_use_plugin": get_lang_text("chat_use_plugin"),
+    "knowledge_qa": get_lang_text("knowledge_qa"),
+
+}
+
 conversation_sql_mode = {
     "auto_execute_ai_response": get_lang_text("sql_generate_mode_direct"),
     "dont_execute_ai_response": get_lang_text("sql_generate_mode_none"),
@@ -274,7 +282,7 @@ conversation_types = {
         "knowledge_qa_type_default_knowledge_base_dialogue"
     ),
     "custome": get_lang_text("knowledge_qa_type_add_knowledge_base_dialogue"),
-    "auto_execute_plugin": get_lang_text("dialogue_use_plugin"),
+    "url": get_lang_text("knowledge_qa_type_url_knowledge_dialogue"),
 }
 
 conv_templates = {

pilot/language/lang_content_mapping.py

@@ -14,17 +14,22 @@ lang_dicts = {
         "knowledge_qa_type_llm_native_dialogue": "LLM原生对话",
         "knowledge_qa_type_default_knowledge_base_dialogue": "默认知识库对话",
         "knowledge_qa_type_add_knowledge_base_dialogue": "新增知识库对话",
-        "dialogue_use_plugin": "对话使用插件",
+        "knowledge_qa_type_url_knowledge_dialogue": "URL网页知识对话",
         "create_knowledge_base": "新建知识库",
         "sql_schema_info": "数据库{}的Schema信息如下: {}\n",
         "current_dialogue_mode": "当前对话模式",
         "database_smart_assistant": "数据库智能助手",
         "sql_vs_setting": "自动执行模式下, DB-GPT可以具备执行SQL、从网络读取知识自动化存储学习的能力",
         "knowledge_qa": "知识问答",
+        "chat_use_plugin": "插件模式",
+        "dialogue_use_plugin": "对话使用插件",
+        "select_plugin": "选择插件",
         "configure_knowledge_base": "配置知识库",
         "new_klg_name": "新知识库名称",
+        "url_input_label": "输入网页地址",
         "add_as_new_klg": "添加为新知识库",
         "add_file_to_klg": "向知识库中添加文件",
+
         "upload_file": "上传文件",
         "add_file": "添加文件",
         "upload_and_load_to_klg": "上传并加载到知识库",
@@ -47,14 +52,18 @@ lang_dicts = {
         "knowledge_qa_type_llm_native_dialogue": "LLM native dialogue",
         "knowledge_qa_type_default_knowledge_base_dialogue": "Default documents",
         "knowledge_qa_type_add_knowledge_base_dialogue": "Added documents",
+        "knowledge_qa_type_url_knowledge_dialogue": "Chat with url",
         "dialogue_use_plugin": "Dialogue Extension",
         "create_knowledge_base": "Create Knowledge Base",
         "sql_schema_info": "the schema information of database {}: {}\n",
         "current_dialogue_mode": "Current dialogue mode",
         "database_smart_assistant": "Database smart assistant",
         "sql_vs_setting": "In the automatic execution mode, DB-GPT can have the ability to execute SQL, read data from the network, automatically store and learn",
+        "chat_use_plugin": "Plugin Mode",
+        "select_plugin": "Select Plugin",
         "knowledge_qa": "Documents QA",
         "configure_knowledge_base": "Configure Documents",
+        "url_input_label": "Please input url",
         "new_klg_name": "New document name",
         "add_as_new_klg": "Add as new documents",
         "add_file_to_klg": "Add file to documents",

pilot/out_parser/base.py

@@ -18,11 +18,14 @@ import re
 
 from pydantic import BaseModel, Extra, Field, root_validator
 from pilot.configs.model_config import LOGDIR
-from pilot.prompts.base import PromptValue
+from pilot.configs.config import Config
 
 T = TypeVar("T")
 logger = build_logger("webserver", LOGDIR + "DbChatOutputParser.log")
 
+CFG = Config()
+
+
 class BaseOutputParser(ABC):
     """Class to parse the output of an LLM call.
 
@@ -33,9 +36,39 @@ class BaseOutputParser(ABC):
         self.sep = sep
         self.is_stream_out = is_stream_out
 
+    def __post_process_code(code):
+        sep = "\n```"
+        if sep in code:
+            blocks = code.split(sep)
+            if len(blocks) % 2 == 1:
+                for i in range(1, len(blocks), 2):
+                    blocks[i] = blocks[i].replace("\\_", "_")
+            code = sep.join(blocks)
+        return code
+
     # TODO 后续和模型绑定
     def _parse_model_stream_resp(self, response, sep: str):
-        pass
+
+        for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
+            if chunk:
+                data = json.loads(chunk.decode())
+
+                """ TODO Multi mode output handler,  rewrite this for multi model, use adapter mode.
+                """
+                if data["error_code"] == 0:
+                    if "vicuna" in CFG.LLM_MODEL:
+
+                        output = data["text"].strip()
+                    else:
+                        output = data["text"].strip()
+
+                    output = self.__post_process_code(output)
+                    yield output
+                else:
+                    output = (
+                            data["text"] + f" (error_code: {data['error_code']})"
+                    )
+                    yield output
 
     def _parse_model_nostream_resp(self, response, sep: str):
         text = response.text.strip()
@@ -64,7 +97,7 @@ class BaseOutputParser(ABC):
         else:
             raise ValueError("Model server error!code=" + respObj_ex["error_code"])
 
-    def parse_model_server_out(self, response) -> str:
+    def parse_model_server_out(self, response):
         """
         parse the model server http response
         Args:

pilot/scene/base_chat.py

@@ -1,6 +1,7 @@
 from abc import ABC, abstractmethod
 import datetime
 import traceback
+import json
 from pydantic import BaseModel, Field, root_validator, validator, Extra
 from typing import (
     Any,
@@ -41,6 +42,7 @@ headers = {"User-Agent": "dbgpt Client"}
 CFG = Config()
 
 
+
 class BaseChat(ABC):
     chat_scene: str = None
     llm_model: Any = None
@@ -89,8 +91,7 @@ class BaseChat(ABC):
     def do_with_prompt_response(self, prompt_response):
         pass
 
-
+    def call(self,  show_fn, state):
-    def call(self):
         input_values = self.generate_input_values()
 
         ### Chat sequence advance
@@ -164,6 +165,7 @@ class BaseChat(ABC):
                             prompt_define_response, result
                         )
                     )
+                show_fn(state, self.current_ai_response())
             else:
                 response = requests.post(
                     urljoin(CFG.MODEL_SERVER, "generate_stream"),
@@ -171,9 +173,14 @@ class BaseChat(ABC):
                     json=payload,
                     timeout=120,
                 )
-                #TODO
+                show_fn(state, "▌")
-
+                ai_response_text =  self.prompt_template.output_parser.parse_model_server_out(response)
+                show_info  =""
+                for resp_text_trunck in ai_response_text:
+                    show_info = resp_text_trunck
+                    show_fn(state, resp_text_trunck + "▌")
 
+                self.current_message.add_ai_message(show_info)
 
         except Exception as e:
             print(traceback.format_exc())
@@ -181,9 +188,11 @@ class BaseChat(ABC):
             self.current_message.add_view_message(
                 f"""<span style=\"color:red\">ERROR!</span>{str(e)}\n  {ai_response_text} """
             )
+            show_fn(state, self.current_ai_response())
         ### 对话记录存储
         self.memory.append(self.current_message)
 
+
     def generate_llm_text(self) -> str:
         text = self.prompt_template.template_define + self.prompt_template.sep
         ### 线处理历史信息
@@ -229,8 +238,6 @@ class BaseChat(ABC):
         return text
 
 
-    def chat_show(self):
-        pass
 
     # 暂时为了兼容前端
     def current_ai_response(self) -> str:

pilot/server/webserver.py

@@ -37,6 +37,7 @@ from pilot.conversation import (
     conv_templates,
     conversation_sql_mode,
     conversation_types,
+    chat_mode_title,
     default_conversation,
 )
 from pilot.common.plugins import scan_plugins
@@ -95,6 +96,11 @@ default_knowledge_base_dialogue = get_lang_text(
 add_knowledge_base_dialogue = get_lang_text(
     "knowledge_qa_type_add_knowledge_base_dialogue"
 )
+
+url_knowledge_dialogue = get_lang_text(
+    "knowledge_qa_type_url_knowledge_dialogue"
+)
+
 knowledge_qa_type_list = [
     llm_native_dialogue,
     default_knowledge_base_dialogue,
@@ -115,7 +121,7 @@ def gen_sqlgen_conversation(dbname):
     db_connect = CFG.local_db.get_session(dbname)
     schemas = CFG.local_db.table_simple_info(db_connect)
     for s in schemas:
-        message += s["schema_info"] + ";"
+        message += s+ ";"
     return get_lang_text("sql_schema_info").format(dbname, message)
 
 
@@ -211,9 +217,9 @@ def post_process_code(code):
 
 
 def get_chat_mode(selected, mode, sql_mode, db_selector) -> ChatScene:
-    if "插件模式" == selected:
+    if chat_mode_title['chat_use_plugin'] == selected:
         return ChatScene.ChatExecution
-    elif "知识问答" == selected:
+    elif chat_mode_title['knowledge_qa']  == selected:
         if mode == conversation_types["default_knownledge"]:
             return ChatScene.ChatKnowledge
         elif mode == conversation_types["custome"]:
@@ -226,37 +232,50 @@ def get_chat_mode(selected, mode, sql_mode, db_selector) -> ChatScene:
 
 
 def http_bot(
-        state, selected, plugin_selector, mode, sql_mode, db_selector, temperature, max_new_tokens, request: gr.Request
+        state, selected, plugin_selector, mode, sql_mode, db_selector, url_input, temperature, max_new_tokens, request: gr.Request
 ):
     logger.info(f"User message send!{state.conv_id},{selected},{mode},{sql_mode},{db_selector},{plugin_selector}")
     start_tstamp = time.time()
-    scene:ChatScene = get_chat_mode(mode, sql_mode, db_selector)
+    scene:ChatScene = get_chat_mode(selected, mode, sql_mode, db_selector)
-    print(f"当前对话模式:{scene.value}")
+    print(f"now chat scene:{scene.value}")
     model_name = CFG.LLM_MODEL
 
+    def chatbot_callback(state, message):
+        print(f"chatbot_callback:{message}")
+        state.messages[-1][-1] = f"{message}"
+        yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
+
     if ChatScene.ChatWithDb == scene:
-        logger.info("基于DB对话走新的模式！")
+        logger.info("chat with db mode use new architecture design！")
         chat_param = {
             "chat_session_id": state.conv_id,
             "db_name": db_selector,
             "user_input": state.last_user_input,
         }
         chat: BaseChat = CHAT_FACTORY.get_implementation(scene.value, **chat_param)
-        chat.call()
+        chat.call(show_fn=chatbot_callback, state= state)
-        state.messages[-1][-1] = f"{chat.current_ai_response()}"
+
-        yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
     elif ChatScene.ChatExecution == scene:
-        logger.info("插件模式对话走新的模式！")
+        logger.info("plugin mode use new architecture design！")
         chat_param = {
             "chat_session_id": state.conv_id,
             "plugin_selector": plugin_selector,
             "user_input": state.last_user_input,
         }
         chat: BaseChat = CHAT_FACTORY.get_implementation(scene.value, **chat_param)
-        chat.call()
+        chat.call(chatbot_callback, state)
-        state.messages[-1][-1] = f"{chat.current_ai_response()}"
-        yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
 
+        # def generate_numbers():
+        #     for i in range(10):
+        #         time.sleep(0.5)
+        #         yield f"Message:{i}"
+        #
+        # def showMessage(message):
+        #      return message
+        #
+        # for n in generate_numbers():
+        #     state.messages[-1][-1] = n + "▌"
+        #     yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
     else:
 
         dbname = db_selector
@@ -284,30 +303,45 @@ def http_bot(
 
             new_state.conv_id = uuid.uuid4().hex
             state = new_state
+        else:
+            ### 后续对话
+            query = state.messages[-2][1]
+            # 第一轮对话需要加入提示Prompt
+            if mode == conversation_types["custome"]:
+                template_name = "conv_one_shot"
+                new_state = conv_templates[template_name].copy()
+                # prompt 中添加上下文提示, 根据已有知识对话, 上下文提示是否也应该放在第一轮, 还是每一轮都添加上下文?
+                # 如果用户侧的问题跨度很大, 应该每一轮都加提示。
+                if db_selector:
+                    new_state.append_message(
+                        new_state.roles[0], gen_sqlgen_conversation(dbname) + query
+                    )
+                    new_state.append_message(new_state.roles[1], None)
+                else:
+                    new_state.append_message(new_state.roles[0], query)
+                    new_state.append_message(new_state.roles[1], None)
+                state = new_state
 
         prompt = state.get_prompt()
         skip_echo_len = len(prompt.replace("</s>", " ")) + 1
         if mode == conversation_types["default_knownledge"] and not db_selector:
+            vector_store_config = {
+                "vector_store_name": "default",
+                "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,
+            }
+            knowledge_embedding_client = KnowledgeEmbedding(
+                file_path="",
+                model_name=LLM_MODEL_CONFIG["text2vec"],
+                local_persist=False,
+                vector_store_config=vector_store_config,
+            )
             query = state.messages[-2][1]
-            knqa = KnownLedgeBaseQA()
+            docs = knowledge_embedding_client.similar_search(query, VECTOR_SEARCH_TOP_K)
-            state.messages[-2][1] = knqa.get_similar_answer(query)
+            prompt = KnownLedgeBaseQA.build_knowledge_prompt(query, docs, state)
-            prompt = state.get_prompt()
             state.messages[-2][1] = query
             skip_echo_len = len(prompt.replace("</s>", " ")) + 1
 
         if mode == conversation_types["custome"] and not db_selector:
-            persist_dir = os.path.join(
-                KNOWLEDGE_UPLOAD_ROOT_PATH, vector_store_name["vs_name"] + ".vectordb"
-            )
-            print("向量数据库持久化地址: ", persist_dir)
-            knowledge_embedding_client = KnowledgeEmbedding(
-                file_path="",
-                model_name=LLM_MODEL_CONFIG["sentence-transforms"],
-                vector_store_config={
-                    "vector_store_name": vector_store_name["vs_name"],
-                    "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,
-                },
-            )
             print("vector store name: ", vector_store_name["vs_name"])
             vector_store_config = {
                 "vector_store_name": vector_store_name["vs_name"],
@@ -327,6 +361,27 @@ def http_bot(
             state.messages[-2][1] = query
             skip_echo_len = len(prompt.replace("</s>", " ")) + 1
 
+        if mode == conversation_types["url"] and  url_input:
+            print("url: ", url_input)
+            vector_store_config = {
+                "vector_store_name": url_input,
+                "text_field": "content",
+                "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,
+            }
+            knowledge_embedding_client = KnowledgeEmbedding(
+                file_path=url_input,
+                model_name=LLM_MODEL_CONFIG["text2vec"],
+                local_persist=False,
+                vector_store_config=vector_store_config,
+            )
+
+            query = state.messages[-2][1]
+            docs = knowledge_embedding_client.similar_search(query, VECTOR_SEARCH_TOP_K)
+            prompt = KnownLedgeBaseQA.build_knowledge_prompt(query, docs, state)
+
+            state.messages[-2][1] = query
+            skip_echo_len = len(prompt.replace("</s>", " ")) + 1
+
         # Make requests
         payload = {
             "model": model_name,
@@ -343,34 +398,6 @@ def http_bot(
         state.messages[-1][-1] = "▌"
         yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
 
-        try:
-            # Stream output
-            response = requests.post(
-                urljoin(CFG.MODEL_SERVER, "generate_stream"),
-                headers=headers,
-                json=payload,
-                stream=True,
-                timeout=20,
-            )
-            for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
-                if chunk:
-                    data = json.loads(chunk.decode())
-                    if data["error_code"] == 0:
-                        output = data["text"][skip_echo_len:].strip()
-                        output = post_process_code(output)
-                        state.messages[-1][-1] = output + "▌"
-                        yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
-                    else:
-                        output = data["text"] + f" (error_code: {data['error_code']})"
-                        state.messages[-1][-1] = output
-                        yield (state, state.to_gradio_chatbot()) + (
-                            disable_btn,
-                            disable_btn,
-                            disable_btn,
-                            enable_btn,
-                            enable_btn,
-                        )
-                        return
         try:
             # Stream output
             response = requests.post(
@@ -421,16 +448,6 @@ def http_bot(
                 enable_btn,
             )
             return
-        except requests.exceptions.RequestException as e:
-            state.messages[-1][-1] = server_error_msg + f" (error_code: 4)"
-            yield (state, state.to_gradio_chatbot()) + (
-                disable_btn,
-                disable_btn,
-                disable_btn,
-                enable_btn,
-                enable_btn,
-            )
-            return
 
         state.messages[-1][-1] = state.messages[-1][-1][:-1]
         yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
@@ -477,15 +494,12 @@ def change_sql_mode(sql_mode):
 
 
 def change_mode(mode):
-    if mode in [default_knowledge_base_dialogue, llm_native_dialogue]:
+    if mode in [add_knowledge_base_dialogue]:
-        return gr.update(visible=False)
-    else:
         return gr.update(visible=True)
+    else:
+        return gr.update(visible=False)
 
 
-def change_tab():
-    autogpt = True
-
 
 def build_single_model_ui():
     notice_markdown = get_lang_text("db_gpt_introduction")
@@ -548,15 +562,14 @@ def build_single_model_ui():
             sql_vs_setting = gr.Markdown(get_lang_text("sql_vs_setting"))
             sql_mode.change(fn=change_sql_mode, inputs=sql_mode, outputs=sql_vs_setting)
 
-        tab_qa = gr.TabItem(get_lang_text("knowledge_qa"), elem_id="QA")
+        tab_plugin = gr.TabItem(get_lang_text("chat_use_plugin"), elem_id="PLUGIN")
-        tab_plugin = gr.TabItem("插件模式", elem_id="PLUGIN")
         # tab_plugin.select(change_func)
         with tab_plugin:
             print("tab_plugin in...")
             with gr.Row(elem_id="plugin_selector"):
                 # TODO
                 plugin_selector = gr.Dropdown(
-                    label="请选择插件",
+                    label=get_lang_text("select_plugin"),
                     choices=list(plugins_select_info().keys()),
                     value="",
                     interactive=True,
@@ -578,6 +591,7 @@ def build_single_model_ui():
                     llm_native_dialogue,
                     default_knowledge_base_dialogue,
                     add_knowledge_base_dialogue,
+                    url_knowledge_dialogue,
                 ],
                 show_label=False,
                 value=llm_native_dialogue,
@@ -586,6 +600,16 @@ def build_single_model_ui():
                 get_lang_text("configure_knowledge_base"), open=False
             )
             mode.change(fn=change_mode, inputs=mode, outputs=vs_setting)
+
+            url_input = gr.Textbox(label=get_lang_text("url_input_label"), lines=1, interactive=True)
+            def show_url_input(evt:gr.SelectData):
+                if evt.value == url_knowledge_dialogue:
+                    return gr.update(visible=True)
+                else:
+                    return gr.update(visible=False)
+            mode.select(fn=show_url_input, inputs=None, outputs=url_input)
+
+
             with vs_setting:
                 vs_name = gr.Textbox(
                     label=get_lang_text("new_klg_name"), lines=1, interactive=True
@@ -636,7 +660,7 @@ def build_single_model_ui():
     btn_list = [regenerate_btn, clear_btn]
     regenerate_btn.click(regenerate, state, [state, chatbot, textbox] + btn_list).then(
         http_bot,
-        [state, selected, plugin_selected, mode, sql_mode, db_selector, temperature, max_output_tokens],
+        [state, selected, plugin_selected, mode, sql_mode, db_selector, url_input, temperature, max_output_tokens],
         [state, chatbot] + btn_list,
     )
     clear_btn.click(clear_history, None, [state, chatbot, textbox] + btn_list)
@@ -645,7 +669,7 @@ def build_single_model_ui():
         add_text, [state, textbox], [state, chatbot, textbox] + btn_list
     ).then(
         http_bot,
-        [state, selected, plugin_selected, mode, sql_mode, db_selector, temperature, max_output_tokens],
+        [state, selected, plugin_selected, mode, sql_mode, db_selector, url_input, temperature, max_output_tokens],
         [state, chatbot] + btn_list,
     )
 
@@ -653,7 +677,7 @@ def build_single_model_ui():
         add_text, [state, textbox], [state, chatbot, textbox] + btn_list
     ).then(
         http_bot,
-        [state, selected, plugin_selected, mode, sql_mode, db_selector, temperature, max_output_tokens],
+        [state, selected, plugin_selected, mode, sql_mode, db_selector, url_input,  temperature, max_output_tokens],
         [state, chatbot] + btn_list,
     )
     vs_add.click(
@@ -760,8 +784,8 @@ if __name__ == "__main__":
 
     # 加载插件可执行命令
     command_categories = [
-        "pilot.commands.audio_text",
+        "pilot.commands.built_in.audio_text",
-        "pilot.commands.image_gen",
+        "pilot.commands.built_in.image_gen",
     ]
     # 排除禁用命令
     command_categories = [

pilot/source_embedding/knowledge_embedding.py

@@ -11,6 +11,7 @@ from pilot.source_embedding.chn_document_splitter import CHNDocumentSplitter
 from pilot.source_embedding.csv_embedding import CSVEmbedding
 from pilot.source_embedding.markdown_embedding import MarkdownEmbedding
 from pilot.source_embedding.pdf_embedding import PDFEmbedding
+from pilot.source_embedding.url_embedding import URLEmbedding
 from pilot.vector_store.connector import VectorStoreConnector
 
 CFG = Config()
@@ -61,6 +62,12 @@ class KnowledgeEmbedding:
                 model_name=self.model_name,
                 vector_store_config=self.vector_store_config,
             )
+        elif self.file_type == "url":
+            embedding = URLEmbedding(
+                file_path=self.file_path,
+                model_name=self.model_name,
+                vector_store_config=self.vector_store_config,
+            )
 
         return embedding