Merge Plugin

README.md

@@ -134,6 +134,7 @@ V100   | 16G      |可以进行对话推理，有明显卡顿
 docker run --name=mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=aa12345678 -dit mysql:latest
 ```
 向量数据库我们默认使用的是Chroma内存数据库，所以无需特殊安装，如果有需要连接其他的同学，可以按照我们的教程进行安装配置。整个DB-GPT的安装过程，我们使用的是miniconda3的虚拟环境。创建虚拟环境，并安装python依赖包
+
 ```
 python>=3.10
 conda create -n dbgpt_env python=3.10

pilot/commands/command.py

@@ -69,7 +69,7 @@ def execute_ai_response_json(
             arguments,
             prompt,
         )
-        result = f"Command {command_name} returned: " f"{command_result}"
+        result = f"{command_result}"
     return result
 
 

pilot/prompts/auto_mode_prompt.py

@@ -7,7 +7,7 @@ from pathlib import Path
 import distro
 import yaml
 from pilot.configs.config import Config
-from pilot.prompts.prompt import build_default_prompt_generator, DEFAULT_PROMPT_OHTER
+from pilot.prompts.prompt import build_default_prompt_generator, DEFAULT_PROMPT_OHTER, DEFAULT_TRIGGERING_PROMPT
 
 
 class AutoModePrompt:

pilot/server/webserver.py

@@ -62,14 +62,15 @@ priority = {
     "vicuna-13b": "aaa"
 }
 
-def get_simlar(q):
 
+def get_simlar(q):
     docsearch = knownledge_tovec_st(os.path.join(DATASETS_DIR, "plan.md"))
     docs = docsearch.similarity_search_with_score(q, k=1)
 
     contents = [dc.page_content for dc, _ in docs]
     return "\n".join(contents)
 
+
 def gen_sqlgen_conversation(dbname):
     mo = MySQLOperator(
         **DB_SETTINGS
@@ -82,10 +83,12 @@ def gen_sqlgen_conversation(dbname):
         message += s["schema_info"] + ";"
     return f"数据库{dbname}的Schema信息如下: {message}\n"
 
+
 def get_database_list():
     mo = MySQLOperator(**DB_SETTINGS)
     return mo.get_db_list()
 
+
 get_window_url_params = """
 function() {
     const params = new URLSearchParams(window.location.search);
@@ -98,6 +101,8 @@ function() {
     return url_params;
     }
 """
+
+
 def load_demo(url_params, request: gr.Request):
     logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")
 
@@ -115,6 +120,7 @@ def load_demo(url_params, request: gr.Request):
             gr.Row.update(visible=True),
             gr.Accordion.update(visible=True))
 
+
 def get_conv_log_filename():
     t = datetime.datetime.now()
     name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
@@ -127,9 +133,8 @@ def regenerate(state, request: gr.Request):
     state.skip_next = False
     return (state, state.to_gradio_chatbot(), "") + (disable_btn,) * 5
 
+
 def clear_history(request: gr.Request):
-
-    
     logger.info(f"clear_history. ip: {request.client.host}")
     state = None
     return (state, [], "") + (disable_btn,) * 5
@@ -148,6 +153,7 @@ def add_text(state, text, request: gr.Request):
     state.skip_next = False
     return (state, state.to_gradio_chatbot(), "") + (disable_btn,) * 5
 
+
 def post_process_code(code):
     sep = "\n```"
     if sep in code:
@@ -158,6 +164,7 @@ def post_process_code(code):
         code = sep.join(blocks)
     return code
 
+
 def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, request: gr.Request):
     if sql_mode == conversation_sql_mode["auto_execute_ai_response"]:
         print("AUTO DB-GPT模式.")
@@ -185,7 +192,8 @@ def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, re
         # 第一轮对话需要加入提示Prompt
         if sql_mode == conversation_sql_mode["auto_execute_ai_response"]:
             # autogpt模式的第一轮对话需要 构建专属prompt
-            system_prompt = auto_prompt.construct_first_prompt(fisrt_message=[query], db_schemes= gen_sqlgen_conversation(dbname))
+            system_prompt = auto_prompt.construct_first_prompt(fisrt_message=[query],
+                                                               db_schemes=gen_sqlgen_conversation(dbname))
             logger.info("[TEST]:" + system_prompt)
             template_name = "auto_dbgpt_one_shot"
             new_state = conv_templates[template_name].copy()
@@ -217,13 +225,11 @@ def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, re
             state.messages[0][1] = ""
             state.messages[-2][1] = follow_up_prompt
 
-
     if mode == conversation_types["default_knownledge"] and not db_selector:
         query = state.messages[-2][1]
         knqa = KnownLedgeBaseQA()
         state.messages[-2][1] = knqa.get_similar_answer(query)
 
-
     prompt = state.get_prompt()
 
     skip_echo_len = len(prompt.replace("</s>", " ")) + 1
@@ -245,11 +251,6 @@ def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, re
         print(response.json())
         print(str(response))
         try:
-            # response = """{"thoughts":{"text":"thought","reasoning":"reasoning","plan":"- short bulleted\n- list that conveys\n- long-term plan","criticism":"constructive self-criticism","speak":"thoughts summary to say to user"},"command":{"name":"db_sql_executor","args":{"sql":"select count(*) as user_count from users u  where create_time >= DATE_SUB(NOW(), INTERVAL 1 MONTH);"}}}"""
-            # response = response.replace("\n", "\\n")
-
-            # response = """{"thoughts":{"text":"In order to get the number of users who have grown in the last three days, I need to analyze the create\_time of each user and see if it is within the last three days. I will use the SQL query to filter the users who have created their account in the last three days.","reasoning":"I can use the SQL query to filter the users who have created their account in the last three days. I will get the current date and then subtract three days from it, and then use this as the filter for the query. This will give me the number of users who have created their account in the last three days.","plan":"- Get the current date and subtract three days from it\n- Use the SQL query to filter the users who have created their account in the last three days\n- Count the number of users who match the filter to get the number of users who have grown in the last three days","criticism":"None"},"command":{"name":"db_sql_executor","args":{"sql":"SELECT COUNT(DISTINCT(ID)) FROM users WHERE create_time >= DATE_SUB(NOW(), INTERVAL 3 DAY);"}}}"""
-            # response = response.replace("\n", "\\)
             text = response.text.strip()
             text = text.rstrip()
             respObj = json.loads(text)
@@ -279,11 +280,11 @@ def http_bot(state, mode, sql_mode, db_selector, temperature, max_new_tokens, re
                     plugin_resp = execute_ai_response_json(auto_prompt.prompt_generator, ai_response)
                     cfg.set_last_plugin_return(plugin_resp)
                     print(plugin_resp)
-                    state.messages[-1][-1] = "Model推理信息:\n"+ ai_response +"\n\nDB-GPT执行结果:\n" + plugin_resp
+                    state.messages[-1][-1] = "Model推理信息:\n" + ai_response + "\n\nDB-GPT执行结果:\n" + plugin_resp
                     yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
         except NotCommands as e:
             print("命令执行:" + e.message)
-            state.messages[-1][-1] = "命令执行:" + e.message +"\n模型输出:\n" + str(ai_response)
+            state.messages[-1][-1] = "命令执行:" + e.message + "\n模型输出:\n" + str(ai_response)
             yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
     else:
         # 流式输出
@@ -350,23 +351,26 @@ block_css = (
             """
 )
 
+
 def change_sql_mode(sql_mode):
     if sql_mode in ["直接执行结果"]:
         return gr.update(visible=True)
     else:
         return gr.update(visible=False)
 
+
 def change_mode(mode):
     if mode in ["默认知识库对话", "LLM原生对话"]:
         return gr.update(visible=False)
     else:
         return gr.update(visible=True)
 
+
 def change_tab():
     autogpt = True
 
-def build_single_model_ui():
 
+def build_single_model_ui():
     notice_markdown = """
     # DB-GPT
     
@@ -398,7 +402,7 @@ def build_single_model_ui():
             interactive=True,
             label="最大输出Token数",
         )
-    tabs= gr.Tabs()
+    tabs = gr.Tabs()
     with tabs:
         tab_sql = gr.TabItem("SQL生成与诊断", elem_id="SQL")
         with tab_sql:
@@ -414,9 +418,6 @@ def build_single_model_ui():
             sql_mode = gr.Radio(["直接执行结果", "不执行结果"], show_label=False, value="不执行结果")
             sql_vs_setting = gr.Markdown("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")
             sql_mode.change(fn=change_sql_mode, inputs=sql_mode, outputs=sql_vs_setting)
-        tab_auto = gr.TabItem("AUTO-GPT", elem_id="auto")
-        with tab_auto:
-            gr.Markdown("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")
 
         tab_qa = gr.TabItem("知识问答", elem_id="QA")
         with tab_qa:
@@ -442,7 +443,6 @@ def build_single_model_ui():
                                                show_label=False)
                         load_folder_button = gr.Button("上传并加载到知识库")
 
-    
     with gr.Blocks():
         chatbot = grChatbot(elem_id="chatbot", visible=False).style(height=550)
         with gr.Row():
@@ -545,7 +545,6 @@ def knowledge_embedding_store(vs_id, files):
     logger.info("knowledge embedding success")
     return os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, vs_id + ".vectordb")
 
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="0.0.0.0")
@@ -579,7 +578,7 @@ if __name__ == "__main__":
     for command_category in command_categories:
         command_registry.import_commands(command_category)
 
-    cfg.command_registry =command_registry
+    cfg.command_registry = command_registry
 
     logger.info(args)
     demo = build_webdemo()