支持插件的prompt

pilot/configs/config.py

@@ -46,6 +46,8 @@ class Config(metaclass=Singleton):
         self.plugins: List[AutoGPTPluginTemplate] = []
         self.plugins_openai = []
 
+        self.command_registry = []
+
         self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN")
         self.image_provider = os.getenv("IMAGE_PROVIDER")
         self.image_size = int(os.getenv("IMAGE_SIZE", 256))

pilot/configs/model_config.py

@@ -36,8 +36,8 @@ ISDEBUG = False
 
 
 DB_SETTINGS = {
-    "user": "puzzle@puzzle0_1954#dev_ipay10",
+    "user": "root",
-    "password": "puzzle123",
+    "password": "root",
-    "host": "obproxy.ocp2.alipay.net",
+    "host": "127.0.0.1",
-    "port": 2883
+    "port": 3306
 }

pilot/conversation.py

@@ -48,7 +48,6 @@ class Conversation:
                 else:
                     ret += role + ":"
             return ret
-
         else:
             raise ValueError(f"Invalid style: {self.sep_style}")
 
@@ -104,12 +103,12 @@ def gen_sqlgen_conversation(dbname):
 
 
 conv_one_shot = Conversation(
-    system="A chat between a curious human and an artificial intelligence assistant, who very familiar with database related knowledge. "
+    system="A chat between a curious user and an artificial intelligence assistant, who very familiar with database related knowledge. "
-           "The assistant gives helpful, detailed, professional and polite answers to the human's questions. ",
+           "The assistant gives helpful, detailed, professional and polite answers to the user's questions. ",
-    roles=("Human", "Assistant"),
+    roles=("USER", "Assistant"),
     messages=(
         (
-            "Human",
+            "USER",
             "What are the key differences between mysql and postgres?",
         ),
         (
@@ -155,12 +154,11 @@ auto_dbgpt_one_shot = Conversation(
     messages=(
         (
             "USER",
-            """ Answer how many users does hackernews have by query mysql database
+            """ Answer how many users does app_users have by query ob database
               Constraints:
-              1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.
+              1. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
-              2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.
+              2. No user assistance
-              3. No user assistance
+              3. Exclusively use the commands listed in double quotes e.g. "command name"
-              4. Exclusively use the commands listed in double quotes e.g. "command name"
   
               Commands:
               1. analyze_code: Analyze Code, args: "code": "<full_code_string>"
@@ -170,13 +168,12 @@ auto_dbgpt_one_shot = Conversation(
               5. list_files: List Files in Directory, args: "directory": "<directory>"
               6. read_file: Read file, args: "filename": "<filename>"
               7. write_to_file: Write to file, args: "filename": "<filename>", "text": "<text>"
-              8. tidb_sql_executor: "Execute SQL in TiDB Database.", args: "sql": "<sql>"
+              8. ob_sql_executor: "Execute SQL in OB Database.", args: "sql": "<sql>"
   
               Resources:
               1. Internet access for searches and information gathering.
               2. Long Term memory management.
               3. vicuna powered Agents for delegation of simple tasks.
-              4. File output.
   
               Performance Evaluation:
               1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.
@@ -210,16 +207,16 @@ auto_dbgpt_one_shot = Conversation(
             """
             {
                 "thoughts": {
-                    "text": "thought",
+                    "text": "To answer how many users  by query  database we need to write SQL query to get the count of the distinct users from the database. We can use ob_sql_executor command to execute the SQL query in  database.",
-                    "reasoning": "reasoning",
+                    "reasoning": "We can use the sql_executor command to execute the SQL query for getting count of distinct users from the users database. We can select the count of the distinct users from the users table.",
-                    "plan": "- short bulleted\n- list that conveys\n- long-term plan",
+                    "plan": "- Write SQL query to get count of distinct users from users database\n- Use ob_sql_executor to execute the SQL query in OB database\n- Parse the SQL result to get the count\n- Respond with the count as the answer",
-                    "criticism": "constructive self-criticism",
+                    "criticism": "None",
-                    "speak": "thoughts summary to say to user"
+                    "speak": "To get the number of users in users, I will execute an SQL query in OB database using the ob_sql_executor command and respond with the count."
                 },
                 "command": {
-                    "name": "command name",
+                    "name": "ob_sql_executor",
                     "args": {
-                        "arg name": "value"
+                        "sql": "SELECT COUNT(DISTINCT(*)) FROM users ;"
                     }
                 }
             } 
@@ -227,18 +224,18 @@ auto_dbgpt_one_shot = Conversation(
         )
     ),
     offset=0,
-    sep_style=SeparatorStyle.THREE,
+    sep_style=SeparatorStyle.SINGLE,
     sep=" ",
     sep2="</s>",
 )
 
 auto_dbgpt_without_shot = Conversation(
-    system="You are DB-GPT, an AI designed to answer questions about HackerNews by query `hackerbews` database in MySQL. "
+    system="You are DB-GPT, an AI designed to answer questions about users by query `users` database in MySQL. "
            "Your decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.",
     roles=("USER", "ASSISTANT"),
     messages=(),
     offset=0,
-    sep_style=SeparatorStyle.FOUR,
+    sep_style=SeparatorStyle.SINGLE,
     sep=" ",
     sep2="</s>",
 )
@@ -262,6 +259,7 @@ conversation_types = {
 conv_templates = {
     "conv_one_shot": conv_one_shot,
     "vicuna_v1": conv_vicuna_v1,
+    "auto_dbgpt_one_shot": auto_dbgpt_one_shot
 }
 
 if __name__ == "__main__":

pilot/prompts/first_conversation_prompt.py

@@ -66,7 +66,7 @@ class FirstPrompt:
         prompt_generator.goals = fisrt_message
         prompt_generator.command_registry = command_registry
         # 加载插件中可用命令
-        cfg = Config
+        cfg = Config()
         for plugin in cfg.plugins:
             if not plugin.can_handle_post_prompt():
                 continue

pilot/prompts/prompt.py

@@ -44,7 +44,7 @@ def build_default_prompt_generator() -> PromptGenerator:
     prompt_generator.add_resource(
         "GPT-3.5 powered Agents for delegation of simple tasks."
     )
-    prompt_generator.add_resource("File output.")
+    # prompt_generator.add_resource("File output.")
 
     # Add performance evaluations to the PromptGenerator object
     prompt_generator.add_performance_evaluation(
@@ -61,5 +61,5 @@ def build_default_prompt_generator() -> PromptGenerator:
         "Every command has a cost, so be smart and efficient. Aim to complete tasks in"
         " the least number of steps."
     )
-    prompt_generator.add_performance_evaluation("Write all code to a file.")
+    # prompt_generator.add_performance_evaluation("Write all code to a file.")
     return prompt_generator

pilot/server/webserver.py

@@ -161,6 +161,8 @@ def post_process_code(code):
 
 def http_bot(state, mode, db_selector, temperature, max_new_tokens, request: gr.Request):
 
+    # MOCk
+    autogpt = True
     print("是否是AUTO-GPT模式.", autogpt)
     start_tstamp = time.time()
     model_name = LLM_MODEL
@@ -175,25 +177,37 @@ def http_bot(state, mode, db_selector, temperature, max_new_tokens, request: gr.
 
     # TODO when tab mode is AUTO_GPT, Prompt need to rebuild.
     if len(state.messages) == state.offset + 2:
-        # 第一轮对话需要加入提示Prompt 
-        
-        template_name = "conv_one_shot"
-        new_state = conv_templates[template_name].copy()
-        new_state.conv_id = uuid.uuid4().hex
-        
         query = state.messages[-2][1]
+        # 第一轮对话需要加入提示Prompt
+        if(autogpt):
+            # autogpt模式的第一轮对话需要 构建专属prompt
+            cfg = Config()
+            first_prompt = FirstPrompt()
+            first_prompt.command_registry = cfg.command_registry
 
-        # 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? 
+            system_prompt = first_prompt.construct_first_prompt(fisrt_message=[query])
-        # 如果用户侧的问题跨度很大, 应该每一轮都加提示。
+            logger.info("[TEST]:" + system_prompt)
-        if db_selector:
+            template_name = "auto_dbgpt_one_shot"
-            new_state.append_message(new_state.roles[0], gen_sqlgen_conversation(dbname) + query)
+            new_state = conv_templates[template_name].copy()
-            new_state.append_message(new_state.roles[1], None)
+            new_state.append_message(role='USER', message=system_prompt)
-            state = new_state
         else:
-            new_state.append_message(new_state.roles[0], query)
+            template_name = "conv_one_shot"
-            new_state.append_message(new_state.roles[1], None)
+            new_state = conv_templates[template_name].copy()
-            state = new_state
 
+        new_state.conv_id = uuid.uuid4().hex
+
+        if not autogpt:
+            # 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)
+                state = new_state
+            else:
+                new_state.append_message(new_state.roles[0], query)
+                new_state.append_message(new_state.roles[1], None)
+
+        state = new_state
     if mode == conversation_types["default_knownledge"] and not db_selector:
         query = state.messages[-2][1]
         knqa = KnownLedgeBaseQA()
@@ -457,8 +471,6 @@ if __name__ == "__main__":
     # 加载插件
     cfg = Config()
 
-    cfg.plugins_dir = "123"
-
     cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
 
     # 加载插件可执行命令
@@ -474,15 +486,9 @@ if __name__ == "__main__":
     for command_category in command_categories:
         command_registry.import_commands(command_category)
 
+    cfg.command_registry =command_category
 
 
-    first_prompt  =FirstPrompt(cfg= cfg)
-    first_prompt.command_registry = command_registry
-
-    system_prompt = first_prompt.construct_first_prompt( fisrt_message=["this is a test goal"])
-
-    logger.info("[TEST]:" + system_prompt)
-
     logger.info(args)
     demo = build_webdemo()
     demo.queue(