fix(docs): update doc example code to work (#2624)

Co-authored-by: alan.cl <alan.cl@antgroup.com>

docs/docs/agents/introduction/conversation.md

@@ -26,7 +26,7 @@ context: AgentContext = AgentContext(
 )
 # Create an agent memory, default memory is ShortTermMemory
 agent_memory: AgentMemory = AgentMemory()
-
+agent_memory.gpts_memory.init(conv_id="test123")
 
 system_prompt_template = """\
 You are a {{ role }}, {% if name %}named {{ name }}, {% endif %}your goal is {{ goal }}.

docs/docs/agents/introduction/custom_agents.md

@@ -10,7 +10,7 @@ summarizer.
 Install the required packages by running the following command:
 
 ```bash
-pip install "dbgpt[agent]>=0.5.6rc1" -U
+pip install "dbgpt[agent,simple_framework]>=0.7.0" "dbgpt_ext>=0.7.0" -U
 pip install openai
 ```
 
@@ -286,8 +286,8 @@ class SummaryActionInput(BaseModel):
     )
 
 class SummaryAction(Action[SummaryActionInput]):
-    def __init__(self):
+    def __init__(self, **kwargs):
-        super().__init__()
+        super().__init__(**kwargs)
 
     @property
     def resource_need(self) -> Optional[ResourceType]:
@@ -371,9 +371,9 @@ class MySummarizerAgent(ConversableAgent):
 ### Action Extended Parameter Processing
 
 ```python
-from typing import Optional, Dict, Any
+from typing import Optional, Dict, Any, List
 from pydantic import BaseModel
-from dbgpt.agent import Action, ActionOutput, AgentResource, ConversableAgent
+from dbgpt.agent import Agent, Action, AgentMessage, ActionOutput, AgentResource, ConversableAgent
 
 
 class SummaryActionInput(BaseModel):
@@ -399,7 +399,13 @@ class MySummarizerAgent(ConversableAgent):
         super().__init__(**kwargs)
         self._init_actions([SummaryAction])
     
-    def prepare_act_param(self) -> Dict[str, Any]:
+    def prepare_act_param(
+        self,
+        received_message: Optional[AgentMessage],
+        sender: Agent,
+        rely_messages: Optional[List[AgentMessage]] = None,
+        **kwargs,
+    ) -> Dict[str, Any]:
         return {"action_extra_param_key": "this is extra param"}
 ```
 
@@ -410,6 +416,7 @@ After the custom agent is created, you can use it in the following way:
 ```python
 
 import asyncio
+import os
 
 from dbgpt.agent import AgentContext, ConversableAgent, AgentMemory, LLMConfig, UserProxyAgent
 from dbgpt.model.proxy import OpenAILLMClient
@@ -418,10 +425,15 @@ class MySummarizerAgent(ConversableAgent):
     ...
 
 async def main():
-    llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo")
+    llm_client = OpenAILLMClient(
+        model_alias="gpt-3.5-turbo",  # or other models, eg. "gpt-4o"
+        api_base=os.getenv("OPENAI_API_BASE"),
+        api_key=os.getenv("OPENAI_API_KEY"),
+    )
     context: AgentContext = AgentContext(conv_id="summarize")
 
     agent_memory: AgentMemory = AgentMemory()
+    agent_memory.gpts_memory.init(conv_id="summarize")
 
     summarizer = (
         await MySummarizerAgent()
@@ -457,7 +469,7 @@ async def main():
             Nuclear electric rocket
             """,
     )
-    print(await agent_memory.gpts_memory.one_chat_completions("summarize"))
+    print(await agent_memory.gpts_memory.app_link_chat_message("summarize"))
 
 if __name__ == "__main__":
     asyncio.run(main())
@@ -467,9 +479,11 @@ Full code as follows:
 
 ```python
 import asyncio
-from typing import Any, Dict, Optional, Tuple
+import os
+from typing import Any, Dict, Optional, Tuple, List
 
 from dbgpt.agent import (
+    Agent,
     Action,
     ActionOutput,
     AgentContext,
@@ -548,7 +562,13 @@ class MySummarizerAgent(ConversableAgent):
         reply_message.context = {"not_related_message": NOT_RELATED_MESSAGE}
         return reply_message
 
-    def prepare_act_param(self) -> Dict[str, Any]:
+    def prepare_act_param(
+        self,
+        received_message: Optional[AgentMessage],
+        sender: Agent,
+        rely_messages: Optional[List[AgentMessage]] = None,
+        **kwargs,
+    ) -> Dict[str, Any]:
         return {"action_extra_param_key": "this is extra param"}
 
     async def correctness_check(
@@ -558,7 +578,7 @@ class MySummarizerAgent(ConversableAgent):
         action_report = message.action_report
         task_result = ""
         if action_report:
-            task_result = action_report.get("content", "")
+            task_result = action_report.content
 
         check_result, model = await self.thinking(
             messages=[
@@ -606,8 +626,8 @@ class SummaryActionInput(BaseModel):
 
 
 class SummaryAction(Action[SummaryActionInput]):
-    def __init__(self):
+    def __init__(self, **kwargs):
-        super().__init__()
+        super().__init__(**kwargs)
 
     @property
     def resource_need(self) -> Optional[ResourceType]:
@@ -671,10 +691,15 @@ class SummaryAction(Action[SummaryActionInput]):
 
 
 async def main():
-    llm_client = OpenAILLMClient(model_alias="gpt-3.5-turbo")
+    llm_client = OpenAILLMClient(
+        model_alias="gpt-3.5-turbo",  # or other models, eg. "gpt-4o"
+        api_base=os.getenv("OPENAI_API_BASE"),
+        api_key=os.getenv("OPENAI_API_KEY"),
+    )
     context: AgentContext = AgentContext(conv_id="summarize")
 
     agent_memory: AgentMemory = AgentMemory()
+    agent_memory.gpts_memory.init(conv_id="summarize")
 
     summarizer = (
         await MySummarizerAgent()
@@ -709,7 +734,7 @@ async def main():
             Nuclear electric rocket
             """,
     )
-    print(await agent_memory.gpts_memory.one_chat_completions("summarize"))
+    print(await agent_memory.gpts_memory.app_link_chat_message("summarize"))
 
 
 if __name__ == "__main__":

docs/docs/agents/introduction/database.md

@@ -9,7 +9,7 @@ the agent to the database.
 To use the database in the agent, you need to install the dependencies with the following command:
 
 ```bash
-pip install "dbgpt[simple_framework]>=0.5.9rc0"
+pip install "dbgpt[agent,simple_framework]>=0.7.0" "dbgpt_ext>=0.7.0"
 ```
 
 ## Create A Database Connector
@@ -33,7 +33,7 @@ created in temporary directory and will be deleted after the program exits.
 :::
 
 ```python
-from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnector
+from dbgpt_ext.datasource.rdbms.conn_sqlite import SQLiteTempConnector
 
 connector = SQLiteTempConnector.create_temporary_db()
 connector.create_temp_tables(
@@ -65,7 +65,7 @@ We connect to the SQLite database by giving the database file path, please make
 :::
 
 ```python
-from dbgpt.datasource.rdbms.conn_sqlite import SQLiteConnector
+from dbgpt_ext.datasource.rdbms.conn_sqlite import SQLiteConnector
 
 connector = SQLiteConnector.from_file_path("path/to/your/database.db")
 ```
@@ -81,7 +81,7 @@ make sure the connection information is correct.
 :::
 
 ```python
-from dbgpt.datasource.rdbms.conn_mysql import MySQLConnector
+from dbgpt_ext.datasource.rdbms.conn_mysql import MySQLConnector
 
 connector = MySQLConnector.from_uri_db(
     host="localhost",
@@ -130,6 +130,7 @@ async def main():
         conv_id="test123", language="en", temperature=0.5, max_new_tokens=2048
     )
     agent_memory = AgentMemory()
+    agent_memory.gpts_memory.init(conv_id="test123")
 
     user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build()
 
@@ -149,7 +150,7 @@ async def main():
     )
 
     ## dbgpt-vis message infos
-    print(await agent_memory.gpts_memory.one_chat_completions("test123"))
+    print(await agent_memory.gpts_memory.app_link_chat_message("test123"))
 
 
 if __name__ == "__main__":

docs/docs/agents/introduction/introduction.md

@@ -20,7 +20,7 @@ a simple way, only a little change to the code when you need to deploy your agen
 
 Firstly, you need to install the `dbgpt` package with the following command:
 ```bash
-pip install "dbgpt[agent]>=0.5.9rc0"
+pip install "dbgpt[agent,simple_framework]>=0.7.0" "dbgpt_ext>=0.7.0"
 ```
 
 Then, you can install the `openai` package with the following command:
@@ -81,6 +81,9 @@ async def main():
         .bind(agent_memory)
         .build()
     )
+    
+    # Initialize GptsMemory
+    agent_memory.gpts_memory.init(conv_id="test123")
 
     # Create a user proxy agent
     user_proxy = await UserProxyAgent().bind(context).bind(agent_memory).build()
@@ -92,7 +95,7 @@ async def main():
         message="Calculate the result of 321 * 123",
     )
     # Obtain conversation history messages between agents
-    print(await agent_memory.gpts_memory.one_chat_completions("test123"))
+    print(await agent_memory.gpts_memory.app_link_chat_message("test123"))
 
 
 if __name__ == "__main__":

docs/docs/agents/introduction/planning.md

@@ -30,7 +30,7 @@ will be created in the temporary directory and will be deleted after the program
 Fist, create a temporary SQLite database and create some tables and insert some data:
 
 ```python
-from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnector
+from dbgpt_ext.datasource.rdbms.conn_sqlite import SQLiteTempConnector
 
 connector = SQLiteTempConnector.create_temporary_db()
 connector.create_temp_tables(
@@ -140,6 +140,7 @@ async def main():
         conv_id="test123", language="en", temperature=0.5, max_new_tokens=2048
     )
     agent_memory = AgentMemory()
+    agent_memory.gpts_memory.init(conv_id="test123")
 
     user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build()
 
@@ -167,7 +168,7 @@ async def main():
     )
 
     # dbgpt-vis message infos
-    print(await agent_memory.gpts_memory.one_chat_completions("test123"))
+    print(await agent_memory.gpts_memory.app_link_chat_message("test123"))
 
 
 if __name__ == "__main__":

docs/docs/agents/introduction/tools.md

@@ -82,6 +82,7 @@ async def main():
         conv_id="test123", language="en", temperature=0.5, max_new_tokens=2048
     )
     agent_memory = AgentMemory()
+    agent_memory.gpts_memory.init(conv_id="test123")
 
     user_proxy = await UserProxyAgent().bind(agent_memory).bind(context).build()
 
@@ -107,7 +108,7 @@ async def main():
     )
 
     # dbgpt-vis message infos
-    print(await agent_memory.gpts_memory.one_chat_completions("test123"))
+    print(await agent_memory.gpts_memory.app_link_chat_message("test123"))
     
 if __name__ == "__main__":
     asyncio.run(main())