feat(agent): Multi agent sdk (#976)

Co-authored-by: xtyuns <xtyuns@163.com>
Co-authored-by: Fangyin Cheng <staneyffer@gmail.com>
Co-authored-by: csunny <cfqsunny@163.com>
Co-authored-by: qidanrui <qidanrui@gmail.com>

examples/agents/auto_plan_agent_dialogue_example.py

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+"""Agents: auto plan agents example?
+
+    Examples:
+     
+        Execute the following command in the terminal:
+        Set env params.
+        .. code-block:: shell
+
+            export OPENAI_API_KEY=sk-xx
+            export OPENAI_API_BASE=https://xx:80/v1
+
+        run example.
+        ..code-block:: shell
+            python examples/agents/auto_plan_agent_dialogue_example.py 
+"""
+
+
+from dbgpt.agent.agents.planner_agent import PlannerAgent
+from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent
+from dbgpt.agent.agents.plan_group_chat import PlanChat, PlanChatManager
+
+from dbgpt.agent.agents.expand.code_assistant_agent import CodeAssistantAgent
+from dbgpt.agent.agents.expand.plugin_assistant_agent import PluginAgent
+from dbgpt.agent.agents.agents_mange import agent_mange
+
+from dbgpt.agent.agents.agent import AgentContext
+from dbgpt.agent.memory.gpts_memory import GptsMemory
+from dbgpt.core.interface.llm import ModelMetadata
+
+import asyncio
+import os
+
+if __name__ == "__main__":
+    from dbgpt.model import OpenAILLMClient
+
+    llm_client = OpenAILLMClient()
+    context: AgentContext = AgentContext(conv_id="test456", llm_provider=llm_client)
+    # context.llm_models = [ModelMetadata(model="gpt-3.5-turbo")]
+    context.llm_models = [ModelMetadata(model="gpt-4-vision-preview")]
+    context.gpts_name = "代码分析助手"
+
+    default_memory = GptsMemory()
+    coder = CodeAssistantAgent(memory=default_memory, agent_context=context)
+    ## TODO  add other agent
+
+    groupchat = PlanChat(agents=[coder], messages=[], max_round=50)
+    planner = PlannerAgent(
+        agent_context=context,
+        memory=default_memory,
+        plan_chat=groupchat,
+    )
+
+    manager = PlanChatManager(
+        plan_chat=groupchat,
+        planner=planner,
+        agent_context=context,
+        memory=default_memory,
+    )
+
+    user_proxy = UserProxyAgent(memory=default_memory, agent_context=context)
+
+    asyncio.run(
+        user_proxy.a_initiate_chat(
+            recipient=manager,
+            reviewer=user_proxy,
+            message="Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.",
+            # message="Find papers on gpt-4 in the past three weeks on arxiv, and organize their titles, authors, and links into a markdown table",
+            # message="find papers on LLM applications from arxiv in the last month, create a markdown table of different domains.",
+        )
+    )
+
+    ## dbgpt-vis message infos
+    print(asyncio.run(default_memory.one_plan_chat_competions("test456")))

examples/agents/single_agent_dialogue_example.py

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+"""Agents: single agents about CodeAssistantAgent?
+
+    Examples:
+     
+        Execute the following command in the terminal:
+        Set env params.
+        .. code-block:: shell
+
+            export OPENAI_API_KEY=sk-xx
+            export OPENAI_API_BASE=https://xx:80/v1
+
+        run example.
+        ..code-block:: shell
+            python examples/agents/single_agent_dialogue_example.py
+"""
+
+from dbgpt.agent.agents.expand.code_assistant_agent import CodeAssistantAgent
+from dbgpt.agent.agents.user_proxy_agent import UserProxyAgent
+from dbgpt.agent.memory.gpts_memory import GptsMemory
+from dbgpt.agent.agents.agent import AgentContext
+from dbgpt.core.interface.llm import ModelMetadata
+import asyncio
+import os
+
+
+if __name__ == "__main__":
+    from dbgpt.model import OpenAILLMClient
+
+    llm_client = OpenAILLMClient()
+    context: AgentContext = AgentContext(conv_id="test456", llm_provider=llm_client)
+    context.llm_models = [ModelMetadata(model="gpt-3.5-turbo")]
+
+    default_memory = GptsMemory()
+    coder = CodeAssistantAgent(memory=default_memory, agent_context=context)
+
+    user_proxy = UserProxyAgent(memory=default_memory, agent_context=context)
+
+    asyncio.run(
+        user_proxy.a_initiate_chat(
+            recipient=coder,
+            reviewer=user_proxy,
+            message="式计算下321 * 123等于多少",  # 用python代码的方式计算下321 * 123等于多少
+            # message="download data from https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.",
+        )
+    )
+
+    ## dbgpt-vis message infos
+    print(asyncio.run(default_memory.one_plan_chat_competions("test456")))