mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-08-30 15:01:31 +00:00
fix: agents & awel examples
This commit is contained in:
csunny
parent
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@ -1,3 +1,32 @@
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# TODO add example run code here
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# TODO add example run code here
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import asyncio
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# Agents examples
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from .agents.auto_plan_agent_dialogue_example import main as auto_plan_main
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from .agents.awel_layout_agents_chat_examples import main as awel_layout_main
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from .agents.custom_tool_agent_example import main as custom_tool_main
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from .agents.plugin_agent_dialogue_example import main as plugin_main
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from .agents.retrieve_summary_agent_dialogue_example import (
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main as retrieve_summary_main,
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)
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from .agents.sandbox_code_agent_example import main as sandbox_code_main
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from .agents.single_agent_dialogue_example import main as single_agent_main
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from .agents.sql_agent_dialogue_example import main as sql_main
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if __name__ == "__main__":
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if __name__ == "__main__":
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# Run the examples
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## Agent examples
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asyncio.run(auto_plan_main())
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asyncio.run(awel_layout_main())
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asyncio.run(custom_tool_main())
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asyncio.run(retrieve_summary_main())
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asyncio.run(plugin_main())
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asyncio.run(sandbox_code_main())
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asyncio.run(single_agent_main())
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asyncio.run(sql_main())
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## awel examples
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print("hello world!")
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print("hello world!")
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@ -31,7 +31,7 @@ from typing import Dict
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from dbgpt._private.pydantic import BaseModel, Field
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from dbgpt._private.pydantic import BaseModel, Field
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from dbgpt.core.awel import DAG, HttpTrigger, MapOperator
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from dbgpt.core.awel import DAG, HttpTrigger, MapOperator
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from dbgpt.model.proxy import OpenAILLMClient
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from dbgpt.model.proxy import OpenAILLMClient
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from dbgpt.rag.knowledge import KnowledgeType
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from dbgpt.rag.knowledge.base import KnowledgeType
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from dbgpt.rag.operators import KnowledgeOperator, SummaryAssemblerOperator
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from dbgpt.rag.operators import KnowledgeOperator, SummaryAssemblerOperator
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@ -1,118 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-10T04:38:14.228948Z",
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"start_time": "2024-04-10T04:38:14.224972Z"
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}
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},
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"source": [
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"import nest_asyncio\n",
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"from dbgpt.agent import (\n",
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" AgentContext,\n",
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" GptsMemory,\n",
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" LLMConfig,\n",
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" ResourceLoader,\n",
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" UserProxyAgent,\n",
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")\n",
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"from dbgpt.agent.expand.code_assistant_agent import CodeAssistantAgent\n",
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"from dbgpt.agent.plan import AutoPlanChatManager\n",
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"from dbgpt.model.proxy import OpenAILLMClient\n",
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"\n",
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"nest_asyncio.apply()"
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],
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"execution_count": 7,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"is_executing": true
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},
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"source": [
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"# Set your api key and api base url\n",
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"# os.environ[\"OPENAI_API_KEY\"] = \"Your API\"\n",
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"# os.environ[\"OPENAI_API_BASE\"] = \"https://api.openai.com/v1\""
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],
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-10T04:19:47.838081Z",
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"start_time": "2024-04-10T04:17:54.465616Z"
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}
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},
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"source": [
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"llm_client = OpenAILLMClient(model_alias=\"gpt-4\")\n",
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"context: AgentContext = AgentContext(conv_id=\"test456\", gpts_app_name=\"代码分析助手\")\n",
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"\n",
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"default_memory = GptsMemory()\n",
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"\n",
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"resource_loader = ResourceLoader()\n",
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"\n",
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"coder = (\n",
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" await CodeAssistantAgent()\n",
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" .bind(context)\n",
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" .bind(LLMConfig(llm_client=llm_client))\n",
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" .bind(default_memory)\n",
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" .bind(resource_loader)\n",
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" .build()\n",
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")\n",
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"\n",
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"manager = (\n",
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" await AutoPlanChatManager()\n",
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" .bind(context)\n",
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" .bind(default_memory)\n",
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" .bind(LLMConfig(llm_client=llm_client))\n",
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" .build()\n",
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")\n",
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"manager.hire([coder])\n",
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"\n",
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"user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build()\n",
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"\n",
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"\n",
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"await user_proxy.initiate_chat(\n",
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" recipient=manager,\n",
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" reviewer=user_proxy,\n",
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" 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.\",\n",
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" # message=\"Find papers on gpt-4 in the past three weeks on arxiv, and organize their titles, authors, and links into a markdown table\",\n",
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" # message=\"find papers on LLM applications from arxiv in the last month, create a markdown table of different domains.\",\n",
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")"
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],
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"execution_count": 4,
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"outputs": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "dbgpt_env",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "f8b6b0e04f284afd2fbb5e4163e7d03bbdc845eaeb6e8c78fae04fce6b51dae6"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -1,135 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"id": "6de2e0bb",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-10T04:37:21.832993Z",
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"start_time": "2024-04-10T04:37:21.828221Z"
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}
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},
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"source": [
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"import os\n",
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"import nest_asyncio\n",
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"from dbgpt.agent import (\n",
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" AgentContext,\n",
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" AgentResource,\n",
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" GptsMemory,\n",
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" LLMConfig,\n",
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" ResourceLoader,\n",
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" ResourceType,\n",
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" UserProxyAgent,\n",
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")\n",
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"from dbgpt.agent.expand.plugin_assistant_agent import PluginAssistantAgent\n",
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"from dbgpt.agent.expand.summary_assistant_agent import SummaryAssistantAgent\n",
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"from dbgpt.agent.plan import WrappedAWELLayoutManager\n",
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"from dbgpt.agent.resource import PluginFileLoadClient\n",
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"from dbgpt.configs.model_config import ROOT_PATH\n",
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"from dbgpt.model.proxy import OpenAILLMClient\n",
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"\n",
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"nest_asyncio.apply()\n",
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"test_plugin_dir = os.path.join(ROOT_PATH, \"examples/test_files/plugins\")"
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],
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"execution_count": 11,
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"outputs": []
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},
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{
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"cell_type": "code",
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"id": "437b9c40",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-10T04:37:27.592117Z",
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"start_time": "2024-04-10T04:37:23.569538Z"
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}
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},
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"source": [
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"# os.environ['OPENAI_API_KEY']=\"sk-x\"\n",
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"# os.environ['OPENAI_API_BASE']=\"https://proxy_url/v1\"\n",
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"# os.environ['SEARCH_ENGINE']=\"baidu\"\n",
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"# os.environ['BAIDU_COOKIE']=\"\"\"your baidu cookie\"\"\"\n",
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"\n",
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"llm_client = OpenAILLMClient(model_alias=\"gpt-3.5-turbo\")\n",
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"context: AgentContext = AgentContext(conv_id=\"test456\", gpts_app_name=\"信息析助手\")\n",
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"\n",
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"default_memory = GptsMemory()\n",
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"\n",
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"resource_loader = ResourceLoader()\n",
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"plugin_file_loader = PluginFileLoadClient()\n",
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"resource_loader.register_resource_api(plugin_file_loader)\n",
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"\n",
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"plugin_resource = AgentResource(\n",
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" type=ResourceType.Plugin,\n",
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" name=\"test\",\n",
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" value=test_plugin_dir,\n",
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")\n",
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"\n",
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"tool_engineer = (\n",
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" await PluginAssistantAgent()\n",
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" .bind(context)\n",
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" .bind(LLMConfig(llm_client=llm_client))\n",
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" .bind(default_memory)\n",
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" .bind([plugin_resource])\n",
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" .bind(resource_loader)\n",
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" .build()\n",
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")\n",
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"summarizer = (\n",
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" await SummaryAssistantAgent()\n",
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" .bind(context)\n",
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" .bind(default_memory)\n",
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" .bind(LLMConfig(llm_client=llm_client))\n",
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" .build()\n",
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")\n",
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"\n",
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"manager = (\n",
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" await WrappedAWELLayoutManager()\n",
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" .bind(context)\n",
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" .bind(default_memory)\n",
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" .bind(LLMConfig(llm_client=llm_client))\n",
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" .build()\n",
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")\n",
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"manager.hire([tool_engineer, summarizer])\n",
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"\n",
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"user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build()\n",
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"\n",
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"await user_proxy.initiate_chat(\n",
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" recipient=manager,\n",
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" reviewer=user_proxy,\n",
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" message=\"查询成都今天天气\",\n",
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" # message=\"查询今天的最新热点财经新闻\",\n",
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")"
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],
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"execution_count": 12,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7ded4107",
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"metadata": {},
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"source": [],
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"outputs": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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