198 lines
8.2 KiB
Python
198 lines
8.2 KiB
Python
import os, sys, requests
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src_dir = os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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sys.path.append(src_dir)
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from dev_opsgpt.tools import (
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toLangchainTools, get_tool_schema, DDGSTool, DocRetrieval,
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TOOL_DICT, TOOL_SETS
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)
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from configs.model_config import *
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from dev_opsgpt.connector.phase import BasePhase
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from dev_opsgpt.connector.agents import BaseAgent
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from dev_opsgpt.connector.chains import BaseChain
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from dev_opsgpt.connector.schema import (
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Message, load_role_configs, load_phase_configs, load_chain_configs
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)
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from dev_opsgpt.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS
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import importlib
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print(src_dir)
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# tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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TOOL_SETS = [
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"StockInfo", "StockName"
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]
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tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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role_configs = load_role_configs(AGETN_CONFIGS)
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chain_configs = load_chain_configs(CHAIN_CONFIGS)
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phase_configs = load_phase_configs(PHASE_CONFIGS)
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agent_module = importlib.import_module("dev_opsgpt.connector.agents")
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# agent的测试
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query = Message(role_name="tool_react", role_type="human",
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input_query="我有一份时序数据，[0.857, 2.345, 1.234, 4.567, 3.456, 9.876, 5.678, 7.890, 6.789, 8.901, 10.987, 12.345, 11.234, 14.567, 13.456, 19.876, 15.678, 17.890, 16.789, \
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18.901, 20.987, 22.345, 21.234, 24.567, 23.456, 29.876, 25.678, 27.890, 26.789, 28.901, 30.987, 32.345, 31.234, 34.567, 33.456, 39.876, 35.678, 37.890, 36.789, 38.901, 40.987]，\
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我不知道这份数据是否存在问题，请帮我判断一下", tools=tools)
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query = Message(role_name="tool_react", role_type="human",
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input_query="帮我确认下127.0.0.1这个服务器的在10点是否存在异常，请帮我判断一下", tools=tools)
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query = Message(role_name="code_react", role_type="human",
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input_query="帮我确认当前目录下有哪些文件", tools=tools)
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# "给我一份冒泡排序的代码"
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query = Message(role_name="user", role_type="human",
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input_query="对employee_data.csv进行数据分析", tools=tools)
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role = role_configs["general_planner"]
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agent_class = getattr(agent_module, role.role.agent_type)
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agent = agent_class(role.role,
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task = None,
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memory = None,
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chat_turn=role.chat_turn,
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do_search = role.do_search,
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do_doc_retrieval = role.do_doc_retrieval,
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do_tool_retrieval = role.do_tool_retrieval,)
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# message = agent.run(query)
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# print(message.role_content)
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# chain的测试
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# query = Message(role_name="deveploer", role_type="human", role_content="编写冒泡排序，并生成测例")
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# query = Message(role_name="general_planner", role_type="human", role_content="对employee_data.csv进行数据分析")
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# query = Message(role_name="tool_react", role_type="human", role_content="我有一份时序数据，[0.857, 2.345, 1.234, 4.567, 3.456, 9.876, 5.678, 7.890, 6.789, 8.901, 10.987, 12.345, 11.234, 14.567, 13.456, 19.876 , 15.678, 17.890, 16.789, 18.901, 20.987, 22.345, 21.234, 24.567, 23.456, 29.876, 25.678, 27.890, 26.789, 28.901, 30.987, 32.345, 31.234, 34.567, 33.456, 39.876, 35.678, 37.890, 36.789, 38.901, 40.987]，\我不知道这份数据是否存在问题，请帮我判断一下", tools=tools)
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# role = role_configs[query.role_name]
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# role1 = role_configs["general_planner"]
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# role2 = role_configs["executor"]
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# agents = [
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# getattr(agent_module, role1.role.agent_type)(role1.role,
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# task = None,
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# memory = None,
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# do_search = role1.do_search,
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# do_doc_retrieval = role1.do_doc_retrieval,
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# do_tool_retrieval = role1.do_tool_retrieval,),
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# getattr(agent_module, role2.role.agent_type)(role2.role,
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# task = None,
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# memory = None,
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# do_search = role2.do_search,
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# do_doc_retrieval = role2.do_doc_retrieval,
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# do_tool_retrieval = role2.do_tool_retrieval,
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# stop = "\n**Observation:**",
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# chat_turn=5,
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# ),
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# ]
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# query = Message(role_name="user", role_type="human",
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# input_query="确认本地是否存在book_data.csv，并查看它有哪些列和数据类型，分析这份数据的内容，根据这个数据预测未来走势", tools=tools)
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# query = Message(role_name="user", role_type="human",
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# input_query="确认本地是否存在employee_data.csv，并查看它有哪些列和数据类型;然后画柱状图", tools=tools)
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# chain = BaseChain(chain_configs["executorChain"], agents, do_code_exec=False, chat_turn=1, do_checker=False)
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# output_message, local_memory = chain.step(query)
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# print(output_message.role_content)
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# print("\n".join("\n".join([": ".join(j) for j in i]) for i in chain.get_agents_memory()))
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# print("\n".join(": ".join(i) for i in chain.get_memory()))
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# print( chain.get_agents_memory_str())
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# print( chain.get_memory_str())
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# 测试 phase
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phase_name = "toolReactPhase"
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phase_name = "codeReactPhase"
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# phase_name = "chatPhase"
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# phase = BasePhase(phase_name,
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# task = None,
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# phase_config = PHASE_CONFIGS,
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# chain_config = CHAIN_CONFIGS,
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# role_config = AGETN_CONFIGS,
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# do_summary=False,
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# do_code_retrieval=False,
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# do_doc_retrieval=True,
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# do_search=False,
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# )
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# query = Message(role_name="user", role_type="human",
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# input_query="确认本地是否存在employee_data.csv，并查看它有哪些列和数据类型，并选择合适的数值列画出折线图")
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phase_name = "baseTaskPhase"
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phase = BasePhase(phase_name,
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task = None,
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phase_config = PHASE_CONFIGS,
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chain_config = CHAIN_CONFIGS,
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role_config = AGETN_CONFIGS,
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do_summary=False,
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do_code_retrieval=False,
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do_doc_retrieval=True,
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do_search=False,
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)
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query_content = "查询贵州茅台的股票代码，并查询截止到当前日期(2023年11月8日)的最近10天的每日时序数据，然后对时序数据画出折线图并分析"
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query_content = "判断下127.0.0.1这个服务器的在10点的监控数据，是否存在异常"
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query_content = "确认本地是否存在employee_data.csv，并查看它有哪些列和数据类型;然后画柱状图"
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query = Message(role_name="user", role_type="human", role_content=query_content, input_query=query_content, origin_query=query_content, tools=tools)
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query = Message(role_name="human", role_type="human", input_query=query_content, role_content=query_content, origin_query=query_content)
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output_message = phase.step(query)
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# print(phase.get_chains_memory(content_key="step_content"))
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# print(phase.get_chains_memory_str(content_key="step_content"))
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# print(output_message.to_tuple_message(return_all=True))
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from dev_opsgpt.tools import DDGSTool, CodeRetrieval
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# print(DDGSTool.run("langchain是什么", 3))
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# print(CodeRetrieval.run("dsadsadsa", query.role_content, code_limit=3, history_node_list=[]))
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# from dev_opsgpt.chat.agent_chat import AgentChat
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# agentChat = AgentChat()
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# value = {
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# "query": "帮我确认下127.0.0.1这个服务器的在10点是否存在异常，请帮我判断一下",
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# "phase_name": "toolReactPhase",
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# "chain_name": "",
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# "history": [],
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# "doc_engine_name": "DSADSAD",
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# "search_engine_name": "duckduckgo",
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# "code_engine_name": "",
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# "top_k": 3,
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# "score_threshold": 1.0,
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# "stream": False,
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# "local_doc_url": False,
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# "do_search": False,
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# "do_doc_retrieval": False,
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# "do_code_retrieval": False,
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# "do_tool_retrieval": False,
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# "custom_phase_configs": {},
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# "custom_chain_configs": {},
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# "custom_role_configs": {},
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# "choose_tools": list(TOOL_SETS),
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# "history_node_list": [],
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# "isDetailed": False,
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# "upload_file": ""
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# }
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# for answer in agentChat.achat(**value):
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# print("answer:", answer) |