229 lines
8.1 KiB
Python
229 lines
8.1 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 coagent.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 coagent.connector.phase import BasePhase
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from coagent.connector.agents import BaseAgent
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from coagent.connector.chains import BaseChain
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from coagent.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS
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import importlib
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from configs.model_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
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from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
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from coagent.connector.configs import AGETN_CONFIGS
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from coagent.connector.schema import Message, load_role_configs
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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("coagent.connector.agents")
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# # agent的测试
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# llm_config = LLMConfig(
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# model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"],
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# api_base_url=os.environ["API_BASE_URL"], temperature=0.3
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# )
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# embed_config = EmbedConfig(
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# embed_engine="model", embed_model="text2vec-base-chinese",
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# embed_model_path="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese"
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# )
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# # 从已有的配置中选择一个config，具体参数细节见下面
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# role_configs = load_role_configs(AGETN_CONFIGS)
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# agent_config = role_configs["general_planner"]
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# # 生成agent实例
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# base_agent = BaseAgent(
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# role=agent_config.role,
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# prompt_config = agent_config.prompt_config,
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# prompt_manager_type=agent_config.prompt_manager_type,
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# chat_turn=agent_config.chat_turn,
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# focus_agents=[],
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# focus_message_keys=[],
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# llm_config=llm_config,
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# embed_config=embed_config,
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# jupyter_work_path=JUPYTER_WORK_PATH,
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# kb_root_path=KB_ROOT_PATH,
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# )
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# # round-1
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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(
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# role_name="human", role_type="user",
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# role_content=query_content, input_query=query_content, origin_query=query_content,
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# )
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# output_message = base_agent.step(query)
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# print(output_message.to_str_content(content_key="parsed_output_list"))
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# chain的测试
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llm_config = LLMConfig(
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model_name="gpt-3.5-turbo", api_key=os.environ["OPENAI_API_KEY"],
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api_base_url=os.environ["API_BASE_URL"], temperature=0.3
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)
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embed_config = EmbedConfig(
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embed_engine="model", embed_model="text2vec-base-chinese",
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embed_model_path="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese"
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)
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role_configs = load_role_configs(AGETN_CONFIGS)
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agent_config = role_configs["general_planner"]
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role1 = role_configs["general_planner"]
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role2 = role_configs["executor"]
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agent_module = importlib.import_module("coagent.connector.agents")
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agents = [
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getattr(agent_module, role1.role.agent_type)(
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role=role1.role,
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prompt_config = role1.prompt_config,
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prompt_manager_type=role1.prompt_manager_type,
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chat_turn=role1.chat_turn,
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focus_agents=role1.focus_agents,
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focus_message_keys=role1.focus_message_keys,
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llm_config=llm_config,
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embed_config=embed_config,
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jupyter_work_path=JUPYTER_WORK_PATH,
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kb_root_path=KB_ROOT_PATH,
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),
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getattr(agent_module, role2.role.agent_type)(
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role=role2.role,
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prompt_config = role2.prompt_config,
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prompt_manager_type=role2.prompt_manager_type,
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chat_turn=role2.chat_turn,
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focus_agents=role2.focus_agents,
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focus_message_keys=role2.focus_message_keys,
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llm_config=llm_config,
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embed_config=embed_config,
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jupyter_work_path=JUPYTER_WORK_PATH,
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kb_root_path=KB_ROOT_PATH,
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),
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]
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chain = BaseChain(
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agents,
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chat_turn=1,
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jupyter_work_path=JUPYTER_WORK_PATH,
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kb_root_path=KB_ROOT_PATH,
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llm_config=llm_config,
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embed_config=embed_config,
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)
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# round-1
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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(
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role_name="human", role_type="user",
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role_content=query_content, input_query=query_content, origin_query=query_content,
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)
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output_message, output_memory = chain.step(query)
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print(output_memory.to_str_messages(content_key="parsed_output_list"))
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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 coagent.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) |