import os, sys, requests src_dir = os.path.join( os.path.dirname(os.path.dirname(os.path.abspath(__file__))) ) sys.path.append(src_dir) from coagent.tools import ( toLangchainTools, get_tool_schema, DDGSTool, DocRetrieval, TOOL_DICT, TOOL_SETS ) from configs.model_config import * from coagent.connector.phase import BasePhase from coagent.connector.agents import BaseAgent from coagent.connector.chains import BaseChain from coagent.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS import importlib from configs.model_config import JUPYTER_WORK_PATH, KB_ROOT_PATH from coagent.llm_models.llm_config import EmbedConfig, LLMConfig from coagent.connector.configs import AGETN_CONFIGS from coagent.connector.schema import Message, load_role_configs print(src_dir) # tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT]) # TOOL_SETS = [ # "StockInfo", "StockName" # ] # tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT]) # role_configs = load_role_configs(AGETN_CONFIGS) # chain_configs = load_chain_configs(CHAIN_CONFIGS) # phase_configs = load_phase_configs(PHASE_CONFIGS) # agent_module = importlib.import_module("coagent.connector.agents") # # agent的测试 # llm_config = LLMConfig( # model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], # api_base_url=os.environ["API_BASE_URL"], temperature=0.3 # ) # embed_config = EmbedConfig( # embed_engine="model", embed_model="text2vec-base-chinese", # embed_model_path="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese" # ) # # 从已有的配置中选择一个config,具体参数细节见下面 # role_configs = load_role_configs(AGETN_CONFIGS) # agent_config = role_configs["general_planner"] # # 生成agent实例 # base_agent = BaseAgent( # role=agent_config.role, # prompt_config = agent_config.prompt_config, # prompt_manager_type=agent_config.prompt_manager_type, # chat_turn=agent_config.chat_turn, # focus_agents=[], # focus_message_keys=[], # llm_config=llm_config, # embed_config=embed_config, # jupyter_work_path=JUPYTER_WORK_PATH, # kb_root_path=KB_ROOT_PATH, # ) # # round-1 # 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" # query = Message( # role_name="human", role_type="user", # role_content=query_content, input_query=query_content, origin_query=query_content, # ) # output_message = base_agent.step(query) # print(output_message.to_str_content(content_key="parsed_output_list")) # chain的测试 llm_config = LLMConfig( model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], api_base_url=os.environ["API_BASE_URL"], temperature=0.3 ) embed_config = EmbedConfig( embed_engine="model", embed_model="text2vec-base-chinese", embed_model_path="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese" ) role_configs = load_role_configs(AGETN_CONFIGS) agent_config = role_configs["general_planner"] role1 = role_configs["general_planner"] role2 = role_configs["executor"] agent_module = importlib.import_module("coagent.connector.agents") agents = [ getattr(agent_module, role1.role.agent_type)( role=role1.role, prompt_config = role1.prompt_config, prompt_manager_type=role1.prompt_manager_type, chat_turn=role1.chat_turn, focus_agents=role1.focus_agents, focus_message_keys=role1.focus_message_keys, llm_config=llm_config, embed_config=embed_config, jupyter_work_path=JUPYTER_WORK_PATH, kb_root_path=KB_ROOT_PATH, ), getattr(agent_module, role2.role.agent_type)( role=role2.role, prompt_config = role2.prompt_config, prompt_manager_type=role2.prompt_manager_type, chat_turn=role2.chat_turn, focus_agents=role2.focus_agents, focus_message_keys=role2.focus_message_keys, llm_config=llm_config, embed_config=embed_config, jupyter_work_path=JUPYTER_WORK_PATH, kb_root_path=KB_ROOT_PATH, ), ] chain = BaseChain( agents, chat_turn=1, jupyter_work_path=JUPYTER_WORK_PATH, kb_root_path=KB_ROOT_PATH, llm_config=llm_config, embed_config=embed_config, ) # round-1 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" query = Message( role_name="human", role_type="user", role_content=query_content, input_query=query_content, origin_query=query_content, ) output_message, output_memory = chain.step(query) print(output_memory.to_str_messages(content_key="parsed_output_list")) # 测试 phase # phase_name = "toolReactPhase" # phase_name = "codeReactPhase" # phase_name = "chatPhase" # phase = BasePhase(phase_name, # task = None, # phase_config = PHASE_CONFIGS, # chain_config = CHAIN_CONFIGS, # role_config = AGETN_CONFIGS, # do_summary=False, # do_code_retrieval=False, # do_doc_retrieval=True, # do_search=False, # ) # query = Message(role_name="user", role_type="human", # 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") # phase_name = "baseTaskPhase" # phase = BasePhase(phase_name, # task = None, # phase_config = PHASE_CONFIGS, # chain_config = CHAIN_CONFIGS, # role_config = AGETN_CONFIGS, # do_summary=False, # do_code_retrieval=False, # do_doc_retrieval=True, # do_search=False, # ) # 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" # 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" # 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" # query = Message(role_name="user", role_type="human", role_content=query_content, input_query=query_content, origin_query=query_content, tools=tools) # query = Message(role_name="human", role_type="human", input_query=query_content, role_content=query_content, origin_query=query_content) # output_message = phase.step(query) # print(phase.get_chains_memory(content_key="step_content")) # print(phase.get_chains_memory_str(content_key="step_content")) # print(output_message.to_tuple_message(return_all=True)) # from coagent.tools import DDGSTool, CodeRetrieval # print(DDGSTool.run("langchain是什么", 3)) # print(CodeRetrieval.run("dsadsadsa", query.role_content, code_limit=3, history_node_list=[])) # from dev_opsgpt.chat.agent_chat import AgentChat # agentChat = AgentChat() # value = { # "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", # "phase_name": "toolReactPhase", # "chain_name": "", # "history": [], # "doc_engine_name": "DSADSAD", # "search_engine_name": "duckduckgo", # "code_engine_name": "", # "top_k": 3, # "score_threshold": 1.0, # "stream": False, # "local_doc_url": False, # "do_search": False, # "do_doc_retrieval": False, # "do_code_retrieval": False, # "do_tool_retrieval": False, # "custom_phase_configs": {}, # "custom_chain_configs": {}, # "custom_role_configs": {}, # "choose_tools": list(TOOL_SETS), # "history_node_list": [], # "isDetailed": False, # "upload_file": "" # } # for answer in agentChat.achat(**value): # print("answer:", answer)