import os, sys, requests src_dir = os.path.join( os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) ) sys.path.append(src_dir) from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH from configs.server_config import SANDBOX_SERVER from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS from coagent.llm_models.llm_config import EmbedConfig, LLMConfig from coagent.connector.phase import BasePhase from coagent.connector.schema import Message, Memory # log-level,print prompt和llm predict os.environ["log_verbose"] = "2" phase_name = "searchChatPhase" 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=os.path.join(src_dir, "embedding_models/text2vec-base-chinese") ) phase = BasePhase( phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH, embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH, ) # round-1 query_content1 = "美国当前总统是谁?" query = Message( role_name="human", role_type="user", role_content=query_content1, input_query=query_content1, origin_query=query_content1, search_engine_name="duckduckgo", score_threshold=1.0, top_k=3 ) output_message, output_memory = phase.step(query) print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list")) # round-2 query_content2 = "美国上一任总统是谁,两个人有什么关系没?" query = Message( role_name="human", role_type="user", role_content=query_content2, input_query=query_content2, origin_query=query_content2, search_engine_name="duckduckgo", score_threshold=1.0, top_k=3 ) output_message, output_memory = phase.step(query) print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))