2024-01-26 14:03:25 +08:00
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import os, sys
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2023-12-26 11:41:53 +08:00
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src_dir = os.path.join(
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os.path.dirname(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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2024-01-26 14:03:25 +08:00
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from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH, LLM_MODEL
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from configs.server_config import SANDBOX_SERVER
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from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
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from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
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from coagent.connector.phase import BasePhase
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from coagent.connector.schema import Message
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2023-12-26 11:41:53 +08:00
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2024-01-26 14:03:25 +08:00
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#
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2023-12-26 11:41:53 +08:00
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tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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2024-01-26 14:03:25 +08:00
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# log-level，print prompt和llm predict
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os.environ["log_verbose"] = "0"
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2023-12-26 11:41:53 +08:00
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phase_name = "baseGroupPhase"
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2024-01-26 14:03:25 +08:00
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llm_config = LLMConfig(
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model_name=LLM_MODEL, 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=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
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)
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2023-12-26 11:41:53 +08:00
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2024-01-26 14:03:25 +08:00
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phase = BasePhase(
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phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
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embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
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)
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2023-12-26 11:41:53 +08:00
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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_content = "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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query = Message(
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2024-01-26 14:03:25 +08:00
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role_name="human", role_type="user", tools=[],
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2023-12-26 11:41:53 +08:00
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role_content=query_content, input_query=query_content, origin_query=query_content,
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)
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2024-01-26 14:03:25 +08:00
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# phase.pre_print(query)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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