114 lines
3.9 KiB
Python
114 lines
3.9 KiB
Python
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from langchain.agents import initialize_agent, Tool
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from langchain.tools import format_tool_to_openai_function, MoveFileTool, StructuredTool
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from pydantic import BaseModel, Field, create_model
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from pydantic.schema import model_schema, get_flat_models_from_fields
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from typing import List, Set
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import jsonref
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import json
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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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WeatherInfo, WorldTimeGetTimezoneByArea, Multiplier, KSigmaDetector,
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toLangchainTools, get_tool_schema,
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TOOL_DICT, TOOL_SETS
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)
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from configs.model_config import (llm_model_dict, LLM_MODEL, VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD)
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from langchain.chat_models import ChatOpenAI
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from langchain.agents import AgentType, initialize_agent
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import langchain
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# langchain.debug = True
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tools = toLangchainTools([WeatherInfo, Multiplier, KSigmaDetector])
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llm = ChatOpenAI(
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streaming=True,
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verbose=True,
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openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
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openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
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model_name=LLM_MODEL
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)
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chat_prompt = '''if you can
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tools: {tools}
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query: {query}
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if you choose llm-tool, you can direct
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'''
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# chain = LLMChain(prompt=chat_prompt, llm=llm)
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# content = chain({"tools": tools, "input": query})
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# tool的检索
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# tool参数的填充
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# 函数执行
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# from langchain.tools import StructuredTool
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tools = [
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StructuredTool(
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name=Multiplier.name,
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func=Multiplier.run,
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description=Multiplier.description,
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args_schema=Multiplier.ToolInputArgs,
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),
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StructuredTool(
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name=WeatherInfo.name,
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func=WeatherInfo.run,
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description=WeatherInfo.description,
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args_schema=WeatherInfo.ToolInputArgs,
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)
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]
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print(tools[0].func(1,2))
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tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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agent = initialize_agent(
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tools,
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llm,
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agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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verbose=True,
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return_intermediate_steps=True
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)
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# agent.return_intermediate_steps = True
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# content = agent.run("查询北京的行政编码，同时返回北京的天气情况")
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# print(content)
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# content = agent.run("判断这份数据是否存在异常，[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]")
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# content = agent("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")
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# # print(content)
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# from langchain.schema import (
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# AgentAction
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# )
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# s = ""
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# if isinstance(content, str):
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# s = content
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# else:
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# for i in content["intermediate_steps"]:
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# for j in i:
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# if isinstance(j, AgentAction):
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# s += j.log + "\n"
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# else:
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# s += "Observation: " + str(j) + "\n"
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# s += "final answer:" + content["output"]
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# print(s)
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# print(content["intermediate_steps"][0][0].log)
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# print( content["intermediate_steps"][0][0].log, content[""] + "\n" + content["i"] + "\n" + )
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# content = agent.run("i want to know the timezone of asia/shanghai, list all timezones available for that area.")
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# print(content) |