wehub-resource-sync
111 lines
3.9 KiB
Python
111 lines
3.9 KiB
Python
"""
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This script demonstrates the use of the LangChain's StructuredChatAgent and AgentExecutor alongside various tools
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The script utilizes the ChatGLM3 model, a large language model for understanding and generating human-like text.
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The model is loaded from a specified path and integrated into the chat agent.
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Tools:
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- Calculator: Performs arithmetic calculations.
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- Weather: Provides weather-related information based on input queries.
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- DistanceConverter: Converts distances between meters, kilometers, and feet.
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The agent operates in three modes:
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1. Single Parameter without History: Uses Calculator to perform simple arithmetic.
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2. Single Parameter with History: Uses Weather tool to answer queries about temperature, considering the
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conversation history.
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3. Multiple Parameters without History: Uses DistanceConverter to convert distances between specified units.
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4. Single use Langchain Tool: Uses Arxiv tool to search for scientific articles.
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Note:
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The model calling tool fails, which may cause some errors or inability to execute. Try to reduce the temperature
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parameters of the model, or reduce the number of tools, especially the third function.
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The success rate of multi-parameter calling is low. The following errors may occur:
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Required fields [type=missing, input_value={'distance': '30', 'unit': 'm', 'to': 'km'}, input_type=dict]
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The model illusion in this case generates parameters that do not meet the requirements.
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The top_p and temperature parameters of the model should be adjusted to better solve such problems.
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Success example:
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*****Action*****
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{
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'action': 'weather',
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'action_input': {
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'location': '厦门'
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}
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}
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*****Answer*****
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{
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'input': '厦门比北京热吗?',
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'chat_history': [HumanMessage(content='北京温度多少度'), AIMessage(content='北京现在12度')],
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'output': '根据最新的天气数据，厦门今天的气温为18度，天气晴朗。而北京今天的气温为12度。所以，厦门比北京热。'
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}
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****************
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"""
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import os
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from langchain import hub
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from langchain.agents import AgentExecutor, create_structured_chat_agent, load_tools
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from langchain_core.messages import AIMessage, HumanMessage
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from ChatGLM3 import ChatGLM3
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from tools.Calculator import Calculator
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from tools.Weather import Weather
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from tools.DistanceConversion import DistanceConverter
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MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
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if __name__ == "__main__":
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llm = ChatGLM3()
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llm.load_model(MODEL_PATH)
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prompt = hub.pull("hwchase17/structured-chat-agent")
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# for single parameter without history
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tools = [Calculator()]
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agent = create_structured_chat_agent(llm=llm, tools=tools, prompt=prompt)
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agent_executor = AgentExecutor(agent=agent, tools=tools)
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ans = agent_executor.invoke({"input": "34 * 34"})
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print(ans)
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# for singe parameter with history
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tools = [Weather()]
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agent = create_structured_chat_agent(llm=llm, tools=tools, prompt=prompt)
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agent_executor = AgentExecutor(agent=agent, tools=tools)
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ans = agent_executor.invoke(
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{
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"input": "厦门比北京热吗?",
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"chat_history": [
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HumanMessage(content="北京温度多少度"),
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AIMessage(content="北京现在12度"),
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],
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}
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)
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print(ans)
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# for multiple parameters without history
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tools = [DistanceConverter()]
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agent = create_structured_chat_agent(llm=llm, tools=tools, prompt=prompt)
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agent_executor = AgentExecutor(agent=agent, tools=tools)
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ans = agent_executor.invoke({"input": "how many meters in 30 km?"})
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print(ans)
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# for using langchain tools
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tools = load_tools(["arxiv"], llm=llm)
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agent = create_structured_chat_agent(llm=llm, tools=tools, prompt=prompt)
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agent_executor = AgentExecutor(agent=agent, tools=tools)
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ans = agent_executor.invoke({"input": "Describe the paper about GLM 130B"})
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print(ans)
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