prompt=hub.pull("hwchase17/react-json")prompt=prompt.partial(tools=render_text_description(tools),tool_names=",".join([t.namefortintools]),)# 定义智能体 chat_model_with_stop=chat_model.bind(stop=["\nObservation"])agent=({"input":lambda x:x["input"],"agent_scratchpad":lambda x:format...
rendered_tools = render_text_description([multiply, add]) print("rendered_tools = ", rendered_tools) 此部分需要好好讲解一下这是工具选择功能实现的核心! 此部分render_text_description函数的作用是构建函数描述符,返回函数的头以及参数规格和函数的功能描述。其对应的输出如下: rendered_tools = multiply(fir...
from langchain.agents.output_parsers import ReActSingleInputOutputParser from langchain.tools.render import render_text_description 创建工具 search = DuckDuckGoSearchAPIWrapper() search_tool = Tool(name="Current Search", func=search.run, description="Useful when you need to answer questions about noun...
items(): # 如果值是字典,递归调用tool_chain if isinstance(value, dict): arguments[key] = tool_chain(value) # 使用参数调用选定的工具 return choose_tool.invoke(arguments) # 渲染工具的文本描述 rendered_tools = render_text_description(tools) # 定义系统提示 system_prompt = f"""你是一个可以访问...
tools=render_text_description(tools), tool_names=", ".join([t.namefortintools]), ) # 定义智能体 chat_model_with_stop = chat_model.bind(stop=["\nObservation"]) agent = ( { "input":lambdax: x["input"], "agent_scratchpad":lambdax: format_log_to_str(x["intermediate_steps"]), ...
tools=render_text_description(tools), tool_names=", ".join([t.namefortintools]), ) # 定义智能体 chat_model_with_stop=chat_model.bind(stop=["\nObservation"]) agent=( { "input":lambdax:x["input"], "agent_scratchpad":lambdax:format_log_to_str(x["intermediate_steps"]), ...
from langchain.tools.render import render_text_description from langchain_community.utilities import SerpAPIWrapper # 设置工具 tools = load_tools(["serpapi", "llm-math"], llm=llm) # 设置 ReAct 风格的提示词 prompt = hub.pull("hwchase17/react-json") ...
tools=render_text_description(tools), tool_names=", ".join([t.name for t in tools]), ) # define the agent chat_model_with_stop = chat_model.bind(stop=["\nObservation"]) agent = ( { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_log_to_str(x["interme...
tool_input = render_text_description_and_args(tools) print(tool_input) # 打印工具输入 生成如下字符串说明: "Recommender":"当你需要推荐电影时很有用", "args":{ "参数":{ "movie":{ { "title":"电影", "description":"用于推荐的电影", ...
tools=render_text_description(tools), tool_names=", ".join([t.name for t in tools]), ) llm_with_stop = llm.bind(stop=["\nObservation"]) agent = ( { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]), ...