fromlangchain.tools.renderimportrender_text_description fromlangchain_community.utilitiesimportSerpAPIWrapper # 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") prompt=prompt.partial( tools=render_text_description(tools)...
fromlangchain.tools.renderimportrender_text_description fromlangchain_community.utilitiesimportSerpAPIWrapper # 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") prompt = prompt.partial( tools=render_text_descript...
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...
fromlangchain.tools.renderimportrender_text_descriptiontools=[book_rooms]rendered_tools=render_text_description(tools)system_prompt=f"""You are an assistant that has access to the following set of tools. Here are the names and descriptions for each tool:{rendered_tools}Given the user input, retu...
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.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_log_to...
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...
(tools=render_text_description(tools),tool_names=", ".join([t.namefortintools]),)# define the agentchat_model_with_stop=chat_model.bind(stop=["\nObservation"])agent=({"input":lambdax:x["input"],"agent_scratchpad":lambdax:format_log_to_str(x["intermediate_steps"]),}|prompt|chat_...
tools=render_text_description(tools), tool_names=", ".join([t.namefortintools]), ) Building Agent Now, configure the agent and its tools by integrating it with the language model loaded in step 2 of this guide: llm_with_stop=llm.bind(stop=["\nObservation"]) ...
"""tool_strings=render_text_description(list(tools))# rendor tools to txttool_names=", ".join([tool.namefortoolintools])format_instructions=format_instructions.format(tool_names=tool_names)template="\n\n".join([prefix,tool_strings,format_instructions,suffix])# generate PromptTemplateifinput_va...