在medium的这篇文章中,作者详细介绍了langchain 是如何调用tools的,感兴趣的可以直接参考这篇文章(medium的这篇文章是收费的,可以直接参考代码:https://github.com/sagaruprety/tutorial_langchain_agents/blob/main/langchain_documents_qa_serp_tools.ipynb) 我感兴趣的部分是作者是如何控制LLM调用工具的顺序。归根结...
1.4 agent from langchain.agents import load_tools from langchain.agents import initialize_agent from langchain.llms import OpenAI llm = OpenAI(temperature=0.9,api_key=api_key)os.environ["SERPAPI_API_KEY"] = "" tools = load_tools(["serpapi", "llm-math"], llm=llm) agent = initialize_...
template="""你精通多种语言,是专业的翻译官。你负责{src_lang}到{dst_lang}的翻译工作。"""prompt=PromptTemplate.from_template(template) # 自动解析出其中的变量 prompt.format(src_lang="英文", dst_lang="中文") # 然后使用 分步赋值 不同阶段,分次给模板赋值。 聊天提示词模板 得到的只是模板结果,之...
Action Input:"LangChain tutorial"[{'url':'https://python.langchain.com/docs/get_started/quickstart','content':"Once we have a key we'll want to set it as an environment variable by running:\nIf you'd prefer not to set an environment variable you can pass the key in directly via the...
from langchain.agents import AgentExecutor, create_react_agent from langchain_community.tools.tavily_search import TavilySearchResults from langchain_openai import OpenAI tools = [TavilySearchResults(max_results=1)] # Get the prompt to use - you can modify this!
•LLM Agents[173]:构建由 LLM 控制的代理•MiniChain[174]:用于与大型语言模型编码的微小库。•Griptape[175]:Python 框架,用于具有链式思维推理、外部工具和记忆的 AI 工作流和管道。•llm-chain[176]:一个强大的 rust 库,用于在 LLM 中构建链,让你能够总结文本和完成复杂任务。•BoxCars[177]:...
(tools)fromlangchain.agentsimportinitialize_agent,AgentTypefromlangchain_openaiimportChatOpenAI# 初始化大模型llm=ChatOpenAI(temperature=0.5)# 初始化Agentagent_chain=initialize_agent(tools,llm,agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,verbose=True,)# 实例化PlayWright浏览器工具包# 创建了...
Check out our Engineering and Data Application use-cases tutorial. LangChain Agents The fundamental concept of agents involves utilizing a language model to select a series of actions. Unlike chains, where a sequence of actions is pre-determined and embedded in the code, agents employ a language...
ENVIRONMENT_NAME=aca-sessions-tutorial-env CONTAINER_APP_NAME=chat-api 生成应用并将其部署到 Azure 容器应用: Bash 复制 az containerapp up \ --name $CONTAINER_APP_NAME \ --resource-group $RESOURCE_GROUP_NAME \ --location $SESSION_POOL_LOCATION \ --environment $ENVIRONMENT_NAME \ --env-va...
🤖 Agents: Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides astandard interface for agents, along withLangGraph.jsfor building...