importcom.fasterxml.jackson.databind.JsonNode;importcom.fasterxml.jackson.databind.ObjectMapper;importcom.langchain.agents.Agent;importcom.langchain.agents.AgentExecutor;importcom.langchain.agents.AgentType;importcom.langchain.llms.LargeLanguageModel;importcom.langchain.tools.Tool;importjava.util.Arrays;impor...
请看下面的例子,演示了使用 LangChain 中的 Tool Calling Agent 来构建一个智能助手的过程,该助手可以根据用户提供的查询自动调用适当的工具,并返回相关信息。 import os from langchain_openai import ChatOpenAI from langchain_community.tools.tavily_search import TavilySearchResults from langchain.agents import...
基于LLaMA + LangChain 的 Agents 实现已经有大佬做过了,链接如下: 本文主要聚焦 ChatGLM2,原因主要有以下两点: ChatGLM2-6B 的使用非常新手友好,部署、p-tuning、Finetune方便,支持低精度推理。 ChatGLM 系列模型对中文的支持较好,原因是训练时加入了较多中文语料,并且进行过 RLHF 更多ChatGLM2 测评以及和 baich...
# Import things that are needed genericallyfromlangchain.chainsimportLLMMathChainfromlangchain.utilitiesimportSerpAPIWrapperfromlangchain.agentsimportAgentType,initialize_agentfromlangchain.chat_modelsimportChatOpenAIfromlangchain.toolsimportBaseTool,StructuredTool,Tool,tool llm=ChatOpenAI(temperature=0) 实例化Too...
langchain agent with tools sample code importasynciofromlangchain_openaiimportChatOpenAIfromlangchain.agentsimporttoolfromlangchain_core.promptsimportChatPromptTemplate, MessagesPlaceholderfromlangchain.agents.format_scratchpad.openai_toolsimport( format_to_openai_tool_messages,...
Step 1: Building the Chain Step 2: Testing the Chain Step 3: Testing the Memory Conclusion How to Add Memory to Both an Agent and its Tools in LangChain? Adding memory to the agents and tools enables them to work better with the ability to use the chat history of the model. With mem...
args_schema(Pydantic BaseModel), is optional but recommended and can be used to provide more information (e.g., few-shot examples) or validation for expected parameters. Before we create a tool, let’s set up our environment: fromlangchainimportLLMMathChainfromlangchain.agentsimportAgentType, ...
1. 执行如下命令,安装 LangChain 的 npm 包 npm install -S langchain 2. 在目录下面创建一个以.mjs 为后缀的文件(例如:test1.mjs) Agents(智体) 在LangChain 中,一个Agent代表的是一个具备理解和生成文本能力的实例。通过给这些 Agent 设置特定行为和数据源,就可以训练他们执行各种与语言相关的任务,从而使...
from langchain.agents import create_openai_functions_agent from langchain.agents import AgentExecutor from langchain import hub # 这里需要配置KEY 免费 search = TavilySearchResults() # message1 = search.invoke("what is the weather in SF") ...
and a flow run will be created to track the invocation of the LLM: Run several LLM calls via langchain agent as Prefect subflows: fromlangchain.agentsimportinitialize_agent,load_toolsfromlangchain.llmsimportOpenAIfromprefectimportflowllm=OpenAI(temperature=0)tools=load_tools(["llm-math"],llm=ll...