词汇表
AI 代理(AI agent)#
人工智能代理是能够响应请求、做出决策并为用户执行现实世界任务的人工智能系统。它们使用大型语言模型（LLM）来解读用户输入，并根据可用的信息和资源决定如何最佳处理请求。
🌐 AI agents are artificial intelligence systems capable of responding to requests, making decisions, and performing real-world tasks for users. They use large language models (LLMs) to interpret user input and make decisions about how to best process requests using the information and resources they have available.
AI 链(AI chain)#
AI 链允许你通过调用各个组件的序列与大型语言模型（LLM）和其他资源进行交互。n8n 中的 AI 链不使用持久内存，因此你无法用它们来引用之前的上下文（如需此功能，请使用 AI 代理）。
🌐 AI chains allow you to interact with large language models (LLMs) and other resources in sequences of calls to components. AI chains in n8n don't use persistent memory, so you can't use them to reference previous context (use AI agents for this).
AI 补全(AI completion)#
完成的答案是由 GPT 等模型生成的。
🌐 Completions are the responses generated by a model like GPT.
AI 嵌入(AI embedding)#
嵌入是使用向量对数据进行的数值表示。人工智能通过在多个维度上映射数值来解释复杂的数据和关系。向量数据库，或称向量存储，是专门用于存储和访问嵌入的数据库。
🌐 Embeddings are numerical representations of data using vectors. They're used by AI to interpret complex data and relationships by mapping values across many dimensions. Vector databases, or vector stores, are databases designed to store and access embeddings.
AI 接地性(AI groundedness)#
在人工智能中，尤其是在增强检索生成（RAG）环境下，扎根性和非扎根性是衡量模型响应准确反映源信息程度的指标。模型使用其来源文档生成扎根的回应，而非扎根的回应则涉及推测或幻觉，这些内容没有得到相同来源的支持。
🌐 In AI, and specifically in retrieval-augmented generation (RAG) contexts, groundedness and ungroundedness are measures of how much a model's responses accurately reflect source information. The model uses its source documents to generate grounded responses, while ungrounded responses involve speculation or hallucination unsupported by those same sources.
AI 幻觉(AI hallucination)#
人工智能中的幻觉是指大型语言模型 (LLM) 错误地感知到不存在的模式或对象。
🌐 Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
AI 重排序(AI reranking)#
重新排序是一种技术，它通过优化候选文档列表的顺序来提高搜索结果的相关性。检索增强生成（RAG）以及其他应用使用重新排序来优先处理最相关的信息，以用于生成或下游任务。
🌐 Reranking is a technique that refines the order of a list of candidate documents to improve the relevance of search results. Retrieval-Augmented Generation (RAG) and other applications use reranking to prioritize the most relevant information for generation or downstream tasks.
AI 记忆(AI memory)#
在人工智能的上下文中，记忆允许人工智能工具在多次交互中保持消息的上下文。这使你能够与人工智能代理进行持续的对话，例如，无需在每条消息中提交连续的上下文。在 n8n 中，人工智能代理节点可以使用记忆，但人工智能链不能。
🌐 In an AI context, memory allows AI tools to persist message context across interactions. This allows you to have a continuing conversations with AI agents, for example, without submitting ongoing context with each message. In n8n, AI agent nodes can use memory, but AI chains can't.
AI 检索增强生成 (RAG)(AI retrieval-augmented generation (RAG))#
检索增强生成（Retrieval-augmented generation，简称 RAG）是一种技术，通过让大型语言模型（LLM）访问来自外部来源的新信息，以提升 AI 的响应能力。RAG 系统会检索相关文档，使生成的回答基于最新的、特定字段的或专有的知识，从而补充其原始训练数据。RAG 系统通常依赖向量存储来高效管理和搜索这些外部数据。
🌐 Retrieval-augmented generation, or RAG, is a technique for providing LLMs access to new information from external sources to improve AI responses. RAG systems retrieve relevant documents to ground responses in up-to-date, domain-specific, or proprietary knowledge to supplement their original training data. RAG systems often rely on vector stores to manage and search this external data efficiently.
AI 工具(AI tool)#
在人工智能的上下文中，工具是一种附加资源，AI在回应请求时可以参考该资源以获取特定信息或功能。AI模型可以使用工具与外部系统互动或完成特定的、专注的任务。
🌐 In an AI context, a tool is an add-on resource that the AI can refer to for specific information or functionality when responding to a request. The AI model can use a tool to interact with external systems or complete specific, focused tasks.
AI 向量存储(AI vector store)#
向量存储或向量数据库用于存储信息的数学表示。可与嵌入和检索器一起使用，创建一个数据库，让你的人工智能在回答问题时访问。
🌐 A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
应用接口(API)#
API，即应用编程接口，提供对服务的数据和功能的编程访问。API 使软件更容易与外部系统进行交互。它们通常作为传统以用户为中心的界面（通过网页浏览器或用户界面访问）的替代方案提供。
🌐 APIs, or application programming interfaces, offer programmatic access to a service's data and functionality. APIs make it easier for software to interact with external systems. They're often offered as an alternative to traditional user-focused interfaces accessed through web browsers or UI.
canvas (n8n)#
画布是 n8n 编辑器 UI 中构建工作流的主要界面。你可以使用画布添加和连接节点来组合工作流。
🌐 The canvas is the main interface for building workflows in n8n's editor UI. You use the canvas to add and connect nodes to compose workflows.
集群节点 (n8n)(cluster node (n8n))#
在 n8n 中，集群节点是协同工作的节点组，用于在工作流中提供功能。它们由一个根节点和一个或多个扩展节点功能的子节点组成。
🌐 In n8n, cluster nodes are groups of nodes that work together to provide functionality in a workflow. They consist of a root node and one or more sub nodes that extend the node's functionality.
凭证 (n8n)(credential (n8n))#
在 n8n 中，凭证存储用于连接特定应用和服务的认证信息。创建凭证并输入你的认证信息（用户名和密码、API 密钥、OAuth 密钥等）后，你可以使用关联的应用节点与该服务进行交互。
🌐 In n8n, credentials store authentication information to connect with specific apps and services. After creating credentials with your authentication information (username and password, API key, OAuth secrets, etc.), you can use the associated app node to interact with the service.
数据固定 (n8n)(data pinning (n8n))#
数据固定允许你在工作流开发期间临时冻结节点的输出数据。这使你能够在无需反复请求外部服务的情况下开发具有可预测数据的工作流。生产环境的工作流会忽略固定数据，并在每次执行时请求新的数据。
🌐 Data pinning allows you to temporarily freeze the output data of a node during workflow development. This allows you to develop workflows with predictable data without making repeated requests to external services. Production workflows ignore pinned data and request new data on each execution.
编辑器 (n8n)(editor (n8n))#
n8n 编辑器 UI 允许你创建和管理工作流。主要区域是画布，你可以在其中通过添加、配置和连接节点来组合工作流。侧边栏和顶部面板允许你访问 UI 的其他区域，如凭证、模板、变量、执行等。
🌐 The n8n editor UI allows you to create and manage workflows. The main area is the canvas, where you can compose workflows by adding, configuring, and connecting nodes. The side and top panels allow you to access other areas of the UI like credentials, templates, variables, executions, and more.
授权 (n8n)(entitlement (n8n))#
在 n8n 中，授权授予 n8n 实例在特定时间内访问计划限制功能的权限。
🌐 In n8n, entitlements grant n8n instances access to plan-restricted features for a specific period of time.
浮动许可是一种可以在多个 n8n 实例之间分配的许可池。你可以重新分配浮动许可，将其访问权限转移到不同的 n8n 实例上。
🌐 Floating entitlements are a pool of entitlements that you can distribute among various n8n instances. You can re-assign a floating entitlement to transfer its access to a different n8n instance.
评估 (n8n)(evaluation (n8n))#
在 n8n 中，评估允许你对执行历史进行标签和组织，并将其与新的执行进行比较。你可以利用这一功能来了解在进行更改时工作流随时间的表现。尤其是在开发以人工智能为中心的工作流时，这非常有用。
🌐 In n8n, evaluation allows you to tag and organize execution history and compare it against new executions. You can use this to understand how your workflow performs over time as you make changes. In particular, this is useful while developing AI-centered workflows.
表达式 (n8n)(expression (n8n))#
在 n8n 中，表达式允许你通过执行 JavaScript 代码动态填充节点参数。你可以使用 n8n 表达式语法来定义值，从而使用来自先前节点、其他工作流或你的 n8n 环境的数据，而不是提供静态值。
🌐 In n8n, expressions allow you to populate node parameters dynamically by executing JavaScript code. Instead of providing a static value, you can use the n8n expression syntax to define the value using data from previous nodes, other workflows, or your n8n environment.
LangChain#
LangChain 是一个用于与大型语言模型（LLM）协作的人工智能开发框架。LangChain 提供了一个标准化系统，用于处理各种模型和其他资源，并将不同组件连接在一起以构建复杂的应用。
🌐 LangChain is an AI-development framework used to work with large language models (LLMs). LangChain provides a standardized system for working with a wide variety of models and other resources and linking different components together to build complex applications.
大型语言模型 (LLM)(Large language model (LLM))#
大型语言模型，或称 LLM，是专为在自然语言处理（NLP）任务中表现出色而设计的人工智能机器学习模型。它们通过在大量数据上进行训练来构建语言和其他数据的概率模型。
🌐 Large language models, or LLMs, are AI machine learning models designed to excel in natural language processing (NLP) tasks. They're built by training on large amounts of data to develop probabilistic models of language and other data.
节点 (n8n)(node (n8n))#
在 n8n 中，节点是你组合用来创建工作流的单独组件。节点定义了工作流的运行时机，允许你获取、发送和处理数据，可以定义流程控制逻辑，并与外部服务连接。
🌐 In n8n, nodes are individual components that you compose to create workflows. Nodes define when the workflow should run, allow you to fetch, send, and process data, can define flow control logic, and connect with external services.
项目 (n8n)(project (n8n))#
n8n 项目允许您将工作流、变量和凭证分成不同的组，以便更容易管理。项目通过共享和将相关资源分块，使团队协作更容易。
🌐 n8n projects allow you to separate workflows, variables, and credentials into separate groups for easier management. Projects make it easier for teams to collaborate by sharing and compartmentalizing related resources.
根节点 (n8n)(root node (n8n))#
每个 n8n 集群节点包含一个定义集群主要功能的根节点。一个或多个子节点附加到根节点，以扩展其功能。
🌐 Each n8n cluster node contains a single root nodes that defines the main functionality of the cluster. One or more sub nodes attach to the root node to extend its functionality.
子节点 (n8n)(sub node (n8n))#
n8n 集群节点由一个或多个连接到根节点的子节点组成。子节点扩展了根节点的功能，提供对特定服务或资源的访问，或者提供特定类型的专用处理，例如计算功能。
🌐 n8n cluster nodes consist of one or more sub nodes connected to a root node. Sub nodes extend the functionality of the root node, providing access to specific services or resources or offering specific types of dedicated processing, like calculator functionality, for example.
模板 (n8n)(template (n8n))#
n8n 模板是由 n8n 及其社区成员设计的预构建工作流，您可以将其导入到您的 n8n 实例中。使用模板时，您可能需要填写凭据并调整配置以满足您的需求。
🌐 n8n templates are pre-built workflows designed by n8n and community members that you can import into your n8n instance. When using templates, you may need to fill in credentials and adjust the configuration to suit your needs.
触发节点 (n8n)(trigger node (n8n))#
触发节点是一种特殊节点，负责在特定条件下执行工作流。所有生产工作流至少需要一个触发器来确定工作流何时运行。
🌐 A trigger node is a special node responsible for executing the workflow in response to certain conditions. All production workflows need at least one trigger to determine when the workflow should run.
工作流 (n8n)(workflow (n8n))#
n8n 工作流是一组用于自动化流程的节点。工作流在触发条件发生时开始执行，并按顺序执行以完成复杂任务。
🌐 An n8n workflow is a collection of nodes that automate a process. Workflows begin execution when a trigger condition occurs and execute sequentially to achieve complex tasks.