构建工作流
AI examples
This section provides explanations of important AI concepts, and workflow templates that highlight those concepts, with explanations and configuration guides. The examples cover common use cases and highlight different features of advanced AI in n8n.
- Agents and chains
Learn about agents1 and chains2 in AI, including exploring key differences using the example workflow.
→ What's a chain in AI?\ → What's an agent in AI?\ → Demonstration of key differences between agents and chains
- Call n8n Workflow Tool
Learn about tools3 in AI, then explore examples that use n8n workflows as custom tools to give your AI workflow access to more data.
→ What's a tool in AI?\ → Chat with Google Sheets\ → Call an API to fetch data\ → Set up a human fallback\ → Let AI specify tool parameters with $fromAI()
- Vector databases
Learn about vector databases4 in AI, along with related concepts including embeddings5 and retrievers.
→ What's a vector database?\ → Populate a Pinecone vector database from a website
- Memory
Learn about memory6 in AI.
- AI workflow templates
You can browse AI templates, included community contributions, on the n8n website.
Footnotes
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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.
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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).
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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.
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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.
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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.
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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.
官方原文和授权
本页来自 N8N 英文官方网站固定快照，并转换成 xueai 静态页面。内容以 N8N 持续更新的官方页面为准。