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Embeddings Azure OpenAI#

Use the Embeddings Azure OpenAI node to generate embeddings for a given text.

On this page, you'll find the node parameters for the Embeddings Azure OpenAI node, and links to more resources.

Credentials

You can find authentication information for this node here.

Parameter resolution in sub-nodes

Sub-nodes behave differently to other nodes when processing multiple items using an expression.

Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five name values, the expression {{ $json.name }} resolves to each name in turn.

In sub-nodes, the expression always resolves to the first item. For example, given an input of five name values, the expression {{ $json.name }} always resolves to the first name.

Node options#

  • Model (Deployment) Name: The model(deployment) to use for generating embeddings.
  • Batch Size: maximum number of documents to send in each request.
  • Strip New Lines: whether to remove new line characters from input text. n8n enables this by default.
  • Timeout: maximum amount of time a request can take in seconds. Set to -1 for no timeout.

Templates and examples#

Ask questions about a PDF using AI

by David Roberts

View template details
Chat with PDF docs using AI (quoting sources)

by David Roberts

View template details
AI Crew to Automate Fundamental Stock Analysis - Q&A Workflow

by Derek Cheung

View template details
Browse Embeddings Azure OpenAI integration templates, or search all templates

Refer to LangChains's OpenAI embeddings documentation for more information about the service.

View n8n's Advanced AI documentation.

  • completion: Completions are the responses generated by a model like GPT.
  • hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
  • vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
  • vector store: 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.