Extract context from voice notes with OpenRouter AI & Milvus for RAG systems
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 10 nodes and runs when an HTTP POST request hits its webhook URL. Along the way it connects OpenRouter Chat Model, Milvus Vector Store and Embeddings OpenAI.
How it runs
Trigger: When an HTTP POST request hits its webhook URL.
- Edit Fields (Edit Fields (Set)): sets 3 fields: title, transcript, timestamp
- AI Agent: runs an AI agent, with model: OpenRouter Chat Model; output parser: Structured Output Parser
- Edit Fields (Edit Fields (Set)): sets 2 fields: tite., output
- Convert to File: converts data to a file (to text)
- Milvus Vector Store: stores documents in the Milvus vector store, with embeddings: Embeddings OpenAI; document loader: Default Data Loader
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (9)
- Webhook
n8n-nodes-base.webhook - Edit Fields (Set)
n8n-nodes-base.set - AI Agent
@n8n/n8n-nodes-langchain.agent - Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured - OpenRouter Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenRouter - Convert to File
n8n-nodes-base.convertToFile - Milvus Vector Store
@n8n/n8n-nodes-langchain.vectorStoreMilvus - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi
From the author
Voice Note Context Extraction Pipeline with AI Agent & Vector Storage This n8n template demonstrates how to automatically extract and store contextual information from voice notes using AI agents and vector databases for future retrieval. How it works -…