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

• Webhook trigger receives voice note data including title, transcript, and timestamp from external services (example here: voicenotes.com )
• Field extraction isolates the key data fields (title, transcript, timestamp) for processing
• AI Context Agent processes the transcript to extract meaningful context while:
  • Correcting speech-to-text errors
  • Converting first-person references to third-person facts
  • Filtering out casual conversation and focusing on significant information
• Output formatting structures the extracted context with timestamps for embedding
• File conversion prepares the context data for vector storage
• Vector embedding uses OpenAI embeddings to create searchable representations
• Milvus storage stores the embedded context for future retrieval in RAG applications

How to use

• Configure the webhook endpoint to receive data from your voice note service
• Set up credentials for OpenRouter (LLM), OpenAI (embeddings), and Milvus (vector storage)
• Customize the AI ​​agent's system prompt to match your context extraction needs
• The workflow automatically processes incoming voice notes and stores extracted context

Requirements

• OpenRouter account for LLM access
• OpenAI API key for embeddings
• Milvus vector database (cloud or self-hosted)
• Voice note service with webhook capabilities (eg, Voicenotes.com )

Customizing this workflow

• Modify the context extraction prompt to focus on specific types of information (preferences, facts, relationships)
• Add filtering logic to process only voice notes with specific tags or keywords
• Integrate with other storage systems like Pinecone, Weaviate, or local vector databases
• Connect to RAG systems to use the stored context for enhanced AI conversations
• Add notification nodes to confirm successful context extraction and storage

Use cases

• Personal AI assistant that remembers your preferences and context from voice notes
• Knowledge management system for capturing insights from recorded thoughts
• Content creation pipeline that extracts key themes from voice recordings
• Research assistant that builds context from interview transcripts or meeting notes