⚒️ Extract context from voice notes with OpenRouter AI & Milvus for RAG systems
⚡ 680 views · ⚒️ Engineering
Description
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 (e.g., 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
🔗 Nodes Used
Webhook, AI Agent, Embeddings OpenAI, Structured Output Parser, Convert to File, Default Data Loader
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File