Extract context from voice notes with OpenRouter AI & Milvus for RAG systems

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