Voice Note Context Extraction Pipeline with AI Agent & Vector Storage
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. An n8n automation workflow template by Daniel Rosehill.
Daniel Rosehill
web
free
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