Hi all, I'm working within an n8n workflow, using the "Supabase Vector Store" node to perform semantic searches against a Supabase/pgvector database. My vector store setup follows the standard SQL provided in the Supabase documentation – the basic documents table (with a metadata JSONB column) and the match_documents function that uses the pgvector extension. Document dates are stored within the metadata column (e.g., {"date": "YYYY-MM-DD", ...}). The challenge I'm facing is filtering results by a date range (e.g., documents from the last 7 days). The n8n "Supabase Vector Store" node calls the standard match_documents function, which filters metadata using the metadata @> filter operator. While this works perfectly for exact key-value matches entered in the node's "Metadata Filter" options, it seems unsuitable for range comparisons on values (like dates) stored inside the JSONB. Given these constraints with the standard function and the n8n node, has anyone found an effective alternative or workaround? I need to combine the vector similarity search with filtering by a date range stored in the metadata, preferably within the n8n context. Looking for suggestions – perhaps SQL function modifications that are compatible, or different n8n approaches beyond the basic Supabase node? Thanks for any insights!

Hey everyone, I’m working on a workflow in N8N connected to VAPI to create an AI agent that can answer calls on my behalf. However, I want to integrate it with my own existing phone number rather than using a virtual number. My goal is for the AI agent to automatically answer calls from unknown numbers (numbers not saved in my contacts), while allowing me to handle calls from known contacts myself. Has anyone built a similar use case, or does anyone have ideas on how to set this up so that the AI agent answers calls on my actual phone number?