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Hey guys So I previously built a chatbot with the help of @Mirko Salzer in Flowise. The knowledge base for the chatbot I upserted into a Supabase Vector Database, so the chatbot queries the database and thereby answers the user's question. The chatbot worked without issues. However, now that I have exported the chatflow from my Flowise instance so I can import it into a business' Flowise instance I receive an error message when chatting with it. It does apparently not read my database and retrieve any information. Kinda weird since it worked in my own instance with the exact same SQL code. - You can see my Flowise chatflow in picture 1. - You can see the error message in picture 2. - You can see the SQL Editor code I used to set up my Supabase DB. Any clues? :) I have tried running it through chatgpt and will keep doing so - but it just doesn't make much sense.

Hey guys 👋 I'm trying to figure out how I connect my n8n instance with the Google Maps Directions API and ask for an effective route based on a list of addresses 🗺️ So my workflow is intended to work like this: - A have a spreadsheet full of clients with their corresponding addresses, and last inspection dates. - Each client needs inspection of their facilities on a given time each year. - I now want to connect a chat trigger to an AI agent, that has the spreadsheet and the Google Maps Direction API as tools. Based on these tools it should be able to read the 'last inspection date" and address from the sheet and based on my demand from the chat, it should reply with a list in chronological order displaying which clients to visit based on their address and last inspection date. All I know is that I have to connect the Google Maps Directions API by using an HTTP request node - and that's it 😳 Have any of you guys had any luck with this? 🙏

So I've been wondering if I manually have to update the node version for my Flowise instance, or if clicking the "Sync fork" once in a while on Github will do the work? I guess at some point node versions will reach their End of Life and needs to be updated.... What do you guys say?

How would you solve a case like this? CASE: I have a spreadsheet with customer questions in one column and a categorization of the customers' questions in a second column. Once a month, I want my agent to create a report based on the information in the spreadsheet. The questions are divided into 11 categories. In the report, I would like to have a section/paragraph for each category along with the most frequently asked questions within each respective category. Now, the spreadsheet can vary from a couple of 100s to 1000s of questions. In each category section in the report, I would like to highlight the 10 mest FAQs. Here are my thoughts: ❌ If I insert all questions from the spreadsheet into ONE AI-agent node (let's say 1000 questions), it will (in my mind) with 100% certainty fail, because it's simply processing to many questions and categories. ❌ If I make a filter and filter all questions by category and then based on the category activate a separate sub-flows (11 subflows because of the 11 categories), I will have to switch between 11 sub-flows when making changes or handling errors. This, in my mind doesn't sound like the solution. ✅ This is what I'm thinking - everything inside the same workflow. See the picture (It's not done). Trigger --> Spreadsheet 1000 (or more) questions --> filter (by category) --> If statement (only passes 100 questions per category (to save token usage)) --> separate AI agents for finding FAQ's per category --> finally merging everything together into one JSON output. AND, instead of running all agents simultaneously (and hit the TPM barrier) each agent will have a delay, so the agent before it is done analysing FAQ's within its category before the second initializes. Do you guys have another, maybe more simple solution to this?