I’m working on a chatbot project and need some help with a specific issue. I built a chatbot interface using Lovable that’s designed to help fitness trainers create personalized workout and diet plans. The setup works like this: when someone types a message, it gets sent to an N8N webhook, and then the bot responds with whatever comes back from that webhook. I’ve got a RAG system running behind it, which works pretty well for answering questions. But here’s the problem - each message is treated completely separately, so the bot doesn’t remember what we talked about earlier in the conversation. This makes the whole chat feel choppy and unnatural. Has anyone dealt with this before? Is there a way to make the webhook remember previous messages, or do I need to completely change how I’m building this thing?
I ran into something similar when building a customer service bot last year. The trick that worked for me was modifying the payload structure from Lovable to include conversation context. Instead of just sending the current message to your N8N webhook, you can maintain a conversation array in your Lovable frontend that gets passed along each time. Your N8N workflow then appends the new message to this history before feeding everything to your RAG system. The key is limiting the context window to maybe 8-12 exchanges to avoid hitting token limits while keeping memory usage reasonable. This approach requires minimal changes to your existing N8N setup since you’re essentially just expanding the input data rather than rebuilding the architecture.
yeah i had this exact issue with my discord bot. easiest fix is to pass a user_id or session_id with each webhook call and store chat history in a simple json file or database table. then just grab the last 5-10 messages before sending to your rag system. works pretty well without major changes to your current setup tbh
To enhance your chatbot’s ability to remember conversation history, it’s essential to implement session management. Since N8N webhooks are inherently stateless, storing the conversation history in a database or a quick-access memory store like Redis can be effective. Assign a unique session ID for each user and ensure your Lovable interface sends this ID with each message to the webhook. This allows your N8N workflow to retrieve past messages from storage, enabling the RAG system to reference the entire conversation instead of treating messages as isolated inputs. I encountered a similar challenge with a support bot, and I found that retaining the last few exchanges greatly improved the flow and coherence of interactions.