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The n8n-nodes-bee-ai Package Blog Post: Bee AI n8n node Github Repo: AshDevFr/n8n-nodes-bee-ai Sub Workflows to fetch them all Bee AI - Fetch all Conversations Note: the Conversation model is a nested object, so because of that, I added a code node to flatten the results. Conversation (GET /v1/{userId}/conversations/{id} response): { "conversation": { "id": 3456789, "summary": "...", ... } } Bee AI - Fetch all Facts Bee AI - Fetch all Todos Bee AI - Fetch all Locations Workflows Bee AI - Backup v2 Uses the Fetch all x workflows to get all the data, then insert it into a MongoDB database anf a json file in Google Drive. ...

I recently developed a custom n8n node package that connects Bee AI with the n8n workflow automation platform, opening up new possibilities for automating personal AI data. What is Bee AI? Bee AI is a wearable personal AI device that sits quietly in the background, capturing and processing your daily interactions. Unlike traditional smart assistants that require explicit activation, Bee continuously learns from your conversations, tasks, and locations, transforming these moments into actionable insights, summaries, and reminders. ...

Self-RAG (Self-Reflective Retrieval-Augmented Generation) is a framework that combines the benefits of retrieval-augmented generation (RAG) with self-reflection, allowing large language models (LLMs) to adaptively retrieve passages on-demand and generate more accurate responses. Langgraph recently published a blog post on Self-RAG with their own implementation. I really like the idea of validating the retrieved information. In that past I have noticed that, while vector databases will return documents that usually match the input query string, some may contain irrelevant content. Having the ability to validate and correct this is a great feature for any RAG setup. ...