This video demonstrates building an AI-powered email search system using a chatbot. The goal is to overcome the limitations of traditional email search by providing contextually relevant responses based on the content of emails, regardless of volume. The tutorial utilizes n8n (a low-code workflow automation tool) and explores both no-code and code-based approaches.
The chatbot interface is built using Python with the Streamlit library.
The video mentions that while many of the tools used can be free (like n8n if self-hosted, and Streamlit), there are associated costs. Specifically, using the OpenAI API for embeddings and queries incurs costs, and the vector database also has associated costs (though alternatives like Pinecone or Superbase are mentioned). Installing an LLM locally on your disk might be possible for certain models, but the video focuses on using cloud-based APIs for simplicity and scalability, and doesn't go into detail about self-hosting LLMs for this specific application. Therefore, achieving this entirely for free, especially with significant email volume, is unlikely due to the costs associated with API usage and database hosting.
The video explains that the system can integrate with various email providers, including Office 365 and Gmail. For Hotmail and Gmail, which are hosted on their respective servers, the process would involve connecting to these services through APIs.
In the context of the n8n workflow shown, the speaker connects to an Outlook account using n8n's built-in integrations. The same principle applies to Gmail. You would typically use n8n's Gmail nodes or create custom API integrations to access your Gmail and Outlook (Hotmail) inboxes. This allows the workflow to fetch emails, process them, and then create embeddings for storage in the vector database.
To build this AI email search application, you would need the following tools and applications:
The video doesn't give a specific time estimate for building the entire app from scratch, as it depends heavily on your familiarity with the tools and the desired level of customization.
However, the creator provides pre-built workflows for n8n and a code repository. If you download and adapt these resources, the setup time could be significantly reduced. The creator suggests that following the provided setup instructions should be "relatively straightforward."
For someone with experience in n8n, Python, and setting up databases, it might take a few hours to a couple of days to get a functional version running and customized. For those newer to these technologies, it could take longer as they learn and troubleshoot.
