RAG Me Up is a generic framework that enables you do to RAG on your own dataset easily. Its essence is a small and lightweight server and a couple of ways to run UIs to communicate with the server.
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A lot of you know me for talking a lot about AI and implementing AI solutions daily. Quite often I do get asked what exactly it is we can do with AI and in particular LLMs and GenAI beyond the default examples of chat and RAG.
Well here is such an example: an application called Job Fair that uses LLMs, RAG and GenAI together with non-standard AI algorithms for extracting personality and skills from CVs and job postings. The result is a chat interface that allows you to talk to an LLM while simultaneously access the extracted metadata like personality and skills... and have the LLM reason about those too!
A couple of features that are interesting to highlight:
🏅 This can be run using commercial LLMs like ChatGPT, Gemini and Claude or locally/on-prem with any OS LLM like LLaMa3 or Phi
⛔ Safeguards are built in to prevent the model from answering any questions related to race, gender, ethnicitiy or any other demographics
🧠 Yet the model is allowed to use the other textual information present ánd tap into the personality and skill information extracted by another AI
🗂️ While model-agnostic, using reasonably strong LLMs allows to extract any type of information (eg. education, level of expertise for a skill) from the CVs and job description that would require expensive and complex parsers before
