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From RAGs to riches: A practical guide to making your local AI chatbot smarter

Jun 16, 2024 - theregister.com
The article discusses the use of Retrieval Augmented Generation (RAG) technology in AI models, specifically in Large Language Models (LLMs) such as Llama3 or Mistral. RAG allows these models to interpret and transform information from an external database, which can be updated independently of the model. This means the LLM-based app can be improved without needing to retrain the model every time new information is added or old data is removed. The article provides a tutorial on how to use RAG to turn an LLM into an AI personal assistant capable of searching internal support docs and the web, using the Ollama LLM runner and the Open WebUI project.

The tutorial covers prerequisites, deploying Open Web UI using Docker, connecting Open WebUI to Ollama, downloading a model, integrating RAG, tagging documents, and testing the system. It also discusses how to use RAG and LLMs to search and summarize the web, similar to the Perplexity AI service. This involves using Google's Programmable Search Engine (PSE) API to create a web-based RAG system for querying specific articles or sites. The article concludes by reminding readers that LLMs can still make mistakes or potentially hallucinate, so it's important to check sources or block-list URLs that might provide incorrect information.

Key takeaways:

  • RAG (Retrieval Augmented Generation) technology is being used to make AI models more useful, allowing them to interpret and transform information from an external database.
  • Open WebUI can be used to turn an off-the-shelf LLM into an AI personal assistant capable of scouring internal support docs and searching the web.
  • Open WebUI's implementation of RAG can be used to search and summarize the web, similar to the Perplexity AI service.
  • Despite the advancements, it's important to remember that LLMs can still make mistakes or potentially hallucinate, so it's necessary to check your sources.
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