allansuzuki / llm_rag_model

Use case when you want to ask things based on certain documents

QA chatbot based on provided documents
Building a LLM model publicly available with RAG feature

The RAG (Retrieval-Augmented Generation) LLM (Large Language Model) is a game-changer in language processing. It's like your regular text generator, but with a superpower—it can pull in info from outside sources to make its output even smarter.

Now, there are two main components in the RAG world: the LLM Solo and the Retriever Solo. The LLM Solo does the talking, generating text like a champ. The Retriever Solo, on the other hand, is the expert at fetching relevant info from big knowledge bases.

This dynamic duo makes the RAG model a real powerhouse, especially for tasks like answering questions or summarizing stuff. It's all built on top of existing models like Meta's Llama2 and made super accessible through tools like Hugging Face.

Imagine the possibilities! From creating tailored and enhanced answers, RAG opens up a whole new world of possibilities for how we interact with language and information. It's like having a conversation with someone who's not just smart but also has know about what specific subject you are interested in, like enterprise documents, researches, ...

Let's jump in the solution and hands-on! :)

llm_w_rag_using_langchain.ipynb

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