RAG in LLM

It's a good new year! How was everyone's vacation? How was your first week back at work?

Last month we discussed separating front-end and back-end web applications in React. In this post, I'm going to share some information about RAG in the Large Language Model LLM and the impact it may have on your daily work.

When discussing the integration of RAG (Rapid Adaptation Generator) into LLM, we delve into an exciting realm within the field of Natural Language Processing.

RAG can be simply defined as an advanced language model crafted to respond to user queries more flexibly and adaptively.

Advantages of RAG

Rapid Adaptation: RAG is engineered for swift adaptation to new information. It possesses the capability to consult external (or internal) resources while posing a question, thereby delivering more precise and real-time responses.

Controlled Retrieval Capabilities: In today's rapidly evolving AI landscape, the security and privacy of information and data are paramount. Questions like "Is my chat on ChatGPT secure?" highlight this concern. While GPT exhibits robust retrieval capabilities, tapping into vast amounts of text, there is a growing demand for a secure AI usage paradigm. Responding to this need, locally-run programs like "PrivateGPT" on GitHub leverage RAGs, enabling users to furnish documentation or data for LLM to comprehend and draw more effective inferences.

Features of LLM

Powerful Performance of LlamaCpp: LLM employs modern deep learning techniques, and pre-training to generate potent representations for improved text understanding and processing.

Utilization of Embeddings: LLM utilizes Embeddings to map text to a high-dimensional vector space, enhancing its ability to comprehend and represent semantic information. In my experience with LLaMA2 7B Chat, I observed that the Embedding function integrates LLM. For instance, the 'LlamaCppEmbeddings' I used necessitates passing the model into the Embedding function for construction, subsequently passing it through various text splitter methods to structure the data.

RetrievalQA Model:

Once vector data is obtained, the RetrievalQA model acts as a 'chain', utilizing the data retrieved by Embedding LLM as a searchable database, delivering more comprehensive and accurate answers.

Impact on Everyday Work

Increased Speed of Information Access: LLM with RAG significantly enhances the speed of information retrieval in daily work. Models efficiently retrieve information from both external texts and internal team knowledge bases, providing organizations with an easier means to deploy local datasets and ensure data security.

In conclusion, the amalgamation of RAG and LLM presents a smarter, more efficient tool for navigating the information age. The ongoing development and optimization of these models will further propel the application of natural language processing technology across various domains. In our daily work, we anticipate a surge in the applications of these technologies to effectively meet the escalating demand for information.