Building an AI Assistant using LLM and Retrieval Augmentation Generation

In the realm of artificial intelligence, the use of advanced models like Large Language Models (LLM) combined with innovative techniques such as Retrieval Augmentation Generation (RAG) has paved the way for the development of intelligent AI assistants. Let's delve into the architecture and functionalities of an AI assistant built using LLM and RAG.

RAG Architecture

The Retrieval Augmentation Generation (RAG) architecture combines the strengths of retrieval-based and generation-based models. It involves retrieving relevant information from a large database using a retriever model, augmenting the retrieved information with additional context, and then generating a coherent response using a generator model.

Get Context from Vector DB

One of the key components of the RAG architecture is the retrieval of context from a Vector Database. The Vector DB stores a vast amount of information in vectorized form, allowing the AI assistant to efficiently retrieve relevant context based on the user's query.

Get Answer from LLM

After obtaining the necessary context from the Vector DB, the AI assistant leverages a Large Language Model (LLM) to generate accurate answers to the user's queries. The LLM is trained on a diverse range of data and can effectively understand and respond to natural language inputs.

Response Formation

Once the context is retrieved and the answer is generated, the AI assistant forms a coherent response by combining the retrieved context with the generated answer. This ensures that the response provided to the user is informative and relevant to their query.

Function Calling

Finally, the AI assistant utilizes function calling to execute specific tasks or actions based on the user's request. By integrating function calling capabilities, the AI assistant can perform a wide range of functions, from providing information to executing commands.

By harnessing the power of LLM and RAG, AI assistants can offer intelligent and personalized interactions, making them invaluable tools in various domains such as customer service, education, and research.