LLM Architecture Diagram and Various Steps
Last updated
Last updated
Now that we've explored the various components that make up the architecture of Large Language Models (LLMs), let's dive into how Retrieval-Augmented Generation (RAG) can work synergistically with these components of an LLM architecture. The aim is to show you how RAG can supercharge an LLM's capabilities by seamlessly integrating real-time or static data sources into the information retrieval and generation processes.
For a nuanced understanding of how Retrieval-Augmented Generation (RAG) optimizes Large Language Models, we'll delve into the essential elements and procedural steps that comprise the LLM architecture.
Data Sources: Whether your starting point is cloud storage, Git repositories, or databases like PostgreSQL, the first task is to bring these varied data forms together through pre-configured connectors.
Dynamic Vector Indexing: Text from these data sources is broken down into smaller segments (also called "chunks") and converted into vector representations. Models specialized for text embeddings, such as OpenAI's text-embedding-ada-002, are employed here. These vectors are continuously indexed to facilitate rapid search later on.
Query Transformation: A user’s input query is likewise transformed into a compatible vector representation, ensuring that it can be effectively matched with the indexed vectors for data retrieval.
Contextual Retrieval: Algorithms like Locality-Sensitive Hashing (LSH) are applied to find the closest matches between the user query and the indexed data vectors, staying within the model's token limitations.
Text Generation: With the retrieved context, foundational LLMs like GPT-3.5 Turbo or Llama-2 employ techniques from the Transformer architecture, such as self-attention, to generate an appropriate response.
User Interface: Finally, the generated text is presented to the user via interfaces like Streamlit or ChatGPT.