LLM Architecture Diagram and Various Steps
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.
LLM Architecture
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.
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