Understanding Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation (RAG) is a powerful technique that enhances the capabilities of Large Language Models (LLMs) by grounding their responses in external, up-to-date information. Instead of relying solely on the knowledge embedded within their training data, RAG systems first retrieve relevant information from a knowledge base and then use this information to inform the generation process.
The Two Core Components of RAG
RAG operates through two primary phases: Retrieval and Generation. These phases work in tandem to produce more accurate, contextually relevant, and factually grounded outputs.
Phase 1: Retrieval
The retrieval phase is responsible for finding the most relevant pieces of information from an external knowledge source based on the user's query. This knowledge source is often a collection of documents, a database, or a website. The process typically involves converting the user's query and the documents into numerical representations called embeddings, which capture their semantic meaning. A similarity search is then performed to identify the documents or text chunks whose embeddings are closest to the query's embedding.
Embeddings are numerical fingerprints of text that capture meaning.
Text is transformed into vectors (lists of numbers) that represent its semantic content. Similar texts will have similar vectors.
The core of the retrieval process relies on embedding models. These models, often neural networks, take text as input and output a fixed-size vector (a list of numbers). The training of these models is designed such that texts with similar meanings are mapped to vectors that are close to each other in a high-dimensional space. When a user asks a question, their query is also converted into an embedding. Then, a similarity metric (like cosine similarity) is used to find the embeddings of documents or text chunks in the knowledge base that are closest to the query embedding. This effectively retrieves the most semantically relevant information.
Phase 2: Generation
Once relevant information has been retrieved, the generation phase takes over. The LLM receives the original user query along with the retrieved context. It then synthesizes this information to formulate a coherent and informative response. By providing the LLM with specific, relevant data, RAG helps to mitigate issues like hallucination and ensures that the generated output is grounded in factual information.
RAG acts like giving an LLM a 'cheat sheet' of relevant facts before it answers a question.
The RAG process can be visualized as a pipeline. First, a user query enters the system. This query is then used to search a knowledge base (e.g., a collection of documents). The search retrieves relevant text snippets. These snippets, along with the original query, are then fed into a Large Language Model (LLM). The LLM processes this combined input to generate a final answer. This iterative process ensures that the LLM's output is informed by external, up-to-date information, making it more accurate and reliable.
Text-based content
Library pages focus on text content
The Role of Vector Databases
Vector databases are crucial infrastructure for RAG systems. They are specifically designed to store and efficiently query high-dimensional vector embeddings. Unlike traditional databases that store structured data, vector databases excel at performing similarity searches, making them ideal for the retrieval phase of RAG.
To efficiently store and query vector embeddings for similarity searches.
Benefits of RAG
RAG offers several significant advantages: it improves factual accuracy, reduces hallucinations, allows LLMs to access real-time or proprietary data, and provides a mechanism for citing sources. This makes LLM applications more trustworthy and useful in a wider range of scenarios.
Learning Resources
The foundational research paper that introduced the concept of RAG, explaining its architecture and benefits for NLP tasks.
A clear and concise explanation of RAG, its components, and why it's important for modern AI applications.
Explains the concept of vector databases and their role in enabling semantic search and AI applications like RAG.
Official documentation from LangChain, a popular framework, detailing how to implement RAG for question answering.
A deep dive into what vector databases are, how they work, and their applications, particularly in the context of AI and RAG.
A practical guide to RAG, covering its architecture, benefits, and use cases with clear explanations.
Explains the fundamental concept of embeddings in machine learning, which is critical for understanding the retrieval phase of RAG.
Details the mechanics of vector search, a core technology powering vector databases and RAG systems.
Learn how to build RAG applications using LlamaIndex, another popular framework for data-augmented LLM applications.
An overview of vector databases, their purpose, and how they facilitate efficient similarity search for AI models.