The purpose of this blog is to delve into a hot topic in the world of generative AI: Retrieval-Augmented Generation (RAG). Many of you may have already built RAG systems, some of which might be in production. We’ll explore the fundamental principles behind RAG, revisiting some concepts you may already be familiar with. However, we’ll present them in a structured sequence that clearly demonstrates these core concepts.
Topics
1. Vector Databases
2. Use-cases
3. Retrieval Augmented Generation at High Level
4. Retrieval Augmented Generation in Detail
1. Vector Databases: Storage for Generative AI
Before diving into RAG, it’s essential to understand vector databases. In real-world work environments, over 80% of the data we encounter is unstructured, including data like PowerPoint presentations, Word documents, images, and audio files. Vector databases excel at handling unstructured data by converting it into vector embeddings. These embeddings capture the underlying semantics, enabling more intuitive searches based on content and context.
2. Use-cases: Vector Database along with idea of Vector Search
RAG Systems like Chatbots: RAG systems enhance chatbots by using vector search to retrieve relevant information based on the semantic meaning of queries. This allows chatbots to provide accurate and contextually appropriate responses, improving user satisfaction and engagement with detailed and informative answers.
Recommendation Systems: Vector search is essential in recommendation systems, helping suggest products, services, or content based on users’ preferences and behaviors. By using vector embeddings, these systems can offer personalized recommendations, increasing engagement, conversion rates, and customer satisfaction.
Search Functionality: Vector search enhances search functionality by enabling more intuitive and context-aware information retrieval. It goes beyond keyword matches to find semantically similar results, offering more relevant search outcomes and a better user experience.
Personalization in Image and Video: Vector search is crucial for personalizing image and video recommendations. By understanding user preferences through vector embeddings, platforms can suggest visually similar content, enhancing user engagement and retention in streaming services and social media.
Document Retrieval and Knowledge Management: Employees often need to access specific documents like policy manuals, training materials, or project reports from a large repository. Vector databases help by storing these documents as vector embeddings, allowing users to find relevant information based on content rather than exact keywords. For example, an employee can search for a presentation on a particular project topic without knowing the exact title or file name.
3. Retrieval Augmented Generation at High Level
The diagram above provides a high-level representation of a RAG (Retrieval-Augmented Generation) system.
The basic idea is that a user has a task or input question and seeks an answer from a collection of embeddings stored in a vector database. This concept goes beyond basic vector search by accommodating a wide range of queries. Users might ask very specific questions, such as “What is X at this particular point in time?” or more complex questions requiring multi-step reasoning or task decomposition to address vague inquiries correctly.
The process begins with a user query, which could be a question or a prompt needing a response. An interface allows the user to input their question and receive an answer. The system retrieves relevant documents or data from a large corpus using a retriever model, which ranks documents based on their relevance to the query. The retrieved documents are then used to augment the context for the generation step, involving selecting and summarizing relevant parts of the documents for the generator model’s input. The final step involves generating the response using the augmented context. The generator model, typically a large language model, can be either closed source, like GPT-4, or open source, such as Meta’s LLAMA.
This architecture diagram illustrates the detailed steps of an actual RAG (Retrieval-Augmented Generation) pipeline. It comprises two main components: data parsing and ingestion, and data querying.
The data parsing and ingestion component introduces a novel ETL (Extract, Transform, Load) process for unstructured data. The key benefit of RAG is its ability to allow you to upload an entire collection of PDFs or other documents into the system, where a large language model (LLM), whether closed or open-source, can generate answers based on that data. The first step involves processing, parsing, and indexing the data, with further discussion on optimization techniques.
The second component, data querying, involves accessing the stored data—whether in a vector database, SQL database, or document store—via an API interface. This configuration enables the LLM to interact with the data, facilitating a flow tailored to the specific use case. For example, if the objective is to enable chat over your data, the LLM will engage with the vector database. Additionally, other tools or services can be integrated alongside the vector database, creating a comprehensive toolset to achieve the overall application goals.
Click here for part 2. I’ll be discussing about Challenges in RAG and how to improve from naive RAG pipeline.