System Design of ChatGPT | Mock interview @gkcs | Highlights and Annotations by Gistr.

Gorav Sa, interviewed on "sudo Code Again," outlines designing a ChatGPT-like system. Key aspects include a web crawler indexing data into an S3-based file system, creating embeddings stored in a sharded, replicated vector database (using HNSW for search), pre- and post-processing layers for query handling, caching mechanisms for both user and global queries, and consideration of response latency and model selection. The design emphasizes scalability and efficient query handling. This segment details the initial requirements gathering and scoping for designing a system similar to ChatGPT, focusing on response time, concurrent chats, chat history limits, and the trade-off between speed and accuracy in response generation. The discussion clarifies the constraints and priorities for the system's design, setting the stage for a practical and efficient solution. This segment focuses on the initial stages of data acquisition for training a large language model, covering web crawling techniques, data storage using cloud-based object storage (like AWS S3), and the rationale behind choosing a specific storage solution based on cost-effectiveness and suitability for storing large files like HTML pages. This segment explains the process of creating embeddings for text data using techniques like word tokenization and vector representation. It introduces the concept of vector databases and their advantages in efficiently storing and searching high-dimensional data, crucial for fast response times in a large language model. The discussion also touches upon the use of Apache Spark or similar AI-optimized farms for parallel processing of large datasets. This segment details a crucial aspect of system design for large language models: handling duplicate queries from different users. The speaker proposes a two-tiered caching system—a local, user-based cache and a global query cache—to reduce redundant computations. The discussion includes using vector embeddings to compare query similarity, setting thresholds for cache inclusion, and acknowledging the trade-off between cache efficiency and potential response quality degradation. The speaker thoughtfully considers the challenges of managing a global cache for semantically similar queries while minimizing storage waste. This segment delves into the challenges of scaling vector databases to handle massive datasets and efficient search algorithms. It introduces the concept of sharding and replication for horizontal scalability and discusses the use of algorithms like Hierarchical Navigable Small World (HNSW) for fast nearest-neighbor search in high-dimensional spaces. The discussion highlights the importance of efficient indexing and data partitioning for optimal performance.