July 2023 Summaries

Milvus is an open-source database designed for vector search, offering robust scalability for various loads. It's ideal for machine learning deployments and includes best-of-class tooling like the JavaScript client. In this tutorial, we'll guide you through setting up a development environment with Milvus Lite and the Milvus node.js SDK(Client). We'll cover connecting to a server, creating databases and collections, inserting data, performing queries and searches, and more. With these tools, working with vector data in JavaScript using Milvus becomes simple and efficient.

Vector databases, crucial infrastructure for AI applications and large language models (LLMs), have gained widespread attention from a broader user base. Unlike traditional relational or NoSQL databases that store structured data, vector databases are purpose-built to store and manage unstructured data in numeric representations called embeddings. They enable similarity searches using the approximate nearest neighbor (ANN) algorithm, making them valuable for various use cases such as recommender systems, anomaly detection, and question-and-answer systems. The democratization of vector databases is essential to make progress in AI technology. However, only some developers have equal access due to barriers like proprietary technology, complex architecture and deployment, high costs, and poor user experience. To improve vector database democratization, it's crucial to evangelize knowledge, expertise, and technologies; open the source code to all developers; provide fully managed vector database services; offer free cloud options for individual developers and small teams; and prioritize a great user experience that meets users' needs. Choosing the right vector database for your project can be challenging due to the many available options. VectorDBBench, an open-source benchmarking tool, thoroughly evaluates and compares different vector database systems based on critical metrics such as queries per second (QPS), latency, throughput, and capacity.

Yujian Tang, a developer advocate at Zilliz, has an extensive background in computer science, statistics, and neuroscience. He previously worked as a software engineer at Amazon and researched machine learning. Tang chose to join Zilliz due to their focus on vector databases and the company's commitment to open-source ethos. As a developer advocate, he works with cutting-edge AI technologies and hosts meetups and conferences. He encourages others interested in DevRel roles to consider joining Zilliz.

Zilliz Cloud is a comprehensive vector database service that accelerates AI and analytics applications at scale. It's built on Milvus, an open-source vector database capable of handling billions of vector embeddings. The use cases for Milvus and Zilliz Cloud are broad and varied, including powering recommendation systems and building AI models in healthcare. The Zilliz REST API provides methods for managing clusters, collections, and vector data, allowing users to create, list, describe, drop, insert, delete, query, and search collections.

Zilliz recently launched an updated version of its cloud platform, introducing new features such as a free tier, dynamic schema and partition keys, and more affordable pricing plans. The latest update has significantly improved performance, making it twice as fast as the previous version and three to ten times faster than other vector databases like Milvus. Zilliz Cloud's speed is attributed to its robust vector indexing engine, optimized code structure, and AutoIndex feature for stable recall rates.

The text discusses the importance of machine learning models and their corresponding embeddings, which are high-dimensional vectors that provide an abstract way to represent input data in the model. It explains how embeddings have been used in various applications such as image recognition and semantic search. The author shares their personal journey working with embeddings and vector search, highlighting their experiences at Yahoo and a startup they founded. They also discuss Zilliz's mission to build an affordable and scalable vector search solution for the enterprise AI infrastructure market. The text ends by inviting readers to join Zilliz in its efforts to democratize enterprise AI infrastructure.

Milvus has released versions 2.2.10 and 2.2.11, which include enhancements to improve functionality and user experience. Updates have been made based on community feedback, with a focus on performance and security improvements. The latest versions introduce the 'FlushAll' function and Database API for RBAC capabilities, optimize disk usage for RocksMq by enabling zstd compression, and replace CGO payload writer with Go payload writer to reduce memory usage. Additionally, several bug fixes and performance enhancements have been made in these releases.

The democratization of technology refers to making it widely available and accessible, particularly in the context of software engineering. This involves using one's knowledge to simplify the creation, adoption, and understanding of technological advances for others. In this article, the author discusses the process of democratizing vector databases, which are complex tools that have traditionally only been available to developers at large enterprises. The author highlights three pillars of technology democratization: education, increasing accessibility, and evangelism. By open-sourcing projects like Milvus, providing educational resources, and offering free tiers for cloud services, companies can help expand the adoption of vector databases and other advanced technologies.

Filip Haltmayer, a software engineer at Zilliz in Redwood City, California, shares his journey into the company that leads in AI and vector search technology. His passion for software engineering led him to focus on distributed systems and machine learning during university. After graduation, he worked on personal projects in these areas before joining Zilliz. The technical interview with Zilliz aligned with his interests, and he was impressed by the team's intelligence and shared passion for pushing boundaries in vector search technology. Two years later, Haltmayer remains happy at Zilliz as it continues to grow and contribute significantly to the field of AI and vector searching.

Milvus, an open-source vector database, paired with PyMilvus - its Python SDK, is a powerful tool for handling large data sets and performing advanced computations and searches. This tutorial guides you in installing and setting up a development environment for using Milvus and PyMilvus. It then walks through example code for analyzing audio files, storing their data in Milvus, and using it to compare audio samples for similarities. The setup includes creating a virtual environment, installing Python dependencies, starting Redis, and installing and starting Milvus Lite. Finally, the tutorial demonstrates how to connect to Redis and Milvus, create a collection, store audio data, and search for similarities.

Facebook's AI Similarity Search (FAISS) is a library that provides efficient and reliable solutions to similarity search problems, especially when dealing with large-scale data. It functions on the concept of "vector similarity" and can handle millions or even billions of vectors quickly and accurately. FAISS has various applications, from image recognition and text retrieval to clustering and data analysis. To set up FAISS, you need Conda installed on your system. Once installed, FAISS can be used for tasks such as searching for similar text data in the Stanford Question Answering Dataset (SQuAD). Best practices include understanding your data, choosing the right index, preprocessing your data effectively, batching your queries, and tuning your parameters. Compared to FAISS, purpose-built vector databases like Milvus offer more advanced capabilities for scalable similarity search and AI applications.

In a recent webinar, Harrison Chase and Filip Haltmayer discussed retrieval techniques for accessing the most relevant context for large language model (LLM) applications. Retrieval involves extracting information from connected external sources and incorporating it into queries to provide context. Semantic search is one of the most critical use cases for retrieval, which functions within a typical CVP architecture (ChatGPT+Vector store+Prompt as code). The webinar also covered edge cases of semantic searches, such as repeated information, conflicting information, temporality, metadata querying, and multi-hop questions. Various solutions to these challenges were proposed during the discussion.