April 2023 Summaries

Milvus version 2.2.6 has been released with critical issues addressed from version 2.2.5. The new release includes bug fixes and performance enhancements, as detailed in the release notes. Users are advised to upgrade to this version for improved functionality. Key resources include PyPI package, documentation, Docker image, and GitHub release page.

LangChain is a framework designed to enhance the capabilities of Large Language Models (LLMs) by enabling users to chain together different computations and knowledge. It allows for the creation of domain-specific chatbots, action agents for specific computation, and more. Milvus, an open-source vector database, plays a crucial role in LangChain's integration as it enables efficient storage and retrieval of large documents or collections of documents. The integration involves extending the VectorStore class to implement functions such as add_texts(), similarity_search(), max_marginal_relevance_search(), and from_text(). However, challenges arise due to Milvus' inability to handle JSON natively, which may require additional work when dealing with existing collections or inserting data. Overall, LangChain offers a promising solution for improving LLMs' usefulness by providing working memory and knowledge base integration.

ChatGPT is an impressive technology that enables developers to create game-changing applications. However, the performance and cost of language model models (LLMs) are significant issues that hinder their widespread application in various fields. To address this issue, a cache layer called GPTCache was developed for LLM-generated responses. This caching layer is similar to Redis and Memcache and can decrease expenses for generating content and provide faster real-time responses. With the help of GPTCache, developers can boost their LLM applications 100 times faster. The cache system reduces the number of ChatGPT calls by taking advantage of temporal and spatial locality in user access for AIGC applications.

GPTCache is an open-source semantic cache designed to improve the efficiency and speed of GPT-based applications by storing responses generated by language models. It allows users to customize the cache according to their needs, including options for embedding functions, similarity evaluation functions, storage location, and eviction policy management. The tool supports multiple popular databases for cache storage and provides a range of vector store options for finding the most similar requests based on extracted embeddings from input requests. GPTCache aims to provide flexibility and cater to a wider range of use cases by supporting multiple APIs and vector stores.

Zilliz Cloud has introduced Backup and Restore feature, allowing users to easily back up their important data and restore it in case of an unexpected loss. The new feature provides easy-to-use interface, automated backups, secure storage, and efficiency for big data. Access to this feature is limited to enterprise users subscribing to the Zilliz Cloud Enterprise plan. Future plans include flexible recovery options, point-in-time recovery, and cross-region backup. Backup & Restore are now available for Zilliz Cloud Enterprise Plan with pricing based on usage: $0.025 GB and free storage retention within 30 days.

Zilliz has introduced a new migration feature that allows customers to seamlessly move their local Milvus database to the fully managed cloud service, Zilliz Cloud. This feature ensures data safety and security during the migration process. The migration tool is free for Enterprise users subscribed to the Zilliz Cloud enterprise plan. Users can start a 30-day free trial with $100 worth of credit to explore the new features in Zilliz Cloud.

Zilliz Cloud, a vector database-as-a-service, is designed to help developers focus on creating innovative AI applications by handling the infrastructure and storage of embeddings. The platform offers multi-cloud and multi-region availability, currently supporting AWS and Google Cloud with plans for future expansions. Users can quickly scale their vector search storage capacity without re-provisioning hardware and benefit from streamlined procurement, consolidated billing, and leveraging pre-committed AWS spend through the AWS Marketplace. Zilliz Cloud is also available on Google Cloud as an official partner, with more regions and cloud providers planned for future releases to support developers' diverse needs.

Zilliz has introduced OSS Chat, a chatbot designed to provide technical knowledge about open-source projects. Built using OpenAI's ChatGPT and a vector database, the service currently supports Hugging Face, Pytorch, and Milvus but plans to expand to more projects in the future. The new AI stack, called CVP Stack (ChatGPT+Vector database+prompt-as-code), is aimed at overcoming ChatGPT's limitations by using a vector database for accurate information retrieval. OSS Chat demonstrates this approach by leveraging GitHub repositories and their associated docs pages as the source of truth, converting data into embeddings, and storing them in Zilliz. When users interact with OSS Chat, it triggers a similarity search in Zilliz to find relevant matches and feeds the retrieved data into ChatGPT for precise responses.

Zilliz has announced the general availability of an update to its cloud vector database service, raising the standard for usability, security, performance, and capability. The latest version supports billion-scale vector collections and offers a 2.5x reduction in search latency compared to the original release. Additionally, Zilliz Cloud is now available on Google Cloud Platform (GCP) and AWS Marketplace. New features include rolling upgrades, backup and restore functionality, recycler bin for data security, and database migration toolkits from open-source Milvus.