June 2023 Summaries

Zilliz Cloud offers three types of Compute Units (CUs) - Performance-optimized, Capacity-optimized, and Cost-optimized. The Performance-optimized CU is ideal for rapid response time applications with high throughput requirements such as Generative AI, Recommender systems, Search engines, Chatbots, Content moderation, Augmenting LLMs' knowledge base, and Anti-fraud systems. Capacity-optimized CUs are suitable for handling large-scale unstructured data searches like text, images, videos, and molecular structures, copyright violations detection, and identity verification. Cost-optimized CUs are perfect for offline tasks with a tight budget but higher search latency. The performance comparison shows that the Performance-optimized CU outperforms others in terms of latency and throughput. Capacity evaluation results indicate that capacity-optimized and cost-optimized CUs have equal capacities, five times larger than the performance-optimized CU. Examples are provided to help businesses choose the most suitable option for their needs.

This article discusses the challenges and solutions in building applications using large language models (LLMs) such as OpenAI's ChatGPT. The three main challenges are high costs, lack of up-to-date information, and need for domain-specific knowledge. Two proposed frameworks to address these issues are fine-tuning and caching + injection. LlamaIndex is a powerful tool that can abstract much of the latter framework. The article introduces LlamaIndex as a "black box around your Data and an LLM" and explains its four main indexing patterns: list, vector store, tree, and keyword indices. It then demonstrates how to create and save a persistent vector index using LlamaIndex with both local and cloud vector databases (Milvus Lite and Zilliz). In summary, the article provides an overview of LlamaIndex, its applications in LLM-based applications, and offers guidance on creating and managing persistent vector store indices for real-world use cases.

The combination of LangChain and Milvus can enhance ChatGPT's intelligence and efficiency by harnessing vector stores' power. LangChain is a framework for developing applications powered by language models, while Milvus is a vector database that enables semantic search functionality. By integrating these tools, developers can create more reliable AI-Generated Content (AIGC) applications and address hallucination problems in ChatGPT. Additionally, using GPTCache and fine-tuning embedding models and prompts can improve the performance and search quality of AIGC applications.

The latest version of Zilliz Cloud introduces design optimizations to improve the product experience. Key updates include prioritizing ease of use, streamlining workflows with clear guidance, valuing user feedback, ensuring visually enjoyable experiences, and offering a smooth user journey. These enhancements aim to provide users with an intuitive interface and seamless navigation while using Zilliz Cloud's vector retrieval capabilities. The company encourages users to share their suggestions or ideas for further improvements through the support portal, LinkedIn, Twitter, or by contacting engineers directly.

This tutorial demonstrates how to use Large Language Models (LLMs) like GPT in production by querying multiple documents using LlamaIndex, LangChain, and Milvus. The process involves setting up a Jupyter Notebook, building a Document Query Engine with LlamaIndex, starting the vector database, gathering documents, creating document indices in LlamaIndex, performing decomposable querying over your documents, comparing non-decomposed queries, and summarizing how to do multi-document querying using LlamaIndex. The use of decomposable queries allows for breaking down complex queries into simpler ones that can be answered by a single data source.

Zilliz Cloud, a cloud service offering fast and scalable vector retrieval capabilities, has introduced the Organizations and Roles feature to simplify team access and permission management. The new feature includes three roles: Organization Owner, Organization Member, and Project Owner, each with unique access and permissions. This update aims to improve collaboration, security, and flexibility in users' workflows. To get started, users can sign up for a free account or log into their existing one, create an organization, invite new members, and manage billings collectively.

The new open-source Vector Database Benchmark Tool is designed to help developers choose the ideal vector database for their projects. This tool enables users to measure performance across critical metrics and compare different options. Key features include flexibility, realistic workload simulation, interactive reports and visualization, and open-source community collaboration. VectorDBBench, written in Python, supports six vector databases: Milvus, Zilliz, Pinecone, Weaviate, Qdrant, and Elasticsearch. Users can download the tool from GitHub and install it using pip. The tool is actively maintained by a community of developers committed to improving its features and performance.

Zilliz Cloud has released an update that introduces new features and more affordable pricing options, making it accessible to all developers regardless of budget. The latest release includes a free tier option with up to two collections handling 500,000 vectors each. Various pricing plans are available: Starter, Standard, Enterprise, and Self-hosted. A new Cost-Optimized CU offers the same storage capacity as the existing Capacity-Optimized CU but costs about 30% less. The Organizations and Roles feature enables users to manage team access and permissions easily. Zilliz Cloud now supports JSON data types, enabling users to store and manage JSON data alongside Approximate Nearest Neighbor (ANN) Search capabilities. Dynamic schema support is also available. A new benchmark tool, VectorDBBench, allows users to measure the performance of vector database solutions against other offerings in the market with their data.

The recent emergence of Language Learning Models (LLMs) has introduced new tools, such as the LLM framework called LangChain. This versatile tool offers various features like different prompting methods, maintaining conversational context, and connecting to external tools. Prompting is a crucial task in building AI applications with LLMs, and this article extensively explores how to use LangChain for more complex prompts. The text covers: 1. Simple Prompts in LangChain: This section demonstrates the basic usage of LangChain prompting by creating a single prompt using the `PromptTemplate` object. It also explains how to add an LLM and create an `LLMChain`. 2. Multi Question Prompts: The article shows how to handle multiple questions within a single prompt using the same `PromptTemplate` object. 3. Few Shot Learning with LangChain Prompts: This section introduces "few shot learning," where users can teach AI how to behave by providing examples of desired responses. It demonstrates this feature using the `FewShotPromptTemplate`. 4. Token Limiting Your LangChain Prompts: To manage token usage and keep costs down, the article explains how to use the `LengthBasedExampleSelector` object to limit tokens in queries. 5. A Summary of Prompting in LangChain: The text concludes by summarizing the key points covered in the article about prompting with LangChain.

Auto-GPT is an open-source, autonomous AI application that utilizes large language models (LLMs) to perform tasks such as browsing the internet, speaking via text-to-speech tools, writing code, and keeping track of its inputs and outputs. It has garnered significant attention due to its potential for automating mundane tasks and enhancing productivity. This article provides a comprehensive guide on setting up Auto-GPT, configuring it, running tasks, and adding memory using Milvus vector database. The integration of Milvus as a backend storage solution allows users to search, retrieve, or edit data more efficiently than the default JSON file format.

The Milvus community has released Milvus 2.2.9, which includes new features such as JSON support, dynamic schema handling, and partition key usage. Additionally, the update allows for more efficient resource utilization by removing the limit on the number of partitions. Bug fixes and performance enhancements are also included in this release. For a complete list of changes, check out the release notes.