Large language models (LLMs) have emerged as transformative tools, unraveling the complexities of natural language understanding and paving the way for modern applications. The primary purpose of this Refcard is to provide an end-to-end understanding of LLM architecture, training methodologies, as well as applications of advanced artificial intelligence models in natural language processing. Offering an introduction and practical insights on how to navigate the intricacies of harnessing LLMs, this Refcard serves as a comprehensive guide for both novices and seasoned practitioners seeking to unlock the capabilities of these powerful language models.
Attribute filtering, or simply "filtering," is a basic function desired by users of vector databases. However, such a simple function faces great complexity.
Suppose Steve saw a photograph of a fashion blogger on a social media platform. He would like to search for a similar jean jacket on an online shopping platform that supports image similarity search. After uploading the image to the platform, Steve was shown a plethora of results of similar jean jackets. However, he only wears Levi’s. Then the results of image similarity search need to be filtered by brand. But the problem is when to apply the filter? Should it be applied before or after approximate nearest neighbor search (ANNS)?
In the previous two posts in this blog series, we have already covered the system architecture of Milvus, the world's most advanced vector database, and its Python SDK and API.
This post mainly aims to help you understand how data is processed in Milvus by going deep into the Milvus system and examining the interaction between the data processing components.
Zilliz has been a pioneer who dedicated itself to enabling users in the face of a rapidly growing demand for unstructured data processing. Zilliz has now open-sourced a graphical user interface, Attu, specifically for Milvus 2.0, an AI-oriented vector database system designed for massive production scenarios. In this article, we would like to show you step by step how to perform a vector similarity search with Attu and Milvus 2.0.
Attu (/ ˈætu /) is an island on the west edge of the Aleutian Islands. This uninhabited realm symbolizes an adventurous spirit.
In comparison with Milvus CLI, which brings the uttermost simplicity of usage, Attu features more:
In this introductory article, we’ll introduce concepts related to the vector database, a new type of technology designed to store, manage, and search embedding vectors. Vector databases are being used in an increasingly large number of applications, including but not limited to image search, recommender system, text understanding, video summarization, drug discovery, stock market analysis, and much more.
Relational Is Not Enough
Data is everywhere. In the early days of the internet, data was mostly structured, and could easily be stored and managed in relational databases. Take, for example, a book database:
