All about vector databases - their meaning, vector embeddings and best vector databases for large language models (LLM)

Large language models have recently shown tremendous growth and development. The field of artificial intelligence is booming with each new release of such models. From education and finance to healthcare and media, LLMs are contributing to almost every field. Famous LLMs such as GPT, BERT, PaLM and LLaMa, by mimicking humans, are revolutionizing the field of artificial intelligence. The well-known chatbot ChatGPT, based on the GPT architecture and developed by OpenAI, mimics humans by generating accurate and creative content, answering questions, summarizing massive text paragraphs, and translating language.

What are vector databases?

Vector databases are a new and unique type of database gaining huge popularity in the field of artificial intelligence and machine learning. Vector databases differ from conventional relational databases, which were originally designed to store tabular data in rows and columns, and from more modern NoSQL databases such as MongoDB, which store data as JSON documents. This is because vector embeddings are the only type of data that a vector database is designed to store and retrieve.

Large language models and increasingly new applications depend on vector embeddings and vector databases, which are specialized databases designed to efficiently store and manipulate vector data. Vector data, which uses points, lines, and polygons to describe objects in space, is often used in a variety of industries, including computer graphics, machine learning, and geographic information systems.

The vector database is based on vector embedding, which is a kind of data encoding that carries semantic information that helps AI systems interpret data and store it in long-term memory. These embeddings are compressed versions of the training data produced by the OD process. They serve as a filter used to process new data during the inference phase of the machine learning process.

In vector databases, geometric properties of data are used to organize and store them. Each item is identified by its coordinates in space and other properties that give it characteristics. A vector database, for example, can be used to record data about cities, highways, rivers, and other geographic features in GIS applications.

Advantages of vector databases

1. Spatial Indexing - Vector databases use spatial indexing techniques such as R-trees and Quad-trees to allow data retrieval based on geographic relationships such as proximity and boundedness, making vector databases superior to other databases.
2. Multidimensional indexing: In addition to spatial indexing, vector databases can support indexing by additional vector data qualities, allowing efficient searching and filtering on non-spatial attributes.
3. Geometric operations: For geometric operations such as intersection, buffering, and distance computation, vector databases often have built-in support, which is important for tasks such as spatial analysis, routing, and map visualization.
4. Integration with geographic information systems (GIS): To efficiently process and analyze spatial data, vector databases are often used in conjunction with GIS software and tools.

Best vector databases for LLM construction

In the case of large language models, vector databases are gaining popularity, with the main application being the storage of vector embeddings derived from LLM training.

1. Pinecone - Pinecone is a powerful vector database with high performance, scalability and the ability to handle complex data. It is ideal for applications requiring instant access to vectors and real-time data updates, as it is built for fast and efficient data retrieval.
2. DataStax - AstraDB, a vector database from DataStax, is available to accelerate application development. AstraDB simplifies and accelerates app development by integrating with Cassandra operations and working with AppCloudDB. It simplifies the development process by eliminating the need for time-consuming configuration updates, and allows developers to automatically scale applications across different cloud infrastructures.
3. MongoDB - MongoDB's Atlas vector search feature is a significant advancement in integrating generative AI and semantic search into applications. MongoDB's vector search capability enables developers to work with data analysis, recommender systems, and natural language processing. Atlas vector search allows developers to effortlessly search unstructured data, enabling them to generate vector embeddings using preferred machine learning models such as OpenAI or Hugging Face and store them directly in MongoDB Atlas.
4. Vespa - Vespa.ai is a powerful vector database with real-time analytics capabilities and fast query returns, making it a useful tool for businesses that need to work with data quickly and efficiently. The main advantages of this database include high data availability and fault tolerance.
5. Milvus - The Milvus vector database system was designed primarily for efficient management of complex data. It provides fast data retrieval and analysis, making it an excellent solution for applications that require real-time processing and instant conclusions. Milvus' ability to successfully handle large data sets is one of its main advantages.

In conclusion, vector databases provide powerful capabilities to manage and analyze vector data, which makes them indispensable tools in various industries and applications related to spatial information.