Generative AI - Chapter 5 [Exploring the Power of VectorDB for Gen AI]

In this edition (Chapter 5), we delve into the transformative power of VectorDB, a cutting-edge database designed specifically for generative artificial intelligence applications. Harnessing the potential of vectors, VectorDB unleashes a new realm of creativity, enabling AI systems to generate highly realistic and diverse content like never before. We will explore the underlying principles of VectorDB, its unique features, and its significant impact on the field of AI. Witness firsthand how this revolutionary database propels the boundaries of generative AI, empowering developers, artists, and researchers to unlock the full potential of their creative visions. Whether you're an AI enthusiast, a seasoned professional, or simply curious about the future of technology, this video will help anyone seeking to grasp the capabilities of VectorDB and its role in shaping the future of generative AI.

Incase if you missed the previous chapters:

• Chapter 1 Recap :?Chapter 1: Generative AI - Chapter 1 [The beginning, context leading to singularity
• Chapter 2 Recap:?Chapter 2: Current state, What you can do to ride the wave
• Chapter 3 Recap:?Chapter 3: ChatGPT, Bing Chat for everyday practical use
• Chapter 4 Recap:?Chapter 4: Gen AI Navigator: Must know facts for beginners.

Here's the 17min video covering latest AI trends, cool acquisitions and the core topic of VectorDB with top 5 DBs, their design & applications.

5 of the most used/popular vector databases

Chroma

Chroma is an open source vector database built to provide developers and organizations of all sizes with the resources they need to build large language model (LLM) applications. It gives developers a highly-scalable and efficient solution for storing, searching, and retrieving high-dimensional vectors.

Pinecone

Pinecone is a cloud-based managed vector database designed to make it easy for businesses and organizations to build and deploy large-scale machine learning applications. Unlike most popular vector databases, Pinecone not open-source

Pinecone supports integrations with multiple LLM models/platforms including Google Cloud Platform, Amazon Web Services (AWS), OpenAI, GPT-3, GPT-3.5, GPT-4, Elasticsearch, Haystack, and more.

Weaviate

Weaviate is an open source vector database that you can use as a self-hosted or fully managed solution. It provides organizations with a powerful tool for handling and managing data while delivering excellent performance, scalability, and ease of use. Whether used in a managed or self-hosted environment, Weaviate offers robust functionality and the flexibility to handle a range of data types and applications.

Milvus

Milvus is yet another open source vector database; and this one has gained popularity in the data science and machine learning fields. One of Milvus’ main advantages is its robust support for vector indexing and querying. It uses state-of-the-art algorithms to speed up the search process, resulting in fast retrieval of similar vectors even when dealing with large-scale datasets.

Qdrant

Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality. Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values.

3D Visualisation of Vector DB internal data representation

6 key real-time applications of Vector DB

Hope this article & the video helps you to get started with VectorDB for yourself or organisation and accelerate long term memory of the models & improve performance and cost efficiency.