Skip to main content

What is Vector Database

A vector database is a type of database that indexes and stores vector embeddings for fast retrieval and similarity search, with capabilities like CRUD operations, metadata filtering, and horizontal scaling.

Information comes in many forms:

  • Some information is unstructured—like text documents, rich media, and audio.
  • Some information is structured—like application logs, tables, and graphs.

Innovations in artificial intelligence and machine learning (AI/ML):

  • Have allowed us to create a type of ML model—embedding models.
  • Embeddings encode all types of data into vectors that capture the meaning and context of an asset.
  • This allows us to find similar assets by searching for neighboring data points.

Vector search methods:

  • Allow unique experiences like taking a photograph with your smartphone and searching for similar images.

Vector databases provide:

  • The ability to store and retrieve vectors as high-dimensional points.
  • Additional capabilities for efficient and fast lookup of nearest-neighbors in the N-dimensional space.
  • They are typically powered by k-nearest neighbor (k-NN) indexes.
  • Built with algorithms like the Hierarchical Navigable Small World (HNSW) and Inverted File Index (IVF) algorithms.
  • Additional capabilities like data management, fault tolerance, authentication and access control, and a query engine.
Labels:

Comments

Post a Comment

Popular posts from this blog

Data Wrangling vs EDA

  Aspect Data Wrangling (Data Preprocessing) Exploratory Data Analysis (EDA) Objective Prepare raw data for modeling by cleaning, transforming, and formatting it appropriately. Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling. Order Typically performed as a preliminary step before EDA. Usually conducted after data wrangling to further investigate data characteristics. Data Handling Focuses on data cleaning, filling missing values, encoding categorical variables, and scaling features. Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies. Techniques Techniques include imputation, outlier detection, feature scaling, and one-hot encoding. Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics. Data Transformation Involves structural changes to the dataset, such as feature engineering, data normaliz...
Post a Comment
Read more

What is Tensor Parallelism and relationship between Buffer and GPU

  Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU .   It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...
Post a Comment
Read more

Infrastructure limitations ESX Server 4 vs ESXI 5

Some limitations in ESX Server 4 may constrain the design of data centers:[28][29] • Guest system maximum RAM: 255 GB • Host system maximum RAM: 1 TB[28] • Number of hosts in a high availability cluster: 32 • Number of Primary Nodes in ESX Cluster high availability: 5 • Number of hosts in a Distributed Resource Scheduler cluster: 32 • Maximum number of processors per virtual machine: 8 • Maximum number of processors per host: 160 • Maximum number of cores per processor: 12 • Maximum number of virtual machines per host: 320 • VMFS-3 limits files to 262,144 (218) blocks, which translates to 256 GB for 1 MB block sizes (the default) or up to 2 TB for 8 MB block sizes.[30] However, on a VMFS Boot drive, it is usually very difficult to use anything other than 1 MB Block size [31]. With ESXI 5 there has been some changes to these limits[32] • Guest system maximum RAM: 1 TB • Host system maximum RAM: 2 TB • Number of hosts in a high availability cluster: 32 • Maximum number ...
Post a Comment
Read more