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Vector Databases for
 Fast Similarity Search

Powering a whole new generation of AI applications is the vector database—ideal for storing, indexing, and searching unstructured data such as image, text, and video.

Vector Databases

The Power of Embeddings

Vector Databases Embeddings

The Power of Embeddings

Vector databases store data in the form of numerical representations called vectors. Each vector resides in a high-dimensional space, where the dimensions correspond to specific data attributes. Data is organized using indexing algorithms such as Hierarchically Navigable Small World (HNSW) or Locality-Sensitive Hashing (LSH), which enable faster retrieval of similar vectors.

When a query is made, the system converts the query into a vector using the same embedding model that was used to encode the stored data. The search is confined to the most relevant sections of the database, speeding up the response. Many vector databases also store the metadata of the vectors, which further helps narrow down the search results.

While other databases struggle with tasks like "find images (or text) similar to this one" vector databases can efficiently compare high-dimensional vectors using metrics like cosine similarity or Euclidean distance.

Key Features

  • Support for unstructured and multimodal data
  • Supports CRUD operations
  • Efficient indexing using ANN algorithms or graph-based techniques
  • Horizontal scaling
  • Optimized for ANN or similarity search
  • Integrates with machine learning workflows and models
  • Low-latency query processing

Native Vector Databases Vs Support for Vector Search

Apart from dedicated vector databases like Milvus and Quadrant (both open source) and Pinecone, there are databases that provide vector search as an extended feature. MongoDB, for instance, provides a specialized vector index called Atlas Vector Search that integrates with the core database. Azure CosmosDB supports storage of high-dimensional vectors along with other document properties. PostgreSQL provides the pgvector extension for storage and search of vector embeddings.

Vector Databases Logos

Vector Database Use Cases

The ability to execute real-time similarity search makes vector databases key to many low-latency AI applications.

Personalized
Recommendations

Recommendation engines built on vector databases excel in nuanced similarity comparison based on diverse attributes. Recommendations are generated in real time as vectors are updated with newly ingested data.

Similarity
Search

As vector databases can quickly unearth text, images, or videos based on a query, a range of applications (duplicate detection, image categorization, harmful content identification, etc.) can be developed harnessing this property.

Natural Language
Processing

Vector databases significantly improve the performance of NLP applications, such as virtual assistants and question-answering systems, as they can retrieve relevant answers even when exact keywords are not used.

Domain-Specific
Chatbots

In specialized chatbots powered by Large Language Models and Retrieval-Augmented Generation (RAG), vector databases play a crucial role in storing and retrieving domain-specific knowledge base.

Multi-Camera
Object Tracking

Surveillance systems built using vector databases can track objects moving across different camera views by storing and matching the object embeddings in real time.

Outlier
Detection

Outliers are more efficiently detected using a vector database as they significantly deviate from a cluster of similar vectors. This is especially true where the task involves high-dimensional data and complex similarity.

Personalized Recommendations

Recommendation engines built on vector databases excel in nuanced similarity comparison based on diverse attributes. Recommendations are generated in real time as vectors are updated with newly ingested data.

Similarity Search

As vector databases can quickly unearth text, images, or videos based on a query, a range of applications (duplicate detection, image categorization, harmful content identification, etc.) can be developed harnessing this property.

Natural Language Processing

Vector databases significantly improve the performance of NLP applications, such as virtual assistants and question-answering systems, as they can retrieve relevant answers even when exact keywords are not used.

Domain-Specific Chatbots

In specialized chatbots powered by Large Language Models and Retrieval-Augmented Generation (RAG), vector databases play a crucial role in storing and retrieving domain-specific knowledge base.

Multi-Camera Object Tracking

Surveillance systems built using vector databases can track objects moving across different camera views by storing and matching the object embeddings in real time.

Outlier Detection

Outliers are more efficiently detected using a vector database as they significantly deviate from a cluster of similar vectors. This is especially true where the task involves high-dimensional data and complex similarity.

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