A vector database is a specialised database that stores data as numerical vectors, called vector embeddings, which represent unstructured and semi-structured data like images, text, and sensor data.

Vector Databases
Vector Databases

Unlike vector search libraries or indexes, a vector database is a comprehensive data management solution that offers features like metadata storage, filtering, scalability, dynamic data updates, backups, and security. 

What are vector embeddings?

Vector embeddings are a numerical representation of data, such as text, images, or other subjects, which are generated by large language models and AI algorithms. 

  • These embeddings capture the essence of the data in a mathematical form, allowing vector databases and search engines to calculate the similarity between vectors based on their distance from each other.
  • This distance represents multiple dimensions of the data objects, enabling machine learning and AI to identify patterns, relationships, and underlying structures, and make informed decisions.

How does a vector database work?

Working of Vector Databases
Working of Vector Databases

A vector database operates by leveraging algorithms to efficiently index and query vector embeddings. 

  • These algorithms facilitate approximate nearest neighbour (ANN) search through techniques such as hashing, quantisation, or graph-based search, enabling rapid retrieval of information.
  • To retrieve data, an ANN search identifies the nearest vector neighbour to a query, providing a efficient and scalable solution for large datasets of high-dimensional vectors. 
  • While less accurate than a kNN search, ANN search strikes a balance between speed and precision, making it ideal for big data applications

Difference between a vector database and a traditional database?

  • Traditional databases store data in tabular form and index data using specific values, returning exact matches when queried. In contrast, vector databases store data as vector embeddings and enable vector search, which returns results based on similarity metrics, rather than exact matches.

Vector databases are specifically designed to handle vector embeddings, making them a superior choice over traditional databases in certain applications, such as:

  • Similarity search
  • Artificial intelligence
  • Machine learning

Vector databases offer advantages like:

  • High-dimensional search capabilities
  • Customized indexing
  • Scalability
  • Flexibility
  • Efficiency

These features make vector databases ideal for applications that require complex data analysis and pattern recognition.

5 of the Best Vector Databases

Vector Databases
Vector Databases

1. Chroma


Chroma is an open-source embedding database that simplifies the development of Large Language Model (LLM) applications. By making knowledge, facts, and skills pluggable for LLMs, Chroma enables you to easily build and integrate new capabilities into your LLM apps.

2. Pinecone


Pinecone is a cutting-edge, managed vector database platform specifically designed to overcome the complexities of high-dimensional data. With its state-of-the-art indexing and search capabilities, Pinecone enables data engineers and data scientists to build and deploy large-scale machine learning applications that efficiently process and analyse high-dimensional data.

3. Weaviate


Weaviate is an open-source vector database that enables you to store and manage large volumes of data objects and vector embeddings from your preferred machine learning models. With Weaviate, you can seamlessly scale to billions of data objects, making it an ideal solution for large-scale machine learning applications.

4. Faiss


Faiss is an open-source library designed for efficient similarity search and clustering of dense vectors. Its algorithms are capable of handling vector sets of diverse sizes, even those that exceed RAM capacity. 

5. Qdrant


Qdrant is a powerful vector database and search engine that enables fast and accurate similarity searches for high-dimensional vectors. Operating as an API service, Qdrant allows you to efficiently find the closest vectors, enabling a wide range of applications.

Applications of vector databases

In AI/ML, vector databases enhance capabilities through:

  • Semantic information retrieval
  • Long-term memory

In NLP, vector databases enable:

  • Vector similarity search
  • Text embeddings processing
  • Computer understanding of human language

In Image Recognition and Retrieval, vector databases:

  • Transform images into image embeddings
  • Retrieve similar images or identify matches through similarity search

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