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Vishnu Sivan
Vishnu Sivan

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Goodbye databases, it’s time to embrace Vector Databases!

The AI revolution is reshaping industries, promising remarkable innovations while introducing new challenges. In this transformative landscape, efficient data processing has become paramount for applications relying on large language models, generative AI, and semantic search. At the heart of these breakthroughs lies vector embeddings, intricate data representations infused with critical semantic information. These embeddings generated by LLMs, encompass numerous attributes or features, rendering their management a complex task. In the realm of AI and machine learning, these features represent different dimensions of data that are essential for discerning patterns, relationships, and underlying structures. To address the unique demands of handling these embeddings, a specialized database is essential. Vector databases are purpose-built to provide optimized storage and querying capabilities for embeddings, bridging the gap between traditional databases and standalone vector indexes as well as empowering AI systems with the tools they need to excel in this data-intensive environment.

Getting Started

Table of contents

  • Introduction to Vector Databases
  • Vector Embeddings
  • Vector Search
  • Approximate Nearest Neighbor Approach(ANN)
  • Vector database vs Relational database
  • Working of Vector Databases
  • Importance of Vector Databases
  • Top 7 Vector Databases
  • Use Cases of Vector Databases

Introduction to Vector Databases

A vector database is a specialized type of database that stores data in the form of multi-dimensional vectors, each representing specific characteristics or qualities. These vectors can have varying dimensions, from a few to thousands, depending on data complexity. Various techniques like machine learning models or feature extraction are used to convert data, including text, images, audio, and video, into these vectors.

The key advantage of a vector database is its ability to efficiently and accurately retrieve data based on vector proximity or similarity. This enables searches based on semantic and contextual relevance rather than relying solely on exact matches or predefined criteria, as seen in traditional databases.

Vector Embeddings

AI and ML have revolutionized the representation of unstructured data by using vector embeddings. These are essential lists of numbers that capture the semantic meaning of data objects. For instance, colors in the RGB system are represented by numbers indicating their red, green, and blue components.

However, representing more complex data, like words or text, in meaningful numerical sequences is challenging. This is where ML models come into play. ML models can represent the meaning of words as vectors by learning the relationships between words in a vector space. These models are often called embeddings models or vectorizers.

Vector embeddings encode the semantic meaning of objects relative to one another. Similar objects are grouped closely in the vector space, meaning that the closer two objects are, the more similar they are.

For example, consider word vectors. In this case, words like “Wolf” and “Dog” are close to each other because dogs are descendants of wolves. “Cat” is also similar because it shares similarities with “Dog” as both are animals and common pets. On the other hand, words representing fruits like “Apple” and “Banana” are further away from animal terms, forming a distinct cluster in the vector space.

Vector Embeddings
Image credits A Gentle Introduction to Vector Databases | Weaviate — vector database

Vector Search

Vector embeddings enable us to perform vector search, similarity search, or semantic search by finding and retrieving similar objects within a vector database. These processes involve locating objects that are close to each other in the vector space.

Just as we can find similar vectors for a specific object (e.g., a dog), we can also find similar vectors to a search query. For example, to discover words which are similar to the word “Kitten,” we generate a vector embedding for “Kitten” and retrieve all items that are close to the query vector, like the word “Cat.”

The numerical representation of data objects empowers us to apply mathematical operations, such as calculating the distance between two vector embeddings, to determine their similarity. This makes vector embeddings a powerful tool for searching and comparing data objects based on their semantic meaning.

Approximate Nearest Neighbor Approach(ANN)

Vector indexing streamlines data retrieval by efficiently organizing vector embeddings. It employs an approximate nearest neighbor (ANN) approach to pre-calculate distances between vector embeddings, cluster similar vectors, and store them in proximity. While this approach sacrifices some accuracy for speed, it allows for faster retrieval of approximate results.

For instance, in a vector database, you can pre-calculate clusters like “animals” and “fruits.” When querying the database for “Kitten,” the search begins with the nearest animals, avoiding distance calculations between fruits and non-animal objects. The ANN algorithm initiates the search within a relevant region, such as four-legged animals, maintaining proximity to relevant results due to pre-organized similarity.

Vector database vs Relational database

The primary difference between traditional relational databases and modern vector databases lies in their optimization for different types of data. Relational databases excel at handling structured data stored in columns, relying on keyword matches for search. In contrast, vector databases are well-suited for structured and unstructured data, including text, images, and audio, along with their vector embeddings, which enable efficient semantic search. Many vector databases store vector embeddings alongside the original data, providing the flexibility to perform both vector-based and traditional keyword searches.

For instance, when searching for jeopardy questions that involve animals, a traditional database necessitates a complex query with specific animal names, while a vector database simplifies the search by allowing a query for the general concept of “animals”.

Vector Search

Working of Vector Databases

Working of Vector Databases
Image credits What is a Vector Database & How Does it Work? Use Cases + Examples | Pinecone

In the context of an application like ChatGPT, which deals with extensive data, the process involves:

  • User inputs a query into the application.
  • Content to be indexed is converted into vector embeddings using the embedding model.
  • The vector embedding, along with a reference to the original content, is stored in the vector database.
  • When the application issues a query, the embedding model generates embeddings for the query. These query embeddings are used to search the database for similar vector embeddings. In traditional databases, queries typically require exact matches, while vector databases utilize similarity metrics to find the most similar vector to a query.

Vector databases employ a combination of algorithms for Approximate Nearest Neighbor (ANN) search. These algorithms, organized into a pipeline, optimize search speed through techniques like hashing, quantization, and graph-based methods. Balancing accuracy and speed is a key consideration when using vector databases, which provide approximate results.

Working of Vector Databases
Image credits What is a Vector Database & How Does it Work? Use Cases + Examples | Pinecone

A vector database query involves three main stages:

  1. Indexing: Vector embeddings are mapped to data structures using various algorithms within the vector database, thereby enhancing search speed.

  2. Querying: The database compares the queried vector to indexed vectors, employing a similarity metric to locate the nearest neighbor.

  3. Post Processing: The vector database performs post-processing on the nearest neighbor to generate the final query output, potentially re-ranking the nearest neighbors for future reference.

Importance of Vector Databases

Vector databases are pivotal for indexing vectors generated through embeddings. It enables searches for similar assets via neighboring vectors. Developers leverage these databases to create unique application experiences, including image searches based on user-taken photos. Automation of metadata extraction from content, coupled with hybrid keyword and vector-based searches, further enhances search capabilities. Vector databases also serve as external knowledge bases for generative AI models like ChatGPT. It ensures trustworthy information and reliable user interactions, particularly in mitigating issues like hallucinations.

Top 7 Vector Databases

The vector database landscape is dynamic and swiftly evolving, with numerous prominent players driving innovation. Each database presents distinctive features and functionalities, serving a variety of requirements and applications in the fields of machine learning and artificial intelligence.

Top 7 Vector Databases
Image credits The 5 Best Vector Databases | A List With Examples | DataCamp

  1. Chroma

Image credits 🏡 Home | Chroma (
Chroma is an open-source embedding database designed to simplify the development of LLM (Large Language Model) applications by enabling the integration of knowledge, facts, and skills for these models. It offers features like managing text documents, converting text to embeddings, and conducting similarity searches.

Key Features:

  • Chroma provides a wide range of features, including queries, filtering, density estimates, and more.
  • Supports LangChain (Python and JavaScript) and LlamaIndex.
  • The API used in a Python notebook seamlessly scales to a production cluster.
  1. Pinecone

Image credits A Pinecone Alternative With Better Search Relevance and Lower Costs — Vectara

Pinecone is a managed vector database platform specifically designed to address the complexities of high-dimensional data. With advanced indexing and search functionalities, Pinecone enables data engineers and data scientists to create and deploy large-scale machine learning applications for efficient processing and analysis of high-dimensional data.

Key Features:

  • Fully managed service.
  • Highly scalable for handling large datasets.
  • Real-time data ingestion for up-to-date information.
  • Low-latency search capabilities.
  • Integration with LangChain.
  1. Milvus

Image credits What is Milvus Vector Database? — Zilliz

Milvus is an open-source vector database with a focus on embedding similarity search and AI applications. It provides an easy-to-use, uniform user experience across deployment environments. The stateless architecture of Milvus 2.0 enhances elasticity and adaptability, making it a reliable choice for a range of use cases including image search, chatbots, and chemical structure search.

Key Features:

  • Capable of searching trillions of vector datasets in milliseconds.
  • Offers straightforward management of unstructured data.
  • Highly scalable and adaptable to diverse workloads.
  • Supports hybrid search capabilities.
  • Incorporates a unified Lambda structure for seamless performance.
  1. Weaviate

Image credits Learning to Retrieve Passages without Supervision | Weaviate

Weaviate is an open-source vector database that enables the storage of data objects and vector embeddings from various machine learning models. It can seamlessly scale to accommodate billions of data objects.

Key Features:

  • Weaviate can rapidly retrieve the ten nearest neighbors from millions of objects in just milliseconds.
  • Users can import or upload their vectorised data, as well as integrate with platforms like OpenAI, HuggingFace, and more.
  • Weaviate is suitable for both prototypes and large-scale production, prioritizing scalability, replication, and security.
  • Weaviate offers features like recommendations, summarizations, and integrations with neural search frameworks.
  1. Qdrant

Image credits qdrant/qdrant: Qdrant

Qdrant is a versatile vector database and API service designed for conducting high-dimensional vector similarity searches. It transforms embeddings and neural network encoders into comprehensive applications suited for matching, searching, and recommendations.

Key Features:

Provides OpenAPI v3 specifications and pre-built clients for multiple programming languages.
Utilizes a custom HNSW algorithm for rapid and accurate vector searches.
Enables result filtering based on associated vector payloads.
Supports various data types including string matching, numerical ranges, and geo-locations.
Designed for cloud-native environments with horizontal scaling capabilities.

  1. Elasticsearch

Image credits Learn more about Elasticsearch — ITZone
Elasticsearch is an open-source analytics engine that offers versatile data handling capabilities, including textual, numerical, geographic, structured, and unstructured data. It is a key component of the Elastic Stack, a suite of open tools for data processing, storage, analysis, and visualization. Elasticsearch excels in various use cases, providing centralized data storage, lightning-fast search, fine-tuned relevance, and scalable analytics.

Key Features:

  • Supports cluster configurations and ensures high availability.
  • Features automatic node recovery and data distribution.
  • Scales horizontally to handle large workloads.
  • Detects errors to maintain secure and accessible clusters and data.
  • Designed for continuous peace of mind, with a distributed architecture that ensures reliability.
  1. Faiss

Image credits Faiss: A library for efficient similarity search — Engineering at Meta (

Faiss, developed by Facebook AI Research, is an open-source library designed for fast and efficient dense vector similarity search and grouping. It supports searching sets of vectors of various sizes, even those that may not fit in RAM, making it versatile for large datasets.

Key Features:

  • Besides returning the nearest neighbor, Faiss also returns the second nearest, third nearest, and k-th nearest neighbors.
  • Allows searching multiple vectors simultaneously (batch processing).
  • Utilizes the greatest inner product search rather than a minimal Euclidean search.
  • Supports various distances, including L1, Linf, and more.

Use cases of Vector Databases

Vector databases are making significant impacts across various industries by excelling in similarity search.

Retail Experiences

Vector databases transform retail by powering advanced recommendation systems that offers personalized shopping experiences based on product attributes and user preferences.

Natural Language Processing (NLP)

Vector databases enhance NLP applications, enabling chatbots and virtual assistants to better understand and respond to human language, improving customer-agent interactions.

Financial Data Analysis

In finance, vector databases analyze complex data to help analysts detect patterns, make informed investment decisions, and forecast market movements.

Anomaly Detection

Vector databases excel at spotting outliers, particularly in sectors like finance and security, making the detection process faster and more accurate, thus preventing fraud and security breaches.


Vector databases personalize medical treatments by analyzing genomic sequences, aligning solutions with individual genetic makeup.

Media Analysis

Vector databases simplify image analysis, aiding in tasks such as medical scans and surveillance footage interpretation for optimizing traffic flow and public safety.

To experiment with vector databases such as Chromadb, Pinecone, Weaviate and Pgvector, follow the below link.

Experimenting with Vector Databases: Chromadb, Pinecone, Weaviate and Pgvector |

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Thanks Gowri M Bhatt for reviewing the content.

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