What are vector databases, how To use them, and what are their limitations?

Table of Contents


A vector database is a type of database that is specifically designed to store and retrieve high-dimensional vectors efficiently.

Vectors are mathematical representations of objects or data points that capture their characteristics or features.

In a vector database, these vectors are stored and indexed in a way that enables fast similarity searches and efficient retrieval of similar vectors.


Technical Details

Vector databases typically employ advanced data structures and algorithms to efficiently handle high-dimensional vectors. Some common techniques used in vector databases include:

1. Vector Indexing: Vector indexing is a key component of vector databases. It involves creating an index structure that organizes the vectors in a way that enables efficient retrieval based on similarity. Various indexing methods, such as tree-based structures (e.g., k-d trees, ball trees), graph-based structures (e.g., Graph-based Nearest Neighbor), or hashing techniques (e.g., Locality Sensitive Hashing), can be used.

2. Similarity Search: Vector databases allow users to perform similarity searches, where they can query the database for vectors that are similar to a given query vector. This is typically achieved by using nearest neighbour search algorithms that leverage the index structure to quickly identify similar vectors based on distance metrics such as Euclidean distance or cosine similarity.

3. Dimensionality Reduction: High-dimensional vectors can be computationally expensive to handle and may suffer from the curse of dimensionality. Vector databases often employ dimensionality reduction techniques, such as Principal Component Analysis (PCA) or Locally Linear Embedding (LLE), to reduce the dimensionality of vectors while preserving their meaningful structure.


Vector databases

  1. Elasticsearch: Elasticsearch is a distributed search and analytics engine that can be used as a vector search engine. It supports vector similarity search through the use of specialized plugins like the “Elasticsearch Vector Scoring” plugin or by leveraging similarity algorithms like cosine similarity. Elasticsearch provides efficient indexing, querying, and aggregations on vector data.
  2. Solr: Solr is an open-source search platform built on Apache Lucene. It offers vector search capabilities through the “Vector Field” feature, where vectors can be indexed and searched using various distance metrics. Solr supports both dense and sparse vectors, and provides options for similarity scoring and filtering based on vector fields.
  3. Vespa: Vespa is an open-source, high-performance search and recommendation engine developed by Yahoo. It supports vector search through its “Approximate Nearest Neighbor” (ANN) functionality. Vespa can index and search vectors efficiently, enabling similarity-based searches and recommendations.
  4. Pinecone: Pinecone is a cloud-based vector database and search engine designed for high-throughput vector similarity search. It provides a simple API for indexing and querying vectors, allowing users to perform fast and accurate nearest neighbor searches. Pinecone also offers automatic indexing and scaling capabilities.
  5. Milvus: Milvus, mentioned earlier as a vector database, also functions as a vector search engine. It supports similarity search and retrieval of high-dimensional vectors using various distance metrics. Milvus provides efficient indexing and querying capabilities and can handle large-scale vector datasets.
  6. Weaviate: Weaviate is an open-source knowledge graph and vector search engine. It allows users to store and search structured data as well as perform similarity searches on high-dimensional vectors. Weaviate leverages graph-based indexing and querying techniques to enable efficient vector searches.
  7. Polyaxon: Polyaxon is an open-source machine learning platform that includes vector search capabilities. It allows users to index and search vectors using approximate nearest neighbor algorithms. Polyaxon provides scalable and distributed vector search functionality for large-scale applications.
  8. MeiliSearch: MeiliSearch is an open-source search engine with vector search support. It enables indexing and searching of both textual and vector data. MeiliSearch provides options for efficient similarity searches based on vectors and supports various similarity scoring algorithms.
  9. MatchZoo: MatchZoo is an open-source deep learning toolkit for text matching tasks, but it can also be used as a vector search engine. It includes pre-built models for vector representation learning and similarity search. MatchZoo enables efficient matching and retrieval of vectors based on semantic similarity.
  10. Squid: Squid is an open-source search platform designed for large-scale document and vector search. It provides a distributed and scalable infrastructure for indexing and searching vectors. Squid supports efficient similarity search algorithms and offers features like relevance ranking and filtering based on vector fields.
  11. Annoy (Approximate Nearest Neighbors Oh Yeah): Annoy is a C++ library that provides approximate nearest neighbor search for high-dimensional data. It uses random projection trees to build an index structure that enables fast similarity searches. Annoy is widely used for efficient nearest neighbor search in various applications.
  12. Faiss (Facebook AI Similarity Search): Faiss is an open-source library developed by Facebook AI Research. It provides highly efficient algorithms for similarity search and clustering of dense vectors. Faiss supports both CPU and GPU implementations, making it suitable for large-scale vector databases.
  13. Spotify’s Annoy (Python Library): Spotify’s Annoy is a Python library inspired by the Annoy library mentioned earlier. It provides an easy-to-use interface for approximate nearest neighbor search and is particularly useful for applications involving large-scale vector datasets.
  14. Milvus: Milvus is an open-source vector database built specifically for machine learning applications. It offers efficient storage, indexing, and querying of high-dimensional vectors. Milvus supports various similarity search algorithms and provides SDKs for multiple programming languages.
  15. Hnswlib (Hierarchical Navigable Small World): Hnswlib is a C++ library that implements the Hierarchical Navigable Small World algorithm. This algorithm enables fast approximate nearest neighbor search by building a hierarchical graph structure. Hnswlib is known for its efficiency in handling large-scale vector databases.
  16. NMSLIB (Non-Metric Space Library): NMSLIB is an open-source library that provides a comprehensive collection of similarity search algorithms. It supports both exact and approximate nearest neighbor search for high-dimensional data. NMSLIB offers implementations in C++, Python, and Java, making it versatile and widely used.
  17. ScaNN (Scalable Nearest Neighbors): ScaNN is a vector similarity search library developed by Google Research. It provides efficient and scalable algorithms for approximate nearest neighbor search. ScaNN is designed to handle large-scale vector databases and is particularly effective when used with GPUs.
  18. PQFast (Product Quantization Fast): PQFast is a vector database library that leverages product quantization techniques for efficient similarity search. It offers both exact and approximate nearest neighbor search capabilities. PQFast is known for its speed and scalability, making it suitable for large-scale applications.
  19. PANNs (PANNS: A Python Library for Approximate Nearest Neighbor Search): PANNs is a Python library that provides implementations of various approximate nearest neighbor search algorithms. It offers both CPU and GPU implementations and supports various distance metrics. PANNs is easy to use and suitable for both small and large-scale vector databases.
  20. NGT (Neighborhood Graph and Tree): NGT is an open-source library that focuses on fast approximate nearest neighbor search. It provides efficient indexing and query processing algorithms for high-dimensional vectors. NGT supports both CPU and GPU implementations, making it suitable for a wide range of applications.

These vector databases offer a range of features and capabilities for efficient storage, indexing, and retrieval of high-dimensional vectors. They are widely used in various domains, including recommendation systems, image retrieval, natural language processing, and more.


Use on Well-Known Websites

Several well-known websites and applications utilize vector databases to enhance their functionality. Here are a few examples:

1. Airbnb: Airbnb employs a vector database to power its search and recommendation system. By representing properties and user preferences as high-dimensional vectors, Airbnb can efficiently match user queries with relevant listings and provide personalized recommendations.

2. Spotify: Spotify uses a vector database to power its music recommendation system. By representing songs and user preferences as vectors, Spotify can find similar songs or create personalized playlists based on user preferences and listening history.

3. Pinterest: Pinterest utilizes a vector database to improve its visual search feature. By converting images into high-dimensional vectors, Pinterest enables users to search for visually similar images, discover related content, and find products matching their preferences.


Code Examples

Here are code examples demonstrating how to query a vector database using popular vector databases:

1. Weaviate (https://www.semi.technology/developers/weaviate/current/):

import requests

query_vector = [0.5, 0.3, 0.8] # Query vector

response = requests.get(f’https://localhost:8080/v1/graphql?query={{Get{{Things{{Find{{}}}}}}}}&nearVector={query_vector}’)


2. Pinecone (https://www.pinecone.io/):

import pinecone


index = pinecone.Index(index_name=’your-index-name’)
query_vector = [0.5, 0.3, 0.8] # Query vector

results = index.query(queries=[query_vector])


3. Vespa (https://vespa.ai/):

from vespa.query import Query, OR, WeakAnd, RankProfile
from vespa.query.tensor import Tensor

query_vector = [0.5, 0.3, 0.8] # Query vector

query = Query(
rank_profile=RankProfile(name=”default”, first_phase=”proximity”),
“embedding”: Tensor(query_vector)

4. Elasticsearch (https://www.elastic.co/elasticsearch/):

from elasticsearch import Elasticsearch

es = Elasticsearch()

query_vector = [0.5, 0.3, 0.8] # Query vector

body = {
“query”: {
“knn”: {
“field”: “vector_field”,
“vector”: {
“values”: query_vector

response = es.search(index=”your-index-name”, body=body)


5. Solr (https://lucene.apache.org/solr/):

from solr import Solr

solr = Solr(‘https://localhost:8983/solr/your-collection’)

query_vector = [0.5, 0.3, 0.8] # Query vector

params = {
‘q’: ‘*:*’,
‘fq’: ‘{!vec field=vector_field model=vectors v=’ + ‘,’.join(str(val) for val in query_vector) + ‘}’,
‘rows’: 10

response = solr.search(**params)



Vector databases provide a powerful means to store and query high-dimensional vectors efficiently.

They are extensively used by popular websites and applications to enhance search, recommendation, and similarity-based functionalities.

By leveraging specialized indexing techniques and similarity search algorithms, vector databases enable these systems to process large volumes of data and deliver personalized experiences to users.

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