Update 2023-05-12-introduction-vector-embedding-databases.md.md

_posts/2023-05-12-introduction-vector-embedding-databases.md.md

@@ -9,7 +9,7 @@ image: https://github.com/jkfran/jkfran.com/assets/6353928/c709f19e-ebab-49a1-a5
 
 In recent years, there has been a growing interest in vector/embedding databases. These databases are designed to store and query vector representations of data, such as text, images, and audio. Vector representations are powerful tools for representing the meaning of data, and they can be used for a variety of tasks, such as search, recommendation, and machine learning.
 
-In this blog post, we will provide an introduction to vector/embedding databases. We will discuss the different types of vector representations, and we will explain how vector/embedding databases work. We will also discuss some of the benefits of using vector/embedding databases.
+In this blog post, we will provide an introduction to vector databases. We will discuss the different types of vector representations, and we will explain how vector databases work. We will also discuss some of the benefits of using vector databases.
 
 ## What are Vector Representations?
 
@@ -21,22 +21,25 @@ There are many different types of vector representations. Some of the most commo
 * **Image embeddings:** Image embeddings are vector representations of images. Image embeddings are typically learned from a large corpus of images. Image embeddings can be used to represent the content of images, and they can be used for a variety of tasks, such as image classification, image retrieval, and object detection.
 * **Audio embeddings:** Audio embeddings are vector representations of audio. Audio embeddings are typically learned from a large corpus of audio. Audio embeddings can be used to represent the content of audio, and they can be used for a variety of tasks, such as speech recognition, speaker identification, and music genre classification.
 
-## How do Vector/Embedding Databases Work?
+![image](https://github.com/jkfran/jkfran.com/assets/6353928/3176a677-479d-4f5d-b3b2-a3bb721da1e2)
 
-Vector/embedding databases are designed to store and query vector representations of data. Vector/embedding databases typically use a variety of techniques to store and query vector representations, such as:
+
+## How do Vector Databases Work?
+
+Vector databases are designed to store and query vector representations of data. Vector databases typically use a variety of techniques to store and query vector representations, such as:
 
 * **Hierarchical Indexes:** Hierarchical indexes are used to store and query vector representations in a tree-like structure. Hierarchical indexes can be used to quickly find vector representations that are similar to a given vector representation.
 * **Spatial Indexes:** Spatial indexes are used to store and query vector representations in a spatial data structure, such as a kd-tree or a quadtree. Spatial indexes can be used to quickly find vector representations that are close to a given vector representation.
 * **Graph Indexes:** Graph indexes are used to store and query vector representations in a graph data structure. Graph indexes can be used to quickly find vector representations that are connected to a given vector representation.
 
-## Benefits of Using Vector/Embedding Databases
+## Benefits of Using Vector Databases
 
-There are many benefits to using vector/embedding databases. Some of the benefits of using vector/embedding databases include:
+There are many benefits to using vector databases. Some of the benefits of using vector databases include:
 
-* **Faster Search:** Vector/embedding databases can be used to quickly find vector representations that are similar to a given vector representation. This can be used to improve the performance of search applications, such as search engines and recommender systems.
-* **More Accurate Search:** Vector/embedding databases can be used to find vector representations that are more semantically similar to a given vector representation. This can be used to improve the accuracy of search applications, such as search engines and recommender systems.
-* **More Flexible Search:** Vector/embedding databases can be used to perform a variety of search queries, such as nearest neighbor search, range search, and keyword search. This makes vector/embedding databases more flexible than traditional relational databases.
+* **Faster Search:** Vector databases can be used to quickly find vector representations that are similar to a given vector representation. This can be used to improve the performance of search applications, such as search engines and recommender systems.
+* **More Accurate Search:** Vector databases can be used to find vector representations that are more semantically similar to a given vector representation. This can be used to improve the accuracy of search applications, such as search engines and recommender systems.
+* **More Flexible Search:** Vector databases can be used to perform a variety of search queries, such as nearest neighbor search, range search, and keyword search. This makes vector databases more flexible than traditional relational databases.
 
 ## Conclusion
 
-Vector/embedding databases are a powerful new technology for storing and querying vector representations of data. Vector/embedding databases can be used to improve the performance and accuracy of a variety of applications, such as search engines, recommender systems, and machine learning applications.
+Vector databases are a powerful new technology for storing and querying vector representations of data. Vector databases can be used to improve the performance and accuracy of a variety of applications, such as search engines, recommender systems, and machine learning applications.