Exploring Vector Databases: Applications, Use cases, and best available databases in the market
In this article, we will talk about vector databases, Where we use them, what is the best alternative of vector dbs in the market and cloud, and the use cases of Vector Databases.
Vector Database -A vector database is a database that stores information as vectors, which are numerical representations of data objects, also known as vector embeddings.
Embeddings are representations of values or objects like text, images, and audio that are designed to be consumed by machine learning models and semantic search algorithms. They translate objects like these into a mathematical form according to the factors or traits each one may or may not have, and the categories they belong to.
Let’s Take a look
- We use the Embedding Model to create a Vector Embedding for the content you want to index
- The vector embedding is added to the Vector database along with a brief mention of the source material from which it was derived.
- Upon receiving a query from the Application, we generate embeddings for it using the same embedding model and then utilize those embeddings to query the database for vector embeddings that are comparable. Similar embeddings are linked to the original content that was used to produce them.
How does the Vector Database work:
We are all familiar with the general idea behind traditional databases: rows and columns of scalar data, such as numbers and strings, are stored. A vector database, on the other hand, is optimized and queried very differently because it relies on vectors for operation.
When searching through rows in a traditional database, we typically look for values that precisely match our query. We use a similarity metric in vector databases to identify the vector that most closely matches our query.
A vector database employs a variety of algorithms, all of which participate in the Approximate Nearest Neighbour (ANN) search. These algorithms improve search results by using hashing, quantization, or graph-based search.
These algorithms are combined into a pipeline that provides fast and accurate retrieval of a vector’s neighbors. Because the vector database produces approximations, the main trade-offs we consider are accuracy versus speed. The faster the query, the more accurate the result. A good system, on the other hand, can provide ultra-fast search with near-perfect accuracy.
When you think about storing them the first thing that comes to mind is Relational Database(BigQurey Database)
We have four articles 2 for apple product and another 2 for apple fruit by taking an example first we generate Embeddings by using (open Ai) and then we will save that in database(BIG query), when you generate query and then you also generates embeddings for that and then try to comparing this embedding to the stored embeddings and then try to take similar query from database and this is called cosine similarity
Characteristics of Vector Database:
Geometric Data Representation: Vector databases are intended to store and manage geometric data in the form of vectors such as points, lines, and polygons. Real-world features such as locations, routes, and boundaries can be represented by these vectors.
Spatial Operations: Vector databases support a wide range of spatial operations and queries, including spatial joins, buffering, distance calculations, and overlay operations. These operations are critical in geospatial analysis and mapping.
Multi-Dimensional Indexing: To efficiently organize and index spatial data, vector databases frequently use multi-dimensional indexing structures such as R-trees or Quad-trees. These structures are designed to be optimized for spatial search queries.
Integration with Geographic Information System (GIS) Tools: Vector databases integrate seamlessly with Geographic Information System (GIS) tools and applications. This integration makes it easier to visualize, analyze, and interpret spatial data using popular GIS software.
There are some examples of Vector Databases in which some are open databases or available for commercial use
How does a Pinecone Vector Database work?
Pinecone is a managed vector database technology designed to address the special issues associated with high-dimensional data. Pinecone, which includes cutting-edge indexing and search capabilities, enables data engineers and data scientists to build and deploy large-scale machine learning applications that efficiently handle and analyze high-dimensional data. Pinecone’s main characteristics are as follows:
.Fully managed service
.Highly scalable
.Real-Time Data ingestion
.Integration with LangChain
How does Chroma work?
Chroma, an open-source embedding database, simplifies the construction of Large Language Model (LLM) applications by rendering information, facts, and skills adaptable. With Chroma, managing text documents, converting text into embeddings, and performing similarity searches becomes straightforward.
Source: datacamp.com
Use case of Vector Database
- Find messages from chat history that are relevant to the current conversation, thus enabling AI-PowePoweredtbots to have “memory”
- Find video clips from a vast archive of sports broadcasts that match a simple description like “leaping over a defender to catch the ball and score a touchdown”
- Find products similar to something a user has purchased before and which fit within that user’s price and style preferences
- Look up documents that are relevant to a task like “replacing the filter on a vacuum” so that an AI-powered assistant can provide a relevant, factual response
Summary
A vector database stores information as numerical representations of data objects, also known as vector embeddings. Vector databases are optimized and queried differently because they rely on vectors for operation. They support geometric data representation, spatial operations, multi-dimensional indexing, and integration with Geographic Information System (GIS) tools. Examples of vector databases include finding messages from chat history that are relevant to the current conversation, enabling AI-powered bots to have “memory.”
Note: We at Techsalo Infotech are a team of engineers solving complex Data engineering and Machine learning problems. Please reach out to us at sales@techsalo.com for any query on How to build these systems at scale and in the cloud.
