## What is vector database ?

**word embeddings**, where words with similar meanings are mapped to close points in the vector space.

**grapes**or

**kiwis**, even though those words weren't explicitly used. This semantic search capability unlocks a new level of data exploration and analysis.

## How vectors are created ?

## Use case vectors solve?

Get ready to explore the diverse problems solvable with vector embeddings! These powerful representations go beyond text, unlocking:

1. **Semantic Search**: Dive deeper than keywords. Find images, videos, or audio similar to your intent, not just literal phrases. Imagine searching for "peaceful nature scene" and discovering breathtaking waterfalls instead of generic landscapes.

2. **Data Similarity Search**: Uncover hidden connections across non-text data. Quickly identify similar products, faces, or even medical scans, regardless of format.

3. **Personalised Recommendations**: Get suggestions that truly understand you. Vector embeddings power recommendation systems that learn your preferences and suggest items you'll genuinely love, not just similar purchases

4. **Retrieval-Augmented Generation (RAG)**: Bridge the gap between information retrieval and generation. Leverage vector embeddings to create summaries, translate languages, or even write different creative text formats based on specific requests. This is number #1 application of LLM powered apps.

5. **Fraud and Anomaly Detection**: Spot suspicious activity faster. Vector embeddings help identify unusual patterns in transactions, financial data, or even network traffic, leading to improved security and fraud prevention.

6.** Search Result Ranking**: Get the most relevant results first. Embeddings power search engines to understand your intent and rank results based on meaning, not just keyword matches.

7.** Efficient Clustering**: Group similar data points effortlessly. Vector embeddings enable efficient clustering of large datasets, revealing hidden patterns and facilitating further analysis.

And that's just the beginning! The potential of vector embeddings continues to expand, promising exciting solutions in areas like drug discovery, social network analysis, and more.

## How Vector database uses vector?

**semantic similarity**. Unlike traditional keyword searches, vector databases understand meaning.

Several algorithms can be used for calculating vector difference, each with its advantages and limitations depending on the specific application and data characteristics. Here are some common ones:

Jaccard similarity

This compares the proportion of shared elements between two binary vectors (containing only 0s and 1s), often used for comparing sets or sparse data.

**Hamming distance**

Several algorithms can be used for calculating vector difference, each with its advantages and limitations depending on the specific application and data characteristics. Here are some common ones:

Jaccard similarity

This compares the proportion of shared elements between two binary vectors (containing only 0s and 1s), often used for comparing sets or sparse data.

**Hamming distance**

## Between two strings or vectors of equal length is the number of positions at which the corresponding symbols are different. In other words, it measures the minimum number of substitutions required to change one string into the other, or equivalently, the minimum number of errors that could have transformed one string into the other

Euclidean distance: This is the most straightforward and intuitive method, calculating the straight-line distance between two points in multidimensional space. It's computationally efficient but sensitive to data scaling and dimensionality.

Manhattan distance: This measures the distance by summing the absolute differences between corresponding elements of the vectors. It's less sensitive to outliers than Euclidean distance but not as intuitive for representing geometric similarity.

Inner Product : This method is a mathematical operation that measures the degree of similarity or alignment between two vectors. It tells you how "close" two vectors are in the multidimensional space they inhabit

Cosine similarity: This method measures the angle between two vectors, reflecting their directional similarity. It's independent of magnitude and useful when interpreting vectors as directions rather than exact positions.

Euclidean distance: This is the most straightforward and intuitive method, calculating the straight-line distance between two points in multidimensional space. It's computationally efficient but sensitive to data scaling and dimensionality.

Manhattan distance: This measures the distance by summing the absolute differences between corresponding elements of the vectors. It's less sensitive to outliers than Euclidean distance but not as intuitive for representing geometric similarity.

Inner Product : This method is a mathematical operation that measures the degree of similarity or alignment between two vectors. It tells you how "close" two vectors are in the multidimensional space they inhabit

Cosine similarity: This method measures the angle between two vectors, reflecting their directional similarity. It's independent of magnitude and useful when interpreting vectors as directions rather than exact positions.

## Conclusion

This post delves into the fascinating world of vector databases, equipping you with a solid understanding of their core concepts, vector creation methods, and similarity search algorithms.

In the next section, we'll dive into the unique storage and retrieval mechanisms employed by vector databases. Unlike traditional databases that rely on B-trees or hash indexes, vector databases utilize innovative approaches specifically designed for efficient vector searches. Get ready to explore a whole new level of data exploration!

This post delves into the fascinating world of vector databases, equipping you with a solid understanding of their core concepts, vector creation methods, and similarity search algorithms.

In the next section, we'll dive into the unique storage and retrieval mechanisms employed by vector databases. Unlike traditional databases that rely on B-trees or hash indexes, vector databases utilize innovative approaches specifically designed for efficient vector searches. Get ready to explore a whole new level of data exploration!