The document explains vector databases, which facilitate advanced search capabilities like visual and semantic search for applications such as e-commerce and customer service. It discusses the importance of vector embeddings for representing data and mentions various tools and approaches used for vector similarity search. Additionally, it provides examples of how to retrieve similar products based on image data using Redis database functionalities.

1. Vector Similarity Search
or
What is Vector Database?
Guy Korland, PhD.
CTO of Incubation, Redis

2. • What?
• Why?
• How?
Vector Database?

3. What is Vector Database?

4. It’s a new database vertical
Vector Database

5. Why do we need it?
Visual Search E-Commerce: Recommenders
Natural Language Search
Customer Service:
semantic search,
Q&A, content
recommendation,
Jobs
Recommend:
Similar products,
brands, properties
Image Recognition:
face recognition,
similar products

6. How?

7. Vector embedding

8. Vector representation of words + similarity (word2vec)
https://medium.com/@hari4om/word-embedding-d816f643140

9. Text embedding (BERT)

10. Semantic Similarity

11. Works in the space
https://github.com/facebookresearch/faiss/wiki

12. Benchmarks
http://ann-benchmarks.com/
• FAISS - Facebook
• ScaNN - Google
• SPTAG - Microsoft
• NGT - Yahoo!
• ANNOY - Spotify

13. Hashes
Bitmaps
Strings
Bit Field
Streams
Hyperloglog
Sorted Sets
Sets
Geospatial
Search Graph
TimeSeries
AI
JSON
Data-Structures Modules
BloomFilter
Redis beyond cache
Lists
Redis Enterprise core
Linear scalability HA Geo-distribution
Durability Backup & restore Tiered-memory access Security
Multi-tenant
Gears

14. Hashes
Bitmaps
Strings
Bit Field
Streams
Hyperloglog
Sorted Sets
Sets
Geospatial
Search Graph
TimeSeries
AI
JSON
Data-Structures Modules
BloomFilter
Redis beyond cache
Lists
Redis Enterprise core
Linear scalability HA Geo-distribution
Durability Backup & restore Tiered-memory access Security
Multi-tenant
Gears

15. Redis
15
Images embeddings
Texts embeddings
Videos embeddings
Stored Indexed
Redis as a database
RedisJSON RedisGraph
Hashes
Redis as a database
with
Similarity Search
RedisJSON RedisGraph
Hashes

16. {
"name": "MY Blue shirt"
"images": [
[0.2, 0.4, 0.3, 0.5…],
[0.1, 0.5, 0.3, 0.2…],
],
"price": 19.90,
"location": "32.05, 34.680"
}
Example
Catalog

17. FT.SEARCH products
"*=>[TOP_K 10 @images $BLOB]"
Example
Find me the Top 10 products most similar to given image
{
"name": "MY Blue shirt"
"images": [
[0.2, 0.4, 0.3, 0.5…],
[0.1, 0.5, 0.3, 0.2…],
],
"price": 19.90,
"location": "32.05, 34.680"
}

18. Example cont.
Find me the Top 10 products most similar to given image
that cost $10-$20
FT.SEARCH my_index
"@price[10 20] => [TOP_K 10 @images $BLOB]"
{
"name": "MY Blue shirt"
"images": [
[0.2, 0.4, 0.3, 0.5…],
[0.1, 0.5, 0.3, 0.2…],
],
"price": 19.90,
"location": "32.05, 34.680"
}

19. Example cont.
Find me the Top 10 products most similar to given image
that cost $10-$20
and the store located 10 KM from TLV
FT.SEARCH my_index
"@price[10 15] @location:[32.082, 34.780 10 km]
=> [TOP_K 10 @images $BLOB]"
{
"name": "MY Blue shirt"
"images": [
[0.2, 0.4, 0.3, 0.5…],
[0.1, 0.5, 0.3, 0.2…],
],
"price": 19.90,
"location": "32.05, 34.680"
}

20. Example cont.
Find me the Top 10 products most similar to given image
that cost $10-$20
and the store located 10 KM from TLV sort by Price
FT.SEARCH my_index
"@price[10 15] @location:[32.082, 34.780 10 km]
=> [TOP_K 10 @images $BLOB]"
SORTBY @price ASC
{
"name": "MY Blue shirt"
"images": [
[0.2, 0.4, 0.3, 0.5…],
[0.1, 0.5, 0.3, 0.2…],
],
"price": 19.90,
"location": "32.05, 34.680"
}

21. Graph?
MATCH
(:Product{image:$BLOB})<-[:in]-(:Cart)-[:in]->(p2:Product)
RETURN p2

22. • https://redisearch.io
- https://github.com/RediSearch/redisearch
• https://redisai.io
- https://github.com/RedisAI/VectorSimilarity
- https://redis.com/blog/build-intelligent-apps-redis-vector-similarity-search/
Where to read more? (Preview)

23. Thank you!
Questions?