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[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Improved Storage Efficiency"
] |
/articles/binary-quantization/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20)
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[
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[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Improved Storage Efficiency"
] |
/articles/binary-quantization/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
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p
|
For 100K OpenAI Embedding (ada-002) vectors we would need 900 Megabytes of RAM and disk space.
|
[
"articles",
"articles/binary-quantization"
] | 1,601 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Improved Storage Efficiency"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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p
|
This consumption can start to add up rapidly as you create multiple collections or add more items to the database.
|
[
"articles",
"articles/binary-quantization"
] | 1,602 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Improved Storage Efficiency"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
With binary quantization, those same 100K OpenAI vectors only require 128 MB of RAM.
|
[
"articles",
"articles/binary-quantization"
] | 1,603 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Improved Storage Efficiency"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
We benchmarked this result using methods similar to those covered in our Scalar Quantization memory estimation.
|
[
"articles",
"articles/binary-quantization"
] | 1,604 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Improved Storage Efficiency"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(23)
|
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p
|
This reduction in RAM needed is achieved through the compression that happens in the binary conversion.
|
[
"articles",
"articles/binary-quantization"
] | 1,605 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Improved Storage Efficiency"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(23)
|
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p
|
Instead of putting the HNSW index for the full vectors into RAM, we just put the binary vectors into RAM, use them for the initial oversampled search, and then use the HNSW full index of the oversampled results for the final precise search.
|
[
"articles",
"articles/binary-quantization"
] | 1,606 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Improved Storage Efficiency"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(23)
|
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p
|
All of this happens under the hoods without any intervention needed on your part.
|
[
"articles",
"articles/binary-quantization"
] | 1,607 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h4:nth-of-type(2)
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h4
|
When should you not use BQ?
|
[
"articles",
"articles/binary-quantization"
] | 1,608 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"When should you not use BQ?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
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p
|
Since this method exploits the over-parameterization of embedding, you can expect poorer results for small embeddings i.e. less than 1024 dimensions.
|
[
"articles",
"articles/binary-quantization"
] | 1,609 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"When should you not use BQ?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
|
With the smaller number of elements, there is not enough information maintained in the binary vector to achieve good results.
|
[
"articles",
"articles/binary-quantization"
] | 1,610 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"When should you not use BQ?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25)
|
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p
|
You will still get faster boolean operations and reduced RAM usage, but the accuracy degradation might be too high.
|
[
"articles",
"articles/binary-quantization"
] | 1,611 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(4)
|
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h2
|
Sample Implementation
|
[
"articles",
"articles/binary-quantization"
] | 1,612 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Sample Implementation"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(26)
|
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p
|
Now that we have introduced you to binary quantization, let’s try our a basic implementation.
|
[
"articles",
"articles/binary-quantization"
] | 1,613 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Sample Implementation"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(26)
|
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p
|
In this example, we will be using OpenAI and Cohere with Qdrant.
|
[
"articles",
"articles/binary-quantization"
] | 1,614 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h4:nth-of-type(3)
|
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h4
|
Create a collection with Binary Quantization enabled
|
[
"articles",
"articles/binary-quantization"
] | 1,615 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Create a collection with Binary Quantization enabled"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
|
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p
|
Here is what you should do at indexing time when you create the collection:
|
[
"articles",
"articles/binary-quantization"
] | 1,616 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Create a collection with Binary Quantization enabled"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(1)
|
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li
|
We store all the “full” vectors on disk.
|
[
"articles",
"articles/binary-quantization"
] | 1,617 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Create a collection with Binary Quantization enabled"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(2)
|
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li
|
Then we set the binary embeddings to be in RAM.
|
[
"articles",
"articles/binary-quantization"
] | 1,618 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Create a collection with Binary Quantization enabled"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28)
|
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p
|
By default, both the full vectors and BQ get stored in RAM.
|
[
"articles",
"articles/binary-quantization"
] | 1,619 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Create a collection with Binary Quantization enabled"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28)
|
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p
|
We move the full vectors to disk because this saves us memory and allows us to store more vectors in RAM.
|
[
"articles",
"articles/binary-quantization"
] | 1,620 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Create a collection with Binary Quantization enabled"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28)
|
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p
|
By doing this, we explicitly move the binary vectors to memory by setting always_ram=True.
|
[
"articles",
"articles/binary-quantization"
] | 1,621 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h4:nth-of-type(4)
|
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h4
|
What is happening in the OptimizerConfig?
|
[
"articles",
"articles/binary-quantization"
] | 1,622 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What is happening in the OptimizerConfig?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29)
|
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0.021114550530910492
] |
p
|
We’re setting indexing_threshold to 0 i.e. disabling the indexing to zero.
|
[
"articles",
"articles/binary-quantization"
] | 1,623 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What is happening in the OptimizerConfig?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29)
|
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p
|
This allows faster uploads of vectors and payloads.
|
[
"articles",
"articles/binary-quantization"
] | 1,624 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What is happening in the OptimizerConfig?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29)
|
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p
|
We will turn it back on down below, once all the data is loaded
|
[
"articles",
"articles/binary-quantization"
] | 1,625 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What is happening in the OptimizerConfig?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(30)
|
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p
|
We’re changing the default_segment_number to 5.
|
[
"articles",
"articles/binary-quantization"
] | 1,626 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What is happening in the OptimizerConfig?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(30)
|
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p
|
Segment numbers influence the number of graph nodes in the underlying HNSW index, thereby indirectly influencing the memory efficiency.
|
[
"articles",
"articles/binary-quantization"
] | 1,627 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h4:nth-of-type(5)
|
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h4
|
Next, we upload our vectors to this and then enable indexing:
|
[
"articles",
"articles/binary-quantization"
] | 1,628 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Next, we upload our vectors to this and then enable indexing:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(31)
|
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p
|
Enable indexing again:
|
[
"articles",
"articles/binary-quantization"
] | 1,629 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h4:nth-of-type(6)
|
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h4
|
Configure the search parameters:
|
[
"articles",
"articles/binary-quantization"
] | 1,630 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Configure the search parameters:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(32)
|
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p
|
When setting search parameters, we specify that we want to use oversampling and rescore.
|
[
"articles",
"articles/binary-quantization"
] | 1,631 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Configure the search parameters:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(33)
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p
|
After Qdrant pulls the oversampled vectors set, the full vectors which will be, say 1536 dimensions for OpenAI will then be pulled up from disk.
|
[
"articles",
"articles/binary-quantization"
] | 1,632 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Configure the search parameters:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(33)
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p
|
Qdrant computes the nearest neighbor with the query vector and returns the accurate, rescored order.
|
[
"articles",
"articles/binary-quantization"
] | 1,633 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Configure the search parameters:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(33)
|
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p
|
This method produces much more accurate results.
|
[
"articles",
"articles/binary-quantization"
] | 1,634 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Configure the search parameters:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(33)
|
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p
|
We enabled this by setting rescore=True.
|
[
"articles",
"articles/binary-quantization"
] | 1,635 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Configure the search parameters:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(34)
|
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p
|
These two parameters are how you are going to balance speed versus accuracy.
|
[
"articles",
"articles/binary-quantization"
] | 1,636 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Configure the search parameters:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(34)
|
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p
|
The larger the size of your oversample, the more items you need to read from disk and the more elements you have to search with the relatively slower full vector index.
|
[
"articles",
"articles/binary-quantization"
] | 1,637 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Configure the search parameters:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(34)
|
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p
|
On the other hand, doing this will produce more accurate results.
|
[
"articles",
"articles/binary-quantization"
] | 1,638 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Configure the search parameters:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(35)
|
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p
|
If you have lower accuracy requirements you can even try doing a small oversample without rescoring.
|
[
"articles",
"articles/binary-quantization"
] | 1,639 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Configure the search parameters:"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(35)
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p
|
Or maybe, for your data set combined with your accuracy versus speed requirements you can just search the binary index and no rescoring, i.e. leaving those two parameters out of the search query.
|
[
"articles",
"articles/binary-quantization"
] | 1,640 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(5)
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h2
|
Benchmark results
|
[
"articles",
"articles/binary-quantization"
] | 1,641 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(36)
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p
|
We retrieved some early results on the relationship between limit and oversampling using the the DBPedia OpenAI 1M vector dataset.
|
[
"articles",
"articles/binary-quantization"
] | 1,642 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(36)
|
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p
|
We ran all these experiments on a Qdrant instance where 100K vectors were indexed and used 100 random queries.
|
[
"articles",
"articles/binary-quantization"
] | 1,643 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(37)
|
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p
|
We varied the 3 parameters that will affect query time and accuracy: limit, rescore and oversampling.
|
[
"articles",
"articles/binary-quantization"
] | 1,644 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(37)
|
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p
|
We offer these as an initial exploration of this new feature.
|
[
"articles",
"articles/binary-quantization"
] | 1,645 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(37)
|
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p
|
You are highly encouraged to reproduce these experiments with your data sets.
|
[
"articles",
"articles/binary-quantization"
] | 1,646 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > blockquote > p
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p
|
Aside: Since this is a new innovation in vector databases, we are keen to hear feedback and results.
|
[
"articles",
"articles/binary-quantization"
] | 1,647 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > blockquote > p
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[
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[
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(38)
|
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p
|
Oversampling:
|
[
"articles",
"articles/binary-quantization"
] | 1,649 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(38)
|
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p
|
In the figure below, we illustrate the relationship between recall and number of candidates:
|
[
"articles",
"articles/binary-quantization"
] | 1,650 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(40)
|
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p
|
We see that “correct” results i.e. recall increases as the number of potential “candidates” increase (limit x oversampling).
|
[
"articles",
"articles/binary-quantization"
] | 1,651 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(40)
|
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p
|
To highlight the impact of changing the limit, different limit values are broken apart into different curves.
|
[
"articles",
"articles/binary-quantization"
] | 1,652 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(40)
|
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p
|
For example, we see that the lowest recall for limit 50 is around 94 correct, with 100 candidates.
|
[
"articles",
"articles/binary-quantization"
] | 1,653 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(40)
|
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p
|
This also implies we used an oversampling of 2.0
|
[
"articles",
"articles/binary-quantization"
] | 1,654 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(41)
|
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p
|
As oversampling increases, we see a general improvement in results – but that does not hold in every case.
|
[
"articles",
"articles/binary-quantization"
] | 1,655 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(42)
|
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p
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Rescore:
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[
"articles",
"articles/binary-quantization"
] | 1,656 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(42)
|
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p
|
As expected, rescoring increases the time it takes to return a query.
|
[
"articles",
"articles/binary-quantization"
] | 1,657 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(42)
|
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p
|
We also repeated the experiment with oversampling except this time we looked at how rescore impacted result accuracy.
|
[
"articles",
"articles/binary-quantization"
] | 1,658 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(44)
|
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[
"articles",
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[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Benchmark results"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(44)
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p
|
We experiment with limits from Top 1 to Top 50 and we are able to get to 100% recall at limit 50, with rescore=True, in an index with 100K vectors.
|
[
"articles",
"articles/binary-quantization"
] | 1,660 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(6)
|
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h2
|
Recommendations
|
[
"articles",
"articles/binary-quantization"
] | 1,661 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Recommendations"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(45)
|
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p
|
Quantization gives you the option to make tradeoffs against other parameters:
|
[
"articles",
"articles/binary-quantization"
] | 1,662 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Recommendations"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(45)
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p
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Dimension count/embedding size
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[
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[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Recommendations"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(45)
|
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p
|
Throughput and Latency requirements
|
[
"articles",
"articles/binary-quantization"
] | 1,664 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Recommendations"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(45)
|
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p
|
Recall requirements
|
[
"articles",
"articles/binary-quantization"
] | 1,665 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Recommendations"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(46)
|
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p
|
If you’re working with OpenAI or Cohere embeddings, we recommend the following oversampling settings:
|
[
"articles",
"articles/binary-quantization"
] | 1,666 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Recommendations"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(47)
|
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p
|
If you determine that binary quantization is appropriate for your datasets and queries then we suggest the following:
|
[
"articles",
"articles/binary-quantization"
] | 1,667 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"Recommendations"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1)
|
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(2)
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(3)
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(4)
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(7)
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h2
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What’s next?
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[
"articles",
"articles/binary-quantization"
] | 1,672 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What’s next?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(48)
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p
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Binary quantization is exceptional if you need to work with large volumes of data under high recall expectations.
|
[
"articles",
"articles/binary-quantization"
] | 1,673 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What’s next?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(48)
|
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p
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You can try this feature either by spinning up a Qdrant container image locally or, having us create one for you through a free account in our cloud hosted service.
|
[
"articles",
"articles/binary-quantization"
] | 1,674 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What’s next?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(49)
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p
|
The article gives examples of data sets and configuration you can use to get going.
|
[
"articles",
"articles/binary-quantization"
] | 1,675 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What’s next?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(49)
|
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p
|
Our documentation covers adding the data to your Qdrant instance as well as creating your indices.
|
[
"articles",
"articles/binary-quantization"
] | 1,676 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What’s next?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(50)
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p
|
If you have any feedback, drop us a note on Twitter or LinkedIn to tell us about your results.
|
[
"articles",
"articles/binary-quantization"
] | 1,677 |
[
"Binary Quantization - Vector Search, 40x Faster - Qdrant",
"What’s next?"
] |
/articles/binary-quantization/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(50)
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p
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[
"articles",
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] | 1,678 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(1)
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(2)
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li
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[
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[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(3)
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(2)
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/articles/food-discovery-demo/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(5)
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li
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Food Discovery Demo
|
[
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"articles/food-discovery-demo"
] | 1,683 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h1
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h1
|
Food Discovery Demo
|
[
"articles",
"articles/food-discovery-demo"
] | 1,684 |
[
"Food Discovery Demo - Qdrant",
"Food Discovery Demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
|
Not every search journey begins with a specific destination in mind.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,685 |
[
"Food Discovery Demo - Qdrant",
"Food Discovery Demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
|
Sometimes, you just want to explore and see what’s out there and what you might like.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,686 |
[
"Food Discovery Demo - Qdrant",
"Food Discovery Demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
|
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p
|
This is especially true when it comes to food.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,687 |
[
"Food Discovery Demo - Qdrant",
"Food Discovery Demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
|
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p
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You might be craving something sweet, but you don’t know what.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,688 |
[
"Food Discovery Demo - Qdrant",
"Food Discovery Demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
|
You might be also looking for a new dish to try,
|
[
"articles",
"articles/food-discovery-demo"
] | 1,689 |
[
"Food Discovery Demo - Qdrant",
"Food Discovery Demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
|
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p
|
and you just want to see the options available.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,690 |
[
"Food Discovery Demo - Qdrant",
"Food Discovery Demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
|
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p
|
In these cases, it’s impossible to express your needs in a textual query, as the thing you are looking for is not
|
[
"articles",
"articles/food-discovery-demo"
] | 1,691 |
[
"Food Discovery Demo - Qdrant",
"Food Discovery Demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
|
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/articles/food-discovery-demo/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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Qdrant’s semantic search for images is useful when you have a hard time expressing your tastes in words.
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[
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[
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/articles/food-discovery-demo/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(1)
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h2
|
General architecture
|
[
"articles",
"articles/food-discovery-demo"
] | 1,694 |
[
"Food Discovery Demo - Qdrant",
"General architecture"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
|
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p
|
We are happy to announce a refreshed version of our Food Discovery Demo.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,695 |
[
"Food Discovery Demo - Qdrant",
"General architecture"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
|
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p
|
This time available as an open source project,
|
[
"articles",
"articles/food-discovery-demo"
] | 1,696 |
[
"Food Discovery Demo - Qdrant",
"General architecture"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
|
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p
|
so you can easily deploy it on your own and play with it.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,697 |
[
"Food Discovery Demo - Qdrant",
"General architecture"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
|
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p
|
If you prefer to dive into the source code directly, then feel free to check out the GitHub repository
|
[
"articles",
"articles/food-discovery-demo"
] | 1,698 |
[
"Food Discovery Demo - Qdrant",
"General architecture"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
|
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] |
p
|
.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,699 |
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