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[ "On Hybrid Search - Qdrant", "Experiments" ]
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[ "articles", "articles/hybrid-search" ]
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[ "On Hybrid Search - Qdrant", "Experiments" ]
/articles/hybrid-search/
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p
Both Qdrant and BM25 provides N candidates each and
[ "articles", "articles/hybrid-search" ]
3,973
[ "On Hybrid Search - Qdrant", "Experiments" ]
/articles/hybrid-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(3) > p:nth-of-type(2)
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p
ms-marco-MiniLM-L-6-v2 cross encoder performs reranking
[ "articles", "articles/hybrid-search" ]
3,974
[ "On Hybrid Search - Qdrant", "Experiments" ]
/articles/hybrid-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(3) > p:nth-of-type(2)
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on those candidates only.
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3,975
[ "On Hybrid Search - Qdrant", "Experiments" ]
/articles/hybrid-search/
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p
This is an approach that makes it possible to use the power of semantic and keyword based
[ "articles", "articles/hybrid-search" ]
3,976
[ "On Hybrid Search - Qdrant", "Experiments" ]
/articles/hybrid-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(3) > p:nth-of-type(2)
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p
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[ "articles", "articles/hybrid-search" ]
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[ "On Hybrid Search - Qdrant" ]
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(1)
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Quality metrics
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(23)
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There are various ways of how to measure the performance of search engines, and Recommender Systems: Machine Learning
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3,979
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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
Metrics and Business Metrics is a great introduction to that topic.
[ "articles", "articles/hybrid-search" ]
3,980
[ "On Hybrid Search - Qdrant", "Quality metrics" ]
/articles/hybrid-search/
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
I selected the following ones:
[ "articles", "articles/hybrid-search" ]
3,981
[ "On Hybrid Search - Qdrant", "Quality metrics" ]
/articles/hybrid-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(6) > li:nth-of-type(1)
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li
NDCG@5, NDCG@10
[ "articles", "articles/hybrid-search" ]
3,982
[ "On Hybrid Search - Qdrant", "Quality metrics" ]
/articles/hybrid-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(6) > li:nth-of-type(2)
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li
Precision@5, Precision@10
[ "articles", "articles/hybrid-search" ]
3,985
[ "On Hybrid Search - Qdrant", "Quality metrics" ]
/articles/hybrid-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(6) > li:nth-of-type(5)
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li
Recall@5, Recall@10
[ "articles", "articles/hybrid-search" ]
3,986
[ "On Hybrid Search - Qdrant", "Quality metrics" ]
/articles/hybrid-search/
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 both systems return a score for each result, we could use DCG and NDCG metrics.
[ "articles", "articles/hybrid-search" ]
3,987
[ "On Hybrid Search - Qdrant", "Quality metrics" ]
/articles/hybrid-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
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normalized be default.
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p
We performed the normalization to a range [0, 1] by dividing each score by the maximum
[ "articles", "articles/hybrid-search" ]
3,990
[ "On Hybrid Search - Qdrant", "Quality metrics" ]
/articles/hybrid-search/
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
score returned for that query.
[ "articles", "articles/hybrid-search" ]
3,991
[ "On Hybrid Search - Qdrant" ]
/articles/hybrid-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(2)
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h3
Datasets
[ "articles", "articles/hybrid-search" ]
3,992
[ "On Hybrid Search - Qdrant", "Datasets" ]
/articles/hybrid-search/
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
There are various benchmarks for search relevance available.
[ "articles", "articles/hybrid-search" ]
3,993
[ "On Hybrid Search - Qdrant", "Datasets" ]
/articles/hybrid-search/
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
Full-text search has been a strong baseline for
[ "articles", "articles/hybrid-search" ]
3,994
[ "On Hybrid Search - Qdrant", "Datasets" ]
/articles/hybrid-search/
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
most of them.
[ "articles", "articles/hybrid-search" ]
3,995
[ "On Hybrid Search - Qdrant", "Datasets" ]
/articles/hybrid-search/
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
However, there are also cases in which semantic search works better by default.
[ "articles", "articles/hybrid-search" ]
3,996
[ "On Hybrid Search - Qdrant", "Datasets" ]
/articles/hybrid-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25)
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[ "articles", "articles/hybrid-search" ]
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[ "On Hybrid Search - Qdrant", "Datasets" ]
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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
I’m performing zero shot search, meaning our models didn’t have any prior exposure to the benchmark datasets,
[ "articles", "articles/hybrid-search" ]
3,998
[ "On Hybrid Search - Qdrant", "Datasets" ]
/articles/hybrid-search/
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
so this is effectively an out-of-domain search.
[ "articles", "articles/hybrid-search" ]
3,999
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(38)
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p
And that’s because of all the multiple processing and ONNX runtime itself at some point in the future.
[ "blog", "blog/fast-embed-models" ]
16,000
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(38)
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p
We also want to support GPUs.
[ "blog", "blog/fast-embed-models" ]
16,001
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(38)
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p
We had some configuration issues on different Nvidia configurations.
[ "blog", "blog/fast-embed-models" ]
16,002
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(38)
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As the GPU changes, the OnX runtime does not seamlessly change the GPU.
[ "blog", "blog/fast-embed-models" ]
16,003
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(39)
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Nirant Kasliwal:
[ "blog", "blog/fast-embed-models" ]
16,004
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(39)
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p
So that is why we do not allow that as a provider.
[ "blog", "blog/fast-embed-models" ]
16,005
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(39)
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p
But you can pass that.
[ "blog", "blog/fast-embed-models" ]
16,006
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(39)
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p
It’s not prohibited, it’s just not a default.
[ "blog", "blog/fast-embed-models" ]
16,007
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(39)
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p
We want to make sure your default is always available and will be available in the happy path, always.
[ "blog", "blog/fast-embed-models" ]
16,008
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(39)
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p
And we quantize the models for you.
[ "blog", "blog/fast-embed-models" ]
16,009
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(39)
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p
So when we quantize, what it means is we do a bunch of tricks supported by a huge shout out to hugging faces optimum.
[ "blog", "blog/fast-embed-models" ]
16,010
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(39)
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p
So we do a bunch of optimizations in the quantization, which is we compress some activations, for instance, gelu.
[ "blog", "blog/fast-embed-models" ]
16,011
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(39)
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p
We also do some graph optimizations and we don’t really do a lot of dropping the bits, which is let’s say 32 to 16 or 64 to 32 kind of quantization only where required.
[ "blog", "blog/fast-embed-models" ]
16,012
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(40)
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p
Nirant Kasliwal:
[ "blog", "blog/fast-embed-models" ]
16,013
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(40)
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p
Most of these gains come from the graph optimizations themselves.
[ "blog", "blog/fast-embed-models" ]
16,014
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(40)
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p
So there are different modes which optimum itself calls out.
[ "blog", "blog/fast-embed-models" ]
16,015
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(40)
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p
And if there are folks interested in that, happy to share docs and details around that.
[ "blog", "blog/fast-embed-models" ]
16,016
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(40)
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p
Yeah, that’s about it.
[ "blog", "blog/fast-embed-models" ]
16,017
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(40)
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p
Those are the two things which we do from which we get bulk of these speed gains.
[ "blog", "blog/fast-embed-models" ]
16,018
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(40)
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p
And I think this goes back to the question which you opened with.
[ "blog", "blog/fast-embed-models" ]
16,019
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(40)
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p
Yes, we do want to support multimodal.
[ "blog", "blog/fast-embed-models" ]
16,020
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(40)
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p
We are looking at how we can do an on and export of Clip, which is as robust as Clip.
[ "blog", "blog/fast-embed-models" ]
16,021
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(41)
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p
Nirant Kasliwal:
[ "blog", "blog/fast-embed-models" ]
16,022
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(41)
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p
So far we have not found anything.
[ "blog", "blog/fast-embed-models" ]
16,023
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(41)
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p
I’ve spent some time looking at this, the quality of life upgrades.
[ "blog", "blog/fast-embed-models" ]
16,024
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(41)
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p
So far, most of our model downloads have been through Google cloud storage hosted by Qdrant.
[ "blog", "blog/fast-embed-models" ]
16,025
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(41)
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p
We want to support hugging Face hub so that we can launch new models much, much faster.
[ "blog", "blog/fast-embed-models" ]
16,026
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(41)
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p
So we will do that soon.
[ "blog", "blog/fast-embed-models" ]
16,027
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(41)
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p
And the next thing is, as I called out, we always want to take performance as a first class citizen.
[ "blog", "blog/fast-embed-models" ]
16,028
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(41)
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p
So we are looking at how we can allow you to change or adapt frozen Embeddings, let’s say open a Embedding or any other model to your specific domain.
[ "blog", "blog/fast-embed-models" ]
16,029
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(41)
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p
So maybe a separate toolkit within Fast Embed which is optional and not a part of the default path, because this is not something which you will use all the time.
[ "blog", "blog/fast-embed-models" ]
16,030
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(42)
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p
Nirant Kasliwal:
[ "blog", "blog/fast-embed-models" ]
16,031
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(42)
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p
We want to make sure that your training and experience parts are separate.
[ "blog", "blog/fast-embed-models" ]
16,032
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(42)
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p
So we will do that.
[ "blog", "blog/fast-embed-models" ]
16,033
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(42)
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p
Yeah, that’s it.
[ "blog", "blog/fast-embed-models" ]
16,034
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(42)
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Fast and sweet.
[ "blog", "blog/fast-embed-models" ]
16,035
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(43)
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Demetrios:
[ "blog", "blog/fast-embed-models" ]
16,036
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(43)
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p
Amazing.
[ "blog", "blog/fast-embed-models" ]
16,037
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(43)
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Like FastEmbed.
[ "blog", "blog/fast-embed-models" ]
16,038
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
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html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(44)
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Nirant Kasliwal:
[ "blog", "blog/fast-embed-models" ]
16,039
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(44)
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p
Yes.
[ "blog", "blog/fast-embed-models" ]
16,040
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(45)
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Demetrios:
[ "blog", "blog/fast-embed-models" ]
16,041
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(45)
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p
There was somebody that talked about how you need to be good at your puns and that might be the best thing, best brag worthy stuff you’ve got.
[ "blog", "blog/fast-embed-models" ]
16,042
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(45)
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p
There’s also a question coming through that I want to ask you.
[ "blog", "blog/fast-embed-models" ]
16,043
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(45)
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p
Is it true that when we use Qdrant client add Fast Embedding is included?
[ "blog", "blog/fast-embed-models" ]
16,044
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(45)
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p
We don’t have to do it?
[ "blog", "blog/fast-embed-models" ]
16,045
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(46)
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p
Nirant Kasliwal:
[ "blog", "blog/fast-embed-models" ]
16,046
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(46)
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p
What do you mean by do it?
[ "blog", "blog/fast-embed-models" ]
16,047
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(46)
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p
As in you don’t have to specify a Fast Embed model?
[ "blog", "blog/fast-embed-models" ]
16,048
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(47)
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p
Demetrios:
[ "blog", "blog/fast-embed-models" ]
16,049
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(47)
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p
Yeah, I think it’s more just like you don’t have to add it on to Qdrant in any way or this is completely separated.
[ "blog", "blog/fast-embed-models" ]
16,050
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(48)
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p
Nirant Kasliwal:
[ "blog", "blog/fast-embed-models" ]
16,051
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(48)
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p
So this is client side.
[ "blog", "blog/fast-embed-models" ]
16,052
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(48)
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p
You own all your data and even when you compress it and send us all the Embedding creation happens on your own compute.
[ "blog", "blog/fast-embed-models" ]
16,053
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(48)
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p
This Embedding creation does not happen on Cauldron cloud, it happens on your own compute.
[ "blog", "blog/fast-embed-models" ]
16,054
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(48)
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p
It’s consistent with the idea that you should have as much control as possible.
[ "blog", "blog/fast-embed-models" ]
16,055
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(48)
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p
This is also why, as of now at least, Fast Embed is not a dedicated server.
[ "blog", "blog/fast-embed-models" ]
16,056
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(48)
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p
We do not want you to be running two different docker images for Qdrant and Fast Embed.
[ "blog", "blog/fast-embed-models" ]
16,057
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(48)
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p
Or let’s say two different ports for Qdrant and Discord within the sorry, Qdrant and Fast Embed in the same docker image or server.
[ "blog", "blog/fast-embed-models" ]
16,058
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(48)
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p
So, yeah, that is more chaos than we would like.
[ "blog", "blog/fast-embed-models" ]
16,059
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(49)
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p
Demetrios:
[ "blog", "blog/fast-embed-models" ]
16,060
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(49)
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p
Yeah, and I think if I understood it, I understood that question a little bit differently, where it’s just like this comes with Qdrant out of the box.
[ "blog", "blog/fast-embed-models" ]
16,061
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(50)
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p
Nirant Kasliwal:
[ "blog", "blog/fast-embed-models" ]
16,062
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(50)
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p
Yes, I think that’s a good way to look at it.
[ "blog", "blog/fast-embed-models" ]
16,063
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(50)
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p
We set all the defaults for you, we select good practices for you and that should work in a vast majority of cases based on the MTEB benchmark, but we cannot guarantee that it will work for every scenario.
[ "blog", "blog/fast-embed-models" ]
16,064
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(50)
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p
Let’s say our default model is picked for English and it’s mostly tested on open domain open web data.
[ "blog", "blog/fast-embed-models" ]
16,065
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(50)
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p
So, for instance, if you’re doing something domain specific, like medical or legal, it might not work that well.
[ "blog", "blog/fast-embed-models" ]
16,066
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(50)
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p
So that is where you might want to still make your own Embeddings.
[ "blog", "blog/fast-embed-models" ]
16,067
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(50)
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p
So that’s the edge case here.
[ "blog", "blog/fast-embed-models" ]
16,068
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(51)
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p
Demetrios:
[ "blog", "blog/fast-embed-models" ]
16,069
[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(51)
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p
What are some of the other knobs that you might want to be turning when you’re looking at using this.
[ "blog", "blog/fast-embed-models" ]
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[ "FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant", "Transcript:" ]
/blog/fast-embed-models/
html > body > div:nth-of-type(1) > div > article > div:nth-of-type(2) > div:nth-of-type(3) > div > p:nth-of-type(52)
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p
Nirant Kasliwal:
[ "blog", "blog/fast-embed-models" ]
16,071