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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(33) | [
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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(34) | [
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"blog",
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[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | How does this play with that? | [
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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(34) | [
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] | p | As of now, we only support models which are open source so that we can serve those models directly. | [
"blog",
"blog/fast-embed-models"
] | 15,978 |
[
"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(35) | [
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"blog",
"blog/fast-embed-models"
] | 15,979 |
[
"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(35) | [
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"blog",
"blog/fast-embed-models"
] | 15,980 |
[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/fast-embed-models"
] | 15,981 |
[
"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(35) | [
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"blog",
"blog/fast-embed-models"
] | 15,982 |
[
"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(35) | [
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] | p | As I ask, if we have folks who would love to try coherent embed V three, we’d use that. | [
"blog",
"blog/fast-embed-models"
] | 15,983 |
[
"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(35) | [
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] | p | Also, I think Nils called out that coherent embed V three is compatible with binary quantization. | [
"blog",
"blog/fast-embed-models"
] | 15,984 |
[
"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(35) | [
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] | p | And I think that’s the only embedding which officially supports that. | [
"blog",
"blog/fast-embed-models"
] | 15,985 |
[
"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(36) | [
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[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
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] | 15,987 |
[
"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(36) | [
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"blog",
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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(36) | [
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] | p | So Qdrant supports that. | [
"blog",
"blog/fast-embed-models"
] | 15,989 |
[
"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(36) | [
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] | p | So please of that might be worth it because it saves about 30 x in memory costs. | [
"blog",
"blog/fast-embed-models"
] | 15,990 |
[
"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(36) | [
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] | p | So that’s quite powerful. | [
"blog",
"blog/fast-embed-models"
] | 15,991 |
[
"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(37) | [
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[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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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(38) | [
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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(38) | [
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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(38) | [
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[
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[
"Qdrant under the hood: Product Quantization - Qdrant",
"Measuring the distance"
] | /articles/product-quantization/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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[
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"Qdrant implementation"
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[
"Qdrant under the hood: Product Quantization - Qdrant"
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] | p | As usual, we did some benchmarks to give you a brief understanding of what you may expect. | [
"articles",
"articles/product-quantization"
] | 3,013 |
[
"Qdrant under the hood: Product Quantization - Qdrant",
"Benchmarks"
] | /articles/product-quantization/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19) | [
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] | p | Again, we reused the same pipeline as in the other benchmarks we published. | [
"articles",
"articles/product-quantization"
] | 3,014 |
[
"Qdrant under the hood: Product Quantization - Qdrant",
"Benchmarks"
] | /articles/product-quantization/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19) | [
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] | p | The results are summarized in the tables: | [
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] | p | In circumstances that do not align with the above, Scalar Quantization should be the preferred choice. | [
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"articles/product-quantization"
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[
"Qdrant under the hood: Product Quantization - Qdrant",
"Good practices"
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"benchmarks/filtered-search-benchmark"
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[
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"benchmarks",
"benchmarks/filtered-search-benchmark"
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[
"- Qdrant",
"Filtered Results"
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"benchmarks",
"benchmarks/filtered-search-benchmark"
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[
"- Qdrant",
"Filtered Results"
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] | p | Speed boost - for some engines/queries, the filtered search is faster than the unfiltered one. | [
"benchmarks",
"benchmarks/filtered-search-benchmark"
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[
"- Qdrant",
"Filtered Results"
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"benchmarks",
"benchmarks/filtered-search-benchmark"
] | 3,045 |
[
"- Qdrant",
"Filtered Results"
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"benchmarks",
"benchmarks/filtered-search-benchmark"
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[
"- Qdrant",
"Filtered Results"
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] | p | Speed downturn - some engines struggle to keep high RPS, it might be related to the requirement of building a filtering mask for the dataset, as described above. | [
"benchmarks",
"benchmarks/filtered-search-benchmark"
] | 3,047 |
[
"- Qdrant",
"Filtered Results"
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] | li | Accuracy collapse - some engines are loosing accuracy dramatically under some filters. | [
"benchmarks",
"benchmarks/filtered-search-benchmark"
] | 3,048 |
[
"- Qdrant",
"Filtered Results"
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] | p | Qdrant avoids all these problems and also benefits from the speed boost, as it implements an advanced query planning strategy. | [
"benchmarks",
"benchmarks/filtered-search-benchmark"
] | 3,052 |
[
"Qdrant under the hood: Scalar Quantization - Qdrant"
] | /articles/scalar-quantization/ | 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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[
"Qdrant under the hood: Scalar Quantization - Qdrant",
"Qdrant under the hood: Scalar Quantization"
] | /articles/scalar-quantization/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2) | [
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0.06365349143743515,
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-0.034168895334005356
] | p | So a single number needs 4 bytes of the memory and a 512-dimensional vector occupies | [
"articles",
"articles/scalar-quantization"
] | 3,071 |
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