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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:"
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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:"
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"blog",
"blog/fast-embed-models"
] | 16,076 |
[
"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(54) | [
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"blog",
"blog/fast-embed-models"
] | 16,077 |
[
"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(54) | [
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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(54) | [
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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(54) | [
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"blog/fast-embed-models"
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[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/fast-embed-models"
] | 16,081 |
[
"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(54) | [
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"blog",
"blog/fast-embed-models"
] | 16,082 |
[
"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(54) | [
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] | p | So whatever is your best practices for the Embedding model, make sure you use it when you’re using it with Qdrant or just in isolation as well. | [
"blog",
"blog/fast-embed-models"
] | 16,083 |
[
"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(55) | [
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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(55) | [
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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(55) | [
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] | p | It could also mean that the best match is, let’s say, 0.6 in your domain. | [
"blog",
"blog/fast-embed-models"
] | 16,089 |
[
"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(55) | [
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"blog",
"blog/fast-embed-models"
] | 16,090 |
[
"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(55) | [
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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(55) | [
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"blog",
"blog/fast-embed-models"
] | 16,092 |
[
"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(56) | [
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"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(56) | [
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"blog",
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] | 16,094 |
[
"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(56) | [
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[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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"blog",
"blog/fast-embed-models"
] | 16,097 |
[
"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(57) | [
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] | p | Okay, thanks for that. | [
"blog",
"blog/fast-embed-models"
] | 16,098 |
[
"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(57) | [
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] | p | So there’s a question coming through in the chat from Shreya asking how is the latency in comparison to elasticsearch? | [
"blog",
"blog/fast-embed-models"
] | 16,099 |
[
"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(58) | [
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] | p | I believe that’s a Qdrant benchmark question and I’m not sure how is elastics HNSW index, because I think that will be the fair comparison. | [
"blog",
"blog/fast-embed-models"
] | 16,102 |
[
"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(58) | [
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] | p | I also believe elastics HNSW index puts some limitations on how many vectors they can store with the payload. | [
"blog",
"blog/fast-embed-models"
] | 16,103 |
[
"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(58) | [
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"blog",
"blog/fast-embed-models"
] | 16,104 |
[
"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(58) | [
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] | p | It’s almost like comparing, let’s say, a single page with the entire book, because that’s typically the ratio from what I remember I also might be a few months outdated on this, but I think the intent behind that question is, is Qdrant fast enough for what Qdrant does? | [
"blog",
"blog/fast-embed-models"
] | 16,105 |
[
"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(58) | [
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] | p | It is definitely fast is, which is embedding similarity search. | [
"blog",
"blog/fast-embed-models"
] | 16,106 |
[
"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(58) | [
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] | p | So for that, it’s exceptionally fast. | [
"blog",
"blog/fast-embed-models"
] | 16,107 |
[
"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(58) | [
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] | p | It’s written in Rust and Twitter for all C. Similar tweets uses this at really large scale. | [
"blog",
"blog/fast-embed-models"
] | 16,108 |
[
"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(58) | [
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"blog",
"blog/fast-embed-models"
] | 16,109 |
[
"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(59) | [
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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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[
"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(60) | [
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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(44) | [
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[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | So there’s another question that I’ve got coming through and it’s around the model selection and embedding size. | [
"blog",
"blog/fast-embed-models"
] | 16,118 |
[
"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(62) | [
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] | p | And given the variety of models and the embedding sizes available, how do you determine the most suitable models and embedding sizes? | [
"blog",
"blog/fast-embed-models"
] | 16,119 |
[
"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(62) | [
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] | p | You kind of got into this on how yeah, one thing that you can do to turn the knobs are choosing a different model. | [
"blog",
"blog/fast-embed-models"
] | 16,120 |
[
"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(62) | [
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"blog",
"blog/fast-embed-models"
] | 16,121 |
[
"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(62) | [
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"blog",
"blog/fast-embed-models"
] | 16,122 |
[
"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(63) | [
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[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | There is the academic way of looking at and then there is the engineer way of looking at it, and then there is the hacker way of looking at it. | [
"blog",
"blog/fast-embed-models"
] | 16,124 |
[
"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(63) | [
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] | p | And I will give you all these three answers in that order. | [
"blog",
"blog/fast-embed-models"
] | 16,125 |
[
"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(63) | [
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] | p | So the academic and the gold standard way of doing this would probably look something like this. | [
"blog",
"blog/fast-embed-models"
] | 16,126 |
[
"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(63) | [
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] | p | You will go at a known benchmark, which might be, let’s say, something like Kilt K-I-L-T or multilingual text embedding benchmark, also known as MTEB or Beer, which is beir one of these three benchmarks. | [
"blog",
"blog/fast-embed-models"
] | 16,127 |
[
"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(63) | [
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"blog",
"blog/fast-embed-models"
] | 16,128 |
[
"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(63) | [
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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(63) | [
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[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | So that is where you would start. | [
"blog",
"blog/fast-embed-models"
] | 16,131 |
[
"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(64) | [
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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(64) | [
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"blog",
"blog/fast-embed-models"
] | 16,133 |
[
"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(64) | [
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] | p | But it’s not going to take you, let’s say, a month to do this. | [
"blog",
"blog/fast-embed-models"
] | 16,134 |
[
"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(64) | [
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"blog",
"blog/fast-embed-models"
] | 16,135 |
[
"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(64) | [
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"blog",
"blog/fast-embed-models"
] | 16,136 |
[
"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(64) | [
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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(64) | [
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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:"
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] | p | So for instance, one of the reasons I mentioned binary quantization and product quantization is with something like binary quantization you can get 98% recall, but with 30 to 40 x memory savings because it discards all the extraneous bits and just keeps the zero or one bit of the embedding itself. | [
"blog",
"blog/fast-embed-models"
] | 16,142 |
[
"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(65) | [
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"blog",
"blog/fast-embed-models"
] | 16,143 |
[
"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(65) | [
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"blog",
"blog/fast-embed-models"
] | 16,144 |
[
"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(65) | [
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] | p | So you might want to use that to just massively scale while keeping your budgets as an engineer. | [
"blog",
"blog/fast-embed-models"
] | 16,145 |
[
"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(65) | [
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] | p | Now, in order to do this, you need to have some sense of three numbers, right? | [
"blog",
"blog/fast-embed-models"
] | 16,146 |
[
"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(65) | [
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[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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] | p | Now, for the performance, which is where engineers are most unfamiliar with, I will give the hacker answer, which is this. | [
"blog",
"blog/fast-embed-models"
] | 16,148 |
[
"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(66) | [
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"blog",
"blog/fast-embed-models"
] | 16,149 |
[
"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(66) | [
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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(66) | [
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] | p | Man, so excited for this one, exactly this. | [
"blog",
"blog/fast-embed-models"
] | 16,151 |
[
"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(66) | [
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] | p | Please tell us the hacker answer. | [
"blog",
"blog/fast-embed-models"
] | 16,152 |
[
"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(67) | [
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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(67) | [
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"blog/fast-embed-models"
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[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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"blog/fast-embed-models"
] | 16,155 |
[
"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(67) | [
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"blog",
"blog/fast-embed-models"
] | 16,156 |
[
"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(67) | [
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"blog",
"blog/fast-embed-models"
] | 16,157 |
[
"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(67) | [
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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(67) | [
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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(67) | [
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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(67) | [
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] | p | I think the largest I have used in production is about 400 500 KBS. | [
"blog",
"blog/fast-embed-models"
] | 16,161 |
[
"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(67) | [
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] | p | That’s about it. | [
"blog",
"blog/fast-embed-models"
] | 16,162 |
[
"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(67) | [
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] | p | And that will improve your recall several, several times. | [
"blog",
"blog/fast-embed-models"
] | 16,163 |
[
"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(68) | [
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[
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"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(68) | [
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[
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"Transcript:"
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] | p | And a third bonus hacker trick is if you’re using an LLM, sometimes what you can do is take a question and rewrite it with a prompt and make embeddings from both, and pull candidates from both. | [
"blog",
"blog/fast-embed-models"
] | 16,166 |
[
"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(68) | [
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"blog",
"blog/fast-embed-models"
] | 16,167 |
[
"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(68) | [
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"blog",
"blog/fast-embed-models"
] | 16,168 |
[
"FastEmbed: Fast & Lightweight Embedding Generation - Nirant Kasliwal | Vector Space Talks - Qdrant",
"Transcript:"
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"Transcript:"
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] | p | Okay, dude, I’m not going to lie, that was a lot more than I was expecting for that answer. | [
"blog",
"blog/fast-embed-models"
] | 16,171 |
Subsets and Splits