Vector-io compatible Datasets
Collection
These datasets can be loaded into your vector database with a single line bash command • 15 items • Updated • 3
Datasets:
titles sequencelengths 1 2 | url stringclasses 222
values | location stringlengths 59 197 | vector sequencelengths 384 384 | tag stringclasses 8
values | text stringlengths 1 961 | sections sequencelengths 1 3 | id int64 0 18.8k |
|---|---|---|---|---|---|---|---|
[
"Explore - Qdrant",
"Context search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(40) | [
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0.11492646485567093,
0.059214998036623,
0.01834903471171856,
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0.015808872878551483,
0.030076848343014717,
0.02832612209022045,
-0.0061395... | p | $$ | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 7,000 |
[
"Explore - Qdrant",
"Context search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(40) | [
0.05739787966012955,
0.0771348774433136,
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0.056607626378536224,
0.08698386698961258,
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0.06096440553665161,
0.01727299764752388,
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0.000010108810784004163,
-0.06308406591415405,
0.07994973659515381,
0.0028... | p | \text{context score} = \sum \min(s(v^+_i) - s(v^-_i), 0.0) | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 7,001 |
[
"Explore - Qdrant",
"Context search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(40) | [
-0.0028162840753793716,
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0.03242453560233116,
0.06413580477237701,
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0.11492646485567093,
0.059214998036623,
0.01834903471171856,
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0.015808872878551483,
0.030076848343014717,
0.02832612209022045,
-0.0061395... | p | $$ | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 7,002 |
[
"Explore - Qdrant",
"Context search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(41) | [
-0.02868049591779709,
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0.0360620841383934,
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0.04643014445900917,
0.033124011009931564,
0.05475998669862747,
0.05363... | p | Where $v^+_i$ and $v^-_i$ are the positive and negative examples of each pair, and $s(v)$ is the similarity function. | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 7,003 |
[
"Explore - Qdrant",
"Context search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(42) | [
-0.016355641186237335,
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0.07600216567516327,
-0.0654485896229744,
0.026526523754000664,
-0.010834446176886559,
0.09623891115188599,
-0.... | p | Using this kind of search, you can expect the output to not necessarily be around a single point, but rather, to be any point that isn’t closer to a negative example, which creates a constrained diverse result. | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 7,004 |
[
"Explore - Qdrant",
"Context search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(42) | [
-0.10629843920469284,
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0.009514844045042992,
0.017439192160964012,
0.07399658858776093,
0.0008232271648012102,
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0.018329406157135963,
0.06397642195224762,
0.07372768968343735,
0.06382... | p | So, even when the API is not called recommend, recommendation systems can also use this approach and adapt it for their specific use-cases. | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 7,005 |
[
"Explore - Qdrant",
"Context search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(36) | [
0.009657991118729115,
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0.005418023094534874,
0.06800470501184464,
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0.020690567791461945,
0.02639947272837162,
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0.018698764964938164,
0.05972754582762718,
0.0... | p | Example: | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 7,006 |
[
"Explore - Qdrant",
"Context search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > aside:nth-of-type(5) > ul > li:nth-of-type(1) | [
-0.05477146431803703,
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0.03811099752783775,
0.042935293167829514,
0.03820614516735077,
0.0008718445897102356,
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0.03002895973622799,
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0.03169797360897064,
0.02395111694931984,
0.12125977128744125,
-0.0728668... | li | When providing ids as examples, they will be excluded from the results. | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 7,007 |
[
"Explore - Qdrant",
"Context search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > aside:nth-of-type(5) > ul > li:nth-of-type(2) | [
0.06362029910087585,
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0.01409970223903656,
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0.025520069524645805,
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0.05720176175236702,
0.08540219068527222,
0.03358956798911095,
-0.0061394586227834225,
0.11556925624608994,
0.026898033916950226,
0.06651... | li | Score is always in descending order (larger is better), regardless of the metric used. | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 7,008 |
[
"Explore - Qdrant",
"Context search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > aside:nth-of-type(5) > ul > li:nth-of-type(3) | [
0.05482957512140274,
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0.004029820207506418,
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0.01832425966858864,
0.013383130542933941,
0.048527371138334274,
0.07761535048484802,
0.02501688338816166,
-0.0250812117010355,
-0.0019830737728625536,
0.019029738381505013,
0.05136... | li | Best possible score is 0.0, and it is normal that many points get this score. | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 7,009 |
[
"Filtrable HNSW - Qdrant"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(1) > li:nth-of-type(1) | [
0.0409557968378067,
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0.020037280395627022,
0.018771959468722343,
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0.06403844058513641,
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-0.03519021347165108,
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-0.027398349717259407,
0.007306049577891827,
-0.... | li | Home | [
"articles",
"articles/filtrable-hnsw"
] | 7,010 |
[
"Filtrable HNSW - Qdrant"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(1) > li:nth-of-type(2) | [
-0.1359460949897766,
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0.01159171387553215,
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0.02118542790412903,
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0.02877473458647728,
-0.03753074258565903,
0.028667796403169632,
-0.06563723087310791,
0.017261670902371407,
0.044... | li | / | [
"articles",
"articles/filtrable-hnsw"
] | 7,011 |
[
"Filtrable HNSW - Qdrant"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(1) > li:nth-of-type(3) | [
-0.007825803011655807,
0.07636253535747528,
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0.11870371550321579,
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0.07180462777614594,
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0.0708870068192482,
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0.0705045759677887,
0.03871509060263634,
0.07755763083696365,
-0.004780877847224474,
0.057188... | li | Articles | [
"articles",
"articles/filtrable-hnsw"
] | 7,012 |
[
"Filtrable HNSW - Qdrant"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(1) > li:nth-of-type(2) | [
-0.1359460949897766,
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0.01159171387553215,
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0.02118542790412903,
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0.02877473458647728,
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0.028667796403169632,
-0.06563723087310791,
0.017261670902371407,
0.044... | li | / | [
"articles",
"articles/filtrable-hnsw"
] | 7,013 |
[
"Filtrable HNSW - Qdrant"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(1) > li:nth-of-type(5) | [
-0.07745631039142609,
0.006582682486623526,
-0.0028027277439832687,
0.04357463866472244,
0.01641889102756977,
0.047957643866539,
0.07221507281064987,
0.0343962125480175,
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0.008216324262320995,
-0.050789669156074524,
-0.05436446890234947,
0.0738753... | li | Filtrable HNSW | [
"articles",
"articles/filtrable-hnsw"
] | 7,014 |
[
"Filtrable HNSW - Qdrant"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h1 | [
-0.07745631039142609,
0.006582682486623526,
-0.0028027277439832687,
0.04357463866472244,
0.01641889102756977,
0.047957643866539,
0.07221507281064987,
0.0343962125480175,
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-0.08718494325876236,
0.008216324262320995,
-0.050789669156074524,
-0.05436446890234947,
0.0738753... | h1 | Filtrable HNSW | [
"articles",
"articles/filtrable-hnsw"
] | 7,015 |
[
"Filtrable HNSW - Qdrant",
"Filtrable HNSW"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1) | [
-0.12239910662174225,
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0.04147666320204735,
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0.0293487049639225,
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0.01123040821403265,
-0.06526719033718109,
-0.03302404284477234,
-0.013492883183062077,
-0.0015734749613329768,
0.0... | p | If you need to find some similar objects in vector space, provided e.g. by embeddings or matching NN, you can choose among a variety of libraries: Annoy, FAISS or NMSLib. | [
"articles",
"articles/filtrable-hnsw"
] | 7,016 |
[
"Filtrable HNSW - Qdrant",
"Filtrable HNSW"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1) | [
0.02924797683954239,
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0.07312949001789093,
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0.031062351539731026,
0.08522619307041168,
0.0043744589202106,
0.014687493443489075,
-0.00057... | p | All of them will give you a fast approximate neighbors search within almost any space. | [
"articles",
"articles/filtrable-hnsw"
] | 7,017 |
[
"Filtrable HNSW - Qdrant",
"Filtrable HNSW"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2) | [
-0.016665145754814148,
0.02192862145602703,
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0.0033371003810316324,
0.024550115689635277,
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-0.02572600543498993,
0.011295231059193611,
-0.027754278853535652,
0.01955348253250122,
0.0... | p | But what if you need to introduce some constraints in your search? | [
"articles",
"articles/filtrable-hnsw"
] | 7,018 |
[
"Filtrable HNSW - Qdrant",
"Filtrable HNSW"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2) | [
0.0026111272163689137,
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0.04782692342996597,
0.03183384984731674,
0.027293171733617783,
0.023508915677666664,
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-0.1168031245470047,
0.05689743533730507,
-0.004613224416971207,
0.0239365566521883,
0.000... | p | For example, you want search only for products in some category or select the most similar customer of a particular brand. | [
"articles",
"articles/filtrable-hnsw"
] | 7,019 |
[
"Filtrable HNSW - Qdrant",
"Filtrable HNSW"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2) | [
-0.03195617347955704,
0.042831409722566605,
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0.026215633377432823,
0.005030016414821148,
0.00018501032900530845,
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-0.012207954190671444,
0.0660615786910057,
-0.052612531930208206,
-0.0029552329797297716,... | p | I did not find any simple solutions for this. | [
"articles",
"articles/filtrable-hnsw"
] | 7,020 |
[
"Filtrable HNSW - Qdrant",
"Filtrable HNSW"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2) | [
-0.046246711164712906,
0.04988311976194382,
0.018693117424845695,
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0.07539281249046326,
0.05151832476258278,
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0.03994698449969292,
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-0.03625159338116646,
-0.01704748533666134,
0.010026239790022373,
0.07622344046831131,
-0.0240... | p | There are several discussions like this, but they only suggest to iterate over top search results and apply conditions consequently after the search. | [
"articles",
"articles/filtrable-hnsw"
] | 7,021 |
[
"Filtrable HNSW - Qdrant",
"Filtrable HNSW"
] | /articles/filtrable-hnsw/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3) | [
-0.08632374554872513,
0.05279216542840004,
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0.03984856233000755,
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-0.06112644076347351,
0.039846453815698624,
-0.007451614364981651,
0.... | p | Let’s see if we could somehow modify any of ANN algorithms to be able to apply constrains during the search itself. | [
"articles",
"articles/filtrable-hnsw"
] | 7,022 |
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This is a dataset created using vector-io
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