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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] | 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) | [
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] | 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) | [
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] | 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) | [
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] | 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) | [
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] | 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) | [
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] | 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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