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[
"Explore - Qdrant",
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"documentation/concepts/explore"
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[
"Explore - Qdrant",
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"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 6,981 |
[
"Explore - Qdrant",
"Discovery search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(35) | [
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] | p | The discovery score is then computed as: | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 6,982 |
[
"Explore - Qdrant",
"Discovery search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(35) | [
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[
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[
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] | p | In other words, the sum of individual ranks determines how many positive zones a point is in, while the closeness hierarchy comes second. | [
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"documentation/concepts/explore"
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[
"Explore - Qdrant",
"Discovery 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) | [
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] | li | Score is always in descending order (larger is better), regardless of the metric used. | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 6,991 |
[
"Explore - Qdrant",
"Discovery search"
] | /documentation/concepts/explore/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > aside:nth-of-type(4) > ul > li:nth-of-type(3) | [
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[
"Explore - Qdrant",
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"documentation/concepts",
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[
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[
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"documentation",
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] | 6,997 |
[
"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(39) | [
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] | p | We can directly associate the score function to a loss function, where 0.0 is the maximum score a point can have, which means it is only in positive areas. | [
"documentation",
"documentation/concepts",
"documentation/concepts/explore"
] | 6,998 |
[
"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(39) | [
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] | p | As soon as a point exists closer to a negative example, its loss will simply be the difference of the positive and negative similarities. | [
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[
"On Hybrid Search - Qdrant"
] | /articles/hybrid-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h4:nth-of-type(1) | [
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] | p | inventory and search queries from Home Depot’s website with a relevancy score from 1 (not relevant) to 3 (highly relevant). | [
"articles",
"articles/hybrid-search"
] | 4,002 |
[
"On Hybrid Search - Qdrant",
"Home Depot"
] | /articles/hybrid-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27) | [
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] | p | There are over 124k products with textual descriptions in the dataset and around 74k search queries with the relevancy | [
"articles",
"articles/hybrid-search"
] | 4,003 |
[
"On Hybrid Search - Qdrant",
"Home Depot"
] | /articles/hybrid-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27) | [
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] | p | I also selected a relatively new search relevance dataset. | [
"articles",
"articles/hybrid-search"
] | 4,007 |
[
"On Hybrid Search - Qdrant",
"WANDS"
] | /articles/hybrid-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28) | [
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] | 4,013 |
[
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"WANDS"
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"articles/hybrid-search"
] | 4,014 |
[
"On Hybrid Search - Qdrant",
"WANDS"
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] | p | Both datasets have been evaluated with the same experiments. | [
"articles",
"articles/hybrid-search"
] | 4,018 |
[
"On Hybrid Search - Qdrant",
"The results"
] | /articles/hybrid-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(30) | [
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] | p | The achieved performance is shown in the tables. | [
"articles",
"articles/hybrid-search"
] | 4,019 |
[
"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(3) | [
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] | p | With the cross-encoder approach, Qdrant retrieved about 56.05% of the relevant items on average, while BM25 | [
"articles",
"articles/hybrid-search"
] | 4,025 |
[
"On Hybrid Search - Qdrant",
"Home Depot"
] | /articles/hybrid-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(33) | [
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] | p | The dataset seems to be more suited for semantic search, but the results might be also improved if we decide to use | [
"articles",
"articles/hybrid-search"
] | 4,029 |
[
"On Hybrid Search - Qdrant",
"WANDS"
] | /articles/hybrid-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(35) | [
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] | p | a hybrid search approach with cross encoder model as a final step. | [
"articles",
"articles/hybrid-search"
] | 4,030 |
[
"On Hybrid Search - Qdrant",
"WANDS"
] | /articles/hybrid-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(36) | [
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] | p | Again, it’s worth mentioning that with the 3rd experiment, with cross-encoder reranking, Qdrant returned more than 48.12% of | [
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] | p | None of the algorithms works better in all the cases. | [
"articles",
"articles/hybrid-search"
] | 4,036 |
[
"On Hybrid Search - Qdrant",
"Some anecdotal observations"
] | /articles/hybrid-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(38) | [
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] | p | There might be some specific queries in which keyword-based search | [
"articles",
"articles/hybrid-search"
] | 4,037 |
[
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] | p | The table shows some interesting examples we could find in WANDS dataset | [
"articles",
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[
"On Hybrid Search - Qdrant",
"Some anecdotal observations"
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[
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"articles",
"articles/hybrid-search"
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[
"On Hybrid Search - Qdrant",
"A wrap up"
] | /articles/hybrid-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(40) | [
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[
"On Hybrid Search - Qdrant",
"A wrap up"
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"On Hybrid Search - Qdrant",
"A wrap up"
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"Finding errors in datasets with Similarity Search - Qdrant"
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"Finding errors in datasets with Similarity Search"
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"articles",
"articles/dataset-quality"
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[
"Finding errors in datasets with Similarity Search - Qdrant",
"Furniture web-marketplace"
] | /articles/dataset-quality/ | 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 say you work on an online furniture marketplace. | [
"articles",
"articles/dataset-quality"
] | 4,071 |
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