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[
"Discovery needs context - Qdrant",
"Context search"
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[
"Discovery needs context - Qdrant",
"Context search"
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[
"Discovery needs context - Qdrant",
"Context search"
] | /articles/discovery-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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[
"Discovery needs context - Qdrant",
"Context search"
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"articles",
"articles/discovery-search"
] | 403 |
[
"Discovery needs context - Qdrant",
"Context search"
] | /articles/discovery-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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[
"Discovery needs context - Qdrant",
"Context search"
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[
"Discovery needs context - Qdrant",
"Context search"
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[
"Discovery needs context - Qdrant",
"Context search"
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[
"Discovery needs context - Qdrant",
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] | p | This way, you should be able to constrain the space enough so you select points from a per-search “category” created just from the context in the input. | [
"articles",
"articles/discovery-search"
] | 408 |
[
"Discovery needs context - Qdrant",
"Context search"
] | /articles/discovery-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(31) | [
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[
"Discovery needs context - Qdrant",
"Wrapping up"
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[
"Discovery needs context - Qdrant",
"Wrapping up"
] | /articles/discovery-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(32) | [
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"articles",
"articles/discovery-search"
] | 413 |
[
"Discovery needs context - Qdrant",
"Wrapping up"
] | /articles/discovery-search/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(32) | [
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[
"What are Vector Embeddings? - Qdrant"
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[
"What are Vector Embeddings? - Qdrant"
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"What are Vector Embeddings? - Qdrant",
"What are Vector Embeddings?"
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"articles",
"articles/what-are-embeddings"
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[
"What are Vector Embeddings? - Qdrant",
"What are Vector Embeddings?"
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"articles/what-are-embeddings"
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] | p | The same embeddings can be repurposed for search, ads, and other features, creating a highly personalized user experience. | [
"articles",
"articles/what-are-embeddings"
] | 432 |
[
"What are Vector Embeddings? - Qdrant",
"What are Vector Embeddings?"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7) | [
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[
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"Why Use Vector Embeddings?"
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[
"What are Vector Embeddings? - Qdrant",
"Why Use Vector Embeddings?"
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"What are Vector Embeddings? - Qdrant",
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] | h2 | How do embeddings work? | [
"articles",
"articles/what-are-embeddings"
] | 445 |
[
"What are Vector Embeddings? - Qdrant",
"How do embeddings work?"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14) | [
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"articles",
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] | 446 |
[
"What are Vector Embeddings? - Qdrant",
"How do embeddings work?"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14) | [
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[
"What are Vector Embeddings? - Qdrant",
"How do embeddings work?"
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] | p | They can then project these vectors into a proper high-dimensional space, specifically, a Vector Database. | [
"articles",
"articles/what-are-embeddings"
] | 448 |
[
"What are Vector Embeddings? - Qdrant",
"How do embeddings work?"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16) | [
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[
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"How do embeddings work?"
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[
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"How do embeddings work?"
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[
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"How do embeddings work?"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > blockquote:nth-of-type(2) > p | [
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] | p | The embedding model that works best for you depends on your use case. | [
"articles",
"articles/what-are-embeddings"
] | 453 |
[
"What are Vector Embeddings? - Qdrant"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(1) | [
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[
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"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19) | [
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"articles",
"articles/what-are-embeddings"
] | 457 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | 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 | So when my computer sees 1.3, it sees the word ‘right’. | [
"articles",
"articles/what-are-embeddings"
] | 458 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20) | [
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] | p | Now I want to make my computer understand the context of the word ‘right’. | [
"articles",
"articles/what-are-embeddings"
] | 459 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20) | [
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] | p | I can use a two-dimensional vector, such as [1.3, 0.8], to represent ‘right’. | [
"articles",
"articles/what-are-embeddings"
] | 460 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20) | [
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[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21) | [
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[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21) | [
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"articles",
"articles/what-are-embeddings"
] | 463 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22) | [
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] | p | The evolution of this concept led to the development of embedding models like Word2Vec and GloVe. | [
"articles",
"articles/what-are-embeddings"
] | 464 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22) | [
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[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
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"Creating Vector Embeddings"
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[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24) | [
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"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
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[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25) | [
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"articles",
"articles/what-are-embeddings"
] | 470 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25) | [
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] | p | In other words, how do we differentiate between: | [
"articles",
"articles/what-are-embeddings"
] | 471 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1) | [
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"articles",
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[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(2) | [
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[
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"Creating Vector Embeddings"
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"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
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] | p | These models pay attention to the entire context. | [
"articles",
"articles/what-are-embeddings"
] | 477 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27) | [
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"articles",
"articles/what-are-embeddings"
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[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29) | [
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"articles",
"articles/what-are-embeddings"
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[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29) | [
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] | p | Think of the term: “biophilic design”, for example. | [
"articles",
"articles/what-are-embeddings"
] | 480 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29) | [
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] | p | To generate its embedding, the transformer architecture can use the following contexts: | [
"articles",
"articles/what-are-embeddings"
] | 481 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(3) > li:nth-of-type(1) | [
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"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
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"articles",
"articles/what-are-embeddings"
] | 483 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(3) > li:nth-of-type(3) | [
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"articles",
"articles/what-are-embeddings"
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[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
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] | p | And then it compares contexts to known architectural and design principles: | [
"articles",
"articles/what-are-embeddings"
] | 485 |
[
"What are Vector Embeddings? - Qdrant",
"Creating Vector Embeddings"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(4) > li:nth-of-type(1) | [
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] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(4) > li:nth-of-type(2) | [
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[
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"Integration with Embedding APIs"
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[
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"Integration with Embedding APIs"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(33) | [
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"articles",
"articles/what-are-embeddings"
] | 494 |
[
"What are Vector Embeddings? - Qdrant",
"Integration with Embedding APIs"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(34) | [
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] | p | Fastembed, which we’ll use on the example below, is designed for efficiency and speed, great for applications needing low-latency responses, such as autocomplete and instant content recommendations. | [
"articles",
"articles/what-are-embeddings"
] | 495 |
[
"What are Vector Embeddings? - Qdrant",
"Integration with Embedding APIs"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(35) | [
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"articles/what-are-embeddings"
] | 496 |
[
"What are Vector Embeddings? - Qdrant"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(3) | [
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] | p | Now that you’re familiar with the core concepts around vector embeddings, how about start building your own Neural Search Service? | [
"articles",
"articles/what-are-embeddings"
] | 498 |
[
"What are Vector Embeddings? - Qdrant",
"Create a Neural Search Service with Fastembed"
] | /articles/what-are-embeddings/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(37) | [
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] | p | Tutorial guides you through a practical application of how to use Qdrant for document management based on descriptions of companies from startups-list.com. | [
"articles",
"articles/what-are-embeddings"
] | 499 |
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