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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Computing the Sparse Vector"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21) | [
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] | p | In SPLADE, we assign weights to all the tokens in the vocabulary using this vector using our learned model. | [
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"articles/sparse-vectors"
] | 700 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h1:nth-of-type(5) | [
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"Term Expansion and Weights"
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"Term Expansion and Weights"
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"articles",
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] | p | Here are the top few sorted_tokens with a weight of more than 1: | [
"articles",
"articles/sparse-vectors"
] | 706 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Term Expansion and Weights"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24) | [
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] | p | If you’re interested in using the higher-performance approach, check out the following models: | [
"articles",
"articles/sparse-vectors"
] | 707 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Term Expansion and Weights"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(1) | [
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Term Expansion and Weights"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(2) | [
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"Why SPLADE works? Term Expansion"
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] | p | This is a crucial advantage over other sparse methods which include the exact word, but completely miss the contextually relevant ones. | [
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Why SPLADE works? Term Expansion"
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[
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"Why SPLADE works? Term Expansion"
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"articles",
"articles/sparse-vectors"
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[
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"Why SPLADE works? Term Expansion"
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] | p | If we use more tokens, we can represent more terms, but the vectors become denser. | [
"articles",
"articles/sparse-vectors"
] | 719 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Why SPLADE works? Term Expansion"
] | /articles/sparse-vectors/ | 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 | For example, assume a 1M document corpus. | [
"articles",
"articles/sparse-vectors"
] | 722 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Why SPLADE works? Term Expansion"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28) | [
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] | p | Say, we use 100 sparse token ids + weights per document. | [
"articles",
"articles/sparse-vectors"
] | 723 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Why SPLADE works? Term Expansion"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(28) | [
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Why SPLADE works? Term Expansion"
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"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant"
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[
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"How SPLADE works? Leveraging BERT"
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[
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"How SPLADE works? Leveraging BERT"
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] | p | Rather than producing dense probability distributions, SPLADE utilizes these logits to construct sparse vectors—think of them as a distilled essence of tokens, where each dimension corresponds to a term from the vocabulary and its associated weight in the context of the given document or query. | [
"articles",
"articles/sparse-vectors"
] | 732 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"How SPLADE works? Leveraging BERT"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(31) | [
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"How SPLADE works? Leveraging BERT"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(32) | [
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[
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"How SPLADE works? Leveraging BERT"
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"articles",
"articles/sparse-vectors"
] | 740 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Interpreting SPLADE"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(35) | [
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"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant"
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Document and Query Encoder"
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] | p | The SPLADE model variant that uses a document encoder with max pooling but no query encoder reaches the same performance level as the prior SPLADE model. | [
"articles",
"articles/sparse-vectors"
] | 749 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Document and Query Encoder"
] | /articles/sparse-vectors/ | 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 | This suggests a limitation in the necessity of a query encoder, potentially affecting the efficiency of the model. | [
"articles",
"articles/sparse-vectors"
] | 750 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(8) | [
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[
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"articles",
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] | 754 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Other Sparse Vector Methods"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(40) | [
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"articles/sparse-vectors"
] | 755 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Other Sparse Vector Methods"
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] | p | This method preserves the ability to query exact words and phrases but avoids the computational overhead of query expansion used in SPLADE. | [
"articles",
"articles/sparse-vectors"
] | 756 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Other Sparse Vector Methods"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(41) | [
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] | 757 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h1:nth-of-type(6) | [
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Leveraging Sparse Vectors in Qdrant for Hybrid Search"
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[
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"articles",
"articles/sparse-vectors"
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(44) | [
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"articles",
"articles/sparse-vectors"
] | 765 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(1) | [
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
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"articles",
"articles/sparse-vectors"
] | 767 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(1) > p | [
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
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[
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"Practical Implementation in Python"
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] | li | This is done using the recreate_collection method where we define the parameters for sparse vectors, such as setting the index configuration. | [
"articles",
"articles/sparse-vectors"
] | 772 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(2) > p | [
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] | p | This is done using the recreate_collection method where we define the parameters for sparse vectors, such as setting the index configuration. | [
"articles",
"articles/sparse-vectors"
] | 775 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(3) | [
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[
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"Practical Implementation in Python"
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
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"articles",
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(3) > p | [
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] | p | This involves defining the sparse vector with its indices and values, and then upserting this point into the collection. | [
"articles",
"articles/sparse-vectors"
] | 779 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(4) | [
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(4) | [
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"articles",
"articles/sparse-vectors"
] | 781 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(4) | [
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] | li | We then use these details to construct a query against our collection. | [
"articles",
"articles/sparse-vectors"
] | 782 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(4) > p | [
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(4) > p | [
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"articles",
"articles/sparse-vectors"
] | 784 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(4) > p | [
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] | p | We then use these details to construct a query against our collection. | [
"articles",
"articles/sparse-vectors"
] | 785 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(5) | [
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"articles",
"articles/sparse-vectors"
] | 786 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(5) | [
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"articles",
"articles/sparse-vectors"
] | 787 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(5) > p | [
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] | p | Retrieving and Interpreting Results: The search operation returns results that include the id of the matching document, its score, and other relevant details. | [
"articles",
"articles/sparse-vectors"
] | 788 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"Practical Implementation in Python"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(3) > li:nth-of-type(5) > p | [
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant"
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] | p | Here, we see the process of inserting a sparse vector into the Qdrant collection. | [
"articles",
"articles/sparse-vectors"
] | 796 |
[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"3. Inserting Sparse Vectors"
] | /articles/sparse-vectors/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(45) | [
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"3. Inserting Sparse Vectors"
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[
"Sparse Vectors in Qdrant: Pure Vector-based Hybrid Search - Qdrant",
"3. Inserting Sparse Vectors"
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] | p | By upserting points with sparse vectors, we prepare our dataset for rapid first-stage retrieval, laying the groundwork for subsequent detailed analysis using dense vectors. | [
"articles",
"articles/sparse-vectors"
] | 799 |
Subsets and Splits