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generall
2023-12-09 20:04:12 +01:00
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commit 9f55cac0f8
2 changed files with 8 additions and 4 deletions
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qdrant-landing/content/documentation/concepts/collections.md
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@@ -14,9 +14,10 @@ The choice of metric depends on the way vectors obtaining and, in particular, on
Qdrant supports these most popular types of metrics:
* Dot product: `Dot` - https://en.wikipedia.org/wiki/Dot_product
* Cosine similarity: `Cosine` - https://en.wikipedia.org/wiki/Cosine_similarity
* Euclidean distance: `Euclid` - https://en.wikipedia.org/wiki/Euclidean_distance
* Dot product: `Dot` - [[wiki]](https://en.wikipedia.org/wiki/Dot_product)
* Cosine similarity: `Cosine` - [[wiki]](https://en.wikipedia.org/wiki/Cosine_similarity)
* Euclidean distance: `Euclid` - [[wiki]](https://en.wikipedia.org/wiki/Euclidean_distance)
* Manhattan distance: `Manhattan` - [[wiki]](https://en.wikipedia.org/wiki/Taxicab_geometry)
<aside role="status">For search efficiency, Cosine similarity is implemented as dot-product over normalized vectors. Vectors are automatically normalized during upload</aside>
qdrant-landing/content/documentation/quick-start.md
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@@ -392,7 +392,10 @@ SearchResponse {
time: 0.004001083,
}
```
You have just conducted vector search. You loaded vectors into a database and queried the database with a vector of your own. Qdrant found the closest results and presented you with a similarity score.
<aside role="status">To make filtered search fast on real datasets, we highly recommend to create <a href="../concepts/indexing/#payload-index">payload indexes</a>!</aside>
You have just conducted vector search. You loaded vectors into a database and queried the database with a vector of your own. Qdrant found the closest results and presented you with a similarity score.
## Next steps