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fix: revised collections defination with respect to addition of named vectors in the API

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Mihir Bagchi
2023-07-24 14:28:08 +05:30
parent 57d8e14729
commit b02c5ae23f
2 changed files with 2 additions and 5 deletions
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qdrant-landing/content/documentation/concepts/collections.md
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@@ -7,8 +7,7 @@ aliases:
# Collections
A collection is a named set of points (vectors with a payload) among which you can search.
Vectors within the same collection must have the same dimensionality and be compared by a single metric.
A collection is a named set of points (vectors with a payload) among which you can search. The vector of each point within the same collection must have the same dimensionality and be compared by a single metric. [Named vectors](https://qdrant.tech/documentation/concepts/collections/#collection-with-multiple-vectors) can be used to have multiple vectors in a single point, each of which can have their own dimensionality and metric requirements.
Distance metrics are used to measure similarities among vectors.
The choice of metric depends on the way vectors obtaining and, in particular, on the method of neural network encoder training.
qdrant-landing/content/documentation/overview/_index.md
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@@ -112,9 +112,7 @@ Let's now evaluate, at a high-level, the way Qdrant is architected.
The diagram above represents a high-level overview of some of the main components of Qdrant. Here
are the terminologies you should get familiar with.
- [Collections](../concepts/collections/): A collection is a named set of
points (vectors with a payload) among which you can search. Vectors within the same collection
must have the same dimensionalities and be compared by a single metric.
- [Collections](../concepts/collections/): A collection is a named set of points (vectors with a payload) among which you can search. The vector of each point within the same collection must have the same dimensionality and be compared by a single metric. [Named vectors](https://qdrant.tech/documentation/concepts/collections/#collection-with-multiple-vectors) can be used to have multiple vectors in a single point, each of which can have their own dimensionality and metric requirements.
- [Distance Metrics](https://en.wikipedia.org/wiki/Metric_space): These are used to measure
similarities among vectors and they must be selected at the same time you are creating a
collection. The choice of metric depends on the way the vectors were obtained and, in particular,