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new: add upload_collection and upload_points (#454)
* new: add draft for upload collections and upload records * Fix formatting of literals in alert banners * Minor text improvement * new: replace upload_records with upload_points * new: reduce amount of points in the examples to save some space * Update qdrant-landing/content/documentation/concepts/points.md Co-authored-by: Mike Jang <michael@linuxexam.com> --------- Co-authored-by: timvisee <tim@visee.me> Co-authored-by: Mike Jang <michael@linuxexam.com>
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co-authored by
Mike Jang
timvisee
parent
52157881a9
commit
0ea09e87e8
@@ -448,15 +448,69 @@ client
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.await?;
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```
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<!--
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The Python client has additional features for loading points.
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These include parallel loading, and also loading directly from a numpy file.
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The Python client has additional features for loading points, which include:
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- Parallelization
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- A retry mechanism
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- Lazy batching support
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For example, you can read your data directly from hard drives, to avoid storing all data in RAM. You can use these
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features with the `upload_collection` and `upload_points` methods.
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Similar to the basic upsert API, these methods support both record-oriented and column-oriented formats.
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<aside role="status">
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<code>upload_points</code> is available as of v1.7.1. It has replaced <code>upload_records</code> which is now deprecated.
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</aside>
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Column-oriented format:
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```python
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client.upload_collection(
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collection_name="{collection_name}",
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ids=[1, 2],
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payloads=[
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{"color": "red"},
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{"color": "green"},
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],
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vectors=[
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[0.9, 0.1, 0.1],
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[0.1, 0.9, 0.1],
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],
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parallel=4,
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max_retries=3,
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)
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```
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-->
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<aside role="status">
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If <code>ids</code> are not provided, they will be generated automatically as UUIDs.
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</aside>
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Record-oriented format:
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```python
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client.upload_points(
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collection_name="{collection_name}",
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points=[
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models.PointStruct(
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id=1,
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payload={
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"color": "red",
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},
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vector=[0.9, 0.1, 0.1],
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),
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models.PointStruct(
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id=2,
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payload={
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"color": "green",
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},
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vector=[0.1, 0.9, 0.1],
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),
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],
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parallel=4,
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max_retries=3,
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)
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```
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All APIs in Qdrant, including point loading, are idempotent.
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It means that executing the same method several times in a row is equivalent to a single execution.
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