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Merge pull request #2010 from qdrant/remove-init_from
Remove init_from from docs
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@@ -59,21 +59,6 @@ will enable the use of
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[memmaps](/documentation/concepts/storage/#configuring-memmap-storage),
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[memmaps](/documentation/concepts/storage/#configuring-memmap-storage),
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which is suitable for ingesting a large amount of data.
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which is suitable for ingesting a large amount of data.
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### Create collection from another collection
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*Available as of v1.0.0*
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It is possible to initialize a collection from another existing collection.
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This might be useful for experimenting quickly with different configurations for the same data set.
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<aside role="alert"> Usage of the <code>init_from</code> can create unpredictable load on the qdrant cluster. It is not recommended to use <code>init_from</code> in performance-sensitive environments.</aside>
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Make sure the vectors have the same `size` and `distance` function when setting up the vectors configuration in the new collection. If you used the previous sample
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code, `"size": 300` and `"distance": "Cosine"`.
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{{< code-snippet path="/documentation/headless/snippets/create-collection/init-from/" >}}
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### Collection with multiple vectors
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### Collection with multiple vectors
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@@ -143,17 +128,33 @@ The distance function for sparse vectors is always `Dot` and does not need to be
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However, there are optional parameters to tune the underlying [sparse vector index](/documentation/concepts/indexing/#sparse-vector-index).
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However, there are optional parameters to tune the underlying [sparse vector index](/documentation/concepts/indexing/#sparse-vector-index).
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### Check collection existence
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### Create collection from another collection
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To create a collection from another collection, use the [Migration Tool](https://github.com/qdrant/migration/). You can use it to either copy a collection within the same Qdrant instance or to copy a collection to another instance.
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For example, to copy a collection from a local instance to a Qdrant Cloud instance, run the following command:
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```bash
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docker run --net=host --rm -it registry.cloud.qdrant.io/library/qdrant-migration qdrant \
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--source.url 'http://localhost:6334' \
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--source.collection 'source-collection' \
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--target.url 'https://example.cloud-region.cloud-provider.cloud.qdrant.io:6334' \
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--target.api-key 'qdrant-key' \
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--target.collection 'target-collection' \
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--migration.batch-size 64
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```
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## Check collection existence
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*Available as of v1.8.0*
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*Available as of v1.8.0*
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{{< code-snippet path="/documentation/headless/snippets/check-collection-exists/simple/" >}}
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{{< code-snippet path="/documentation/headless/snippets/check-collection-exists/simple/" >}}
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### Delete collection
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## Delete collection
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{{< code-snippet path="/documentation/headless/snippets/delete-collection/simple/" >}}
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{{< code-snippet path="/documentation/headless/snippets/delete-collection/simple/" >}}
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### Update collection parameters
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## Update collection parameters
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Dynamic parameter updates may be helpful, for example, for more efficient initial loading of vectors.
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Dynamic parameter updates may be helpful, for example, for more efficient initial loading of vectors.
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For example, you can disable indexing during the upload process, and enable it immediately after the upload is finished.
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For example, you can disable indexing during the upload process, and enable it immediately after the upload is finished.
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-1
@@ -1 +0,0 @@
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Description: This code snippet demonstrates creating a new collection by initializing it from an existing collection. The new collection will inherit the vector settings, such as size and distance function, from the source collection specified in the `init_from` field. It is a convenient feature for testing different configurations quickly. However, caution is advised as using `init_from` may lead to unpredictable load on the cluster, especially in performance-sensitive environments. Make sure that the vectors in both collections have matching settings for size and distance function.
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-13
@@ -1,13 +0,0 @@
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```bash
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curl -X PUT http://localhost:6333/collections/{collection_name} \
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-H 'Content-Type: application/json' \
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--data-raw '{
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"vectors": {
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"size": 300,
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"distance": "Cosine"
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},
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"init_from": {
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"collection": {from_collection_name}
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}
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}'
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```
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-12
@@ -1,12 +0,0 @@
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```csharp
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using Qdrant.Client;
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using Qdrant.Client.Grpc;
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var client = new QdrantClient("localhost", 6334);
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await client.CreateCollectionAsync(
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collectionName: "{collection_name}",
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vectorsConfig: new VectorParams { Size = 100, Distance = Distance.Cosine },
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initFromCollection: "{from_collection_name}"
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);
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```
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-21
@@ -1,21 +0,0 @@
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```go
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import (
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"context"
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"github.com/qdrant/go-client/qdrant"
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)
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client, err := qdrant.NewClient(&qdrant.Config{
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Host: "localhost",
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Port: 6334,
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})
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client.CreateCollection(context.Background(), &qdrant.CreateCollection{
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CollectionName: "{collection_name}",
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VectorsConfig: qdrant.NewVectorsConfig(&qdrant.VectorParams{
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Size: 100,
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Distance: qdrant.Distance_Cosine,
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}),
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InitFromCollection: qdrant.PtrOf("{from_collection_name}"),
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})
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```
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-12
@@ -1,12 +0,0 @@
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```http
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PUT /collections/{collection_name}
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{
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"vectors": {
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"size": 100,
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"distance": "Cosine"
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},
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"init_from": {
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"collection": "{from_collection_name}"
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}
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}
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```
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-26
@@ -1,26 +0,0 @@
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```java
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import io.qdrant.client.QdrantClient;
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import io.qdrant.client.QdrantGrpcClient;
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import io.qdrant.client.grpc.Collections.CreateCollection;
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import io.qdrant.client.grpc.Collections.Distance;
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import io.qdrant.client.grpc.Collections.VectorParams;
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import io.qdrant.client.grpc.Collections.VectorsConfig;
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QdrantClient client =
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new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build());
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client
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.createCollectionAsync(
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CreateCollection.newBuilder()
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.setCollectionName("{collection_name}")
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.setVectorsConfig(
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VectorsConfig.newBuilder()
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.setParams(
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VectorParams.newBuilder()
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.setSize(100)
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.setDistance(Distance.Cosine)
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.build()))
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.setInitFromCollection("{from_collection_name}")
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.build())
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.get();
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```
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-11
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```python
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from qdrant_client import QdrantClient, models
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client = QdrantClient(url="http://localhost:6333")
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client.create_collection(
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collection_name="{collection_name}",
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vectors_config=models.VectorParams(size=100, distance=models.Distance.COSINE),
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init_from=models.InitFrom(collection="{from_collection_name}"),
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)
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```
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-14
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```rust
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use qdrant_client::Qdrant;
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use qdrant_client::qdrant::{CreateCollectionBuilder, Distance, VectorParamsBuilder};
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let client = Qdrant::from_url("http://localhost:6334").build()?;
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client
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.create_collection(
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CreateCollectionBuilder::new("{collection_name}")
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.vectors_config(VectorParamsBuilder::new(100, Distance::Cosine))
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.init_from_collection("{from_collection_name}"),
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)
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.await?;
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```
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-10
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```typescript
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import { QdrantClient } from "@qdrant/js-client-rest";
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const client = new QdrantClient({ host: "localhost", port: 6333 });
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client.createCollection("{collection_name}", {
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vectors: { size: 100, distance: "Cosine" },
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init_from: { collection: "{from_collection_name}" },
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});
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```
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