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https://github.com/qdrant/landing_page.git
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Model migration tutorial: use insert-only update mode and Cloud Inference (#2168)
* Switch to insert-only mode instead of conditional upserts * Make code snippets testable; use Cloud Inference * Use regular upserts instead of batch_update_points * Add snippets for TS, Rust, Java, C#, and Go
This commit is contained in:
Abdon Pijpelink
+6
@@ -0,0 +1,6 @@
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```csharp
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await client.CreateCollectionAsync(
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collectionName: NEW_COLLECTION,
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vectorsConfig: new VectorParams { Size = 512, Distance = Distance.Cosine }
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);
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```
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+9
@@ -0,0 +1,9 @@
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```go
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client.CreateCollection(context.Background(), &qdrant.CreateCollection{
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CollectionName: NEW_COLLECTION,
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VectorsConfig: qdrant.NewVectorsConfig(&qdrant.VectorParams{
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Size: 512, // Size of the new embedding vectors
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Distance: qdrant.Distance_Cosine,
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}),
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})
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```
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+7
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```java
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client.createCollectionAsync(NEW_COLLECTION,
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VectorParams.newBuilder()
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.setSize(512) // Size of the new embedding vectors
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.setDistance(Distance.Cosine) // Similarity function for the new model
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.build()).get();
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```
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+11
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```python
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client.create_collection(
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collection_name=NEW_COLLECTION,
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vectors_config=(
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models.VectorParams(
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size=512, # Size of the new embedding vectors
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distance=models.Distance.COSINE # Similarity function for the new model
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)
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)
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)
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```
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+8
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```rust
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client
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.create_collection(
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CreateCollectionBuilder::new(new_collection)
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.vectors_config(VectorParamsBuilder::new(512, Distance::Cosine)), // Size of the new embedding vectors
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)
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.await?;
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```
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+8
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```typescript
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await client.createCollection(NEW_COLLECTION, {
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vectors: {
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size: 512, // Size of the new embedding vectors
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distance: "Cosine", // Similarity function for the new model
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},
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});
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```
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+123
@@ -0,0 +1,123 @@
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```csharp
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using Qdrant.Client;
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using Qdrant.Client.Grpc;
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await client.CreateCollectionAsync(
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collectionName: NEW_COLLECTION,
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vectorsConfig: new VectorParams { Size = 512, Distance = Distance.Cosine }
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);
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await client.UpsertAsync(
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collectionName: OLD_COLLECTION,
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points: new List<PointStruct>
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{
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new()
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{
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Id = 1,
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Vectors = new Document
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{
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Text = "Example document",
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Model = OLD_MODEL
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},
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Payload = { ["text"] = "Example document" }
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}
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}
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);
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await client.UpsertAsync(
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collectionName: NEW_COLLECTION,
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points: new List<PointStruct>
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{
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new()
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{
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Id = 1,
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// Use the new embedding model to encode the document
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Vectors = new Document
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{
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Text = "Example document",
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Model = NEW_MODEL
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},
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Payload = { ["text"] = "Example document" }
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}
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}
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);
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PointId? lastOffset = null;
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uint limit = 100; // Number of points to read in each batch
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bool reachedEnd = false;
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while (!reachedEnd)
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{
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// Get the next batch of points from the old collection
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var scrollResult = await client.ScrollAsync(
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collectionName: OLD_COLLECTION,
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limit: limit,
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offset: lastOffset,
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// Include payloads in the response, as we need them to re-embed the vectors
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payloadSelector: true,
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// We don't need the old vectors, so let's save on the bandwidth
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vectorsSelector: false
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);
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var records = scrollResult.Result;
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lastOffset = scrollResult.NextPageOffset;
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// Re-embed the points using the new model
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var points = new List<PointStruct>();
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foreach (var record in records)
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{
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var text = record.Payload.ContainsKey("text")
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? record.Payload["text"].StringValue
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: "";
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points.Add(new PointStruct
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{
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// Keep the original ID to ensure consistency
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Id = record.Id,
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// Use the new embedding model to encode the text from the payload,
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// assuming that was the original source of the embedding
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Vectors = new Document
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{
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Text = text,
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Model = NEW_MODEL
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},
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// Keep the original payload
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Payload = { record.Payload }
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});
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}
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// Upsert the re-embedded points into the new collection
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await client.UpsertAsync(
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new()
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{
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CollectionName = NEW_COLLECTION,
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Points = { points },
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// Only insert the point if a point with this ID does not already exist.
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UpdateMode = UpdateMode.InsertOnly
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}
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);
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// Check if we reached the end of the collection
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reachedEnd = (lastOffset == null);
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}
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var results = await client.QueryAsync(
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collectionName: OLD_COLLECTION,
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query: new Document
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{
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Text = "my query",
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Model = OLD_MODEL
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},
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limit: 10
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);
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results = await client.QueryAsync(
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collectionName: NEW_COLLECTION,
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query: new Document
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{
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Text = "my query",
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Model = NEW_MODEL
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},
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limit: 10
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);
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```
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+114
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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.CreateCollection(context.Background(), &qdrant.CreateCollection{
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CollectionName: NEW_COLLECTION,
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VectorsConfig: qdrant.NewVectorsConfig(&qdrant.VectorParams{
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Size: 512, // Size of the new embedding vectors
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Distance: qdrant.Distance_Cosine,
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}),
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})
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client.Upsert(context.Background(), &qdrant.UpsertPoints{
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CollectionName: OLD_COLLECTION,
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Points: []*qdrant.PointStruct{
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{
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Id: qdrant.NewIDNum(1),
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Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
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Text: "Example document",
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Model: OLD_MODEL,
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}),
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Payload: qdrant.NewValueMap(map[string]any{"text": "Example document"}),
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},
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},
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})
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client.Upsert(context.Background(), &qdrant.UpsertPoints{
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CollectionName: NEW_COLLECTION,
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Points: []*qdrant.PointStruct{
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{
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Id: qdrant.NewIDNum(1),
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// Use the new embedding model to encode the document
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Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
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Text: "Example document",
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Model: NEW_MODEL,
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}),
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Payload: qdrant.NewValueMap(map[string]any{"text": "Example document"}),
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},
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},
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})
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var lastOffset *qdrant.PointId
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batchSize := uint32(100) // Number of points to read in each batch
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reachedEnd := false
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for !reachedEnd {
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// Get the next batch of points from the old collection
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scrollResult, err := client.Scroll(context.Background(), &qdrant.ScrollPoints{
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CollectionName: OLD_COLLECTION,
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Limit: qdrant.PtrOf(batchSize),
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Offset: lastOffset,
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// Include payloads in the response, as we need them to re-embed the vectors
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WithPayload: qdrant.NewWithPayload(true),
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// We don't need the old vectors, so let's save on the bandwidth
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WithVectors: qdrant.NewWithVectors(false),
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})
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records := scrollResult
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// Re-embed the points using the new model
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points := make([]*qdrant.PointStruct, len(records))
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for idx, record := range records {
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text := ""
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if val, ok := record.Payload["text"]; ok {
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text = val.GetStringValue()
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}
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points[idx] = &qdrant.PointStruct{
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// Keep the original ID to ensure consistency
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Id: record.Id,
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// Use the new embedding model to encode the text from the payload,
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// assuming that was the original source of the embedding
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Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
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Text: text,
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Model: NEW_MODEL,
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}),
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// Keep the original payload
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Payload: record.Payload,
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}
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}
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// Upsert the re-embedded points into the new collection
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client.Upsert(context.Background(), &qdrant.UpsertPoints{
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CollectionName: NEW_COLLECTION,
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Points: points,
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// Only insert the point if a point with this ID does not already exist.
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UpdateMode: qdrant.UpdateMode_InsertOnly.Enum(),
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})
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// Check if we reached the end of the collection
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reachedEnd = (lastOffset == nil)
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}
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results, err := client.Query(context.Background(), &qdrant.QueryPoints{
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CollectionName: OLD_COLLECTION,
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Query: qdrant.NewQueryDocument(&qdrant.Document{
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Text: "my query",
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Model: OLD_MODEL,
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}),
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Limit: qdrant.PtrOf(uint64(10)),
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})
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results, err = client.Query(context.Background(), &qdrant.QueryPoints{
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CollectionName: NEW_COLLECTION,
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Query: qdrant.NewQueryDocument(&qdrant.Document{
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Text: "my query",
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Model: NEW_MODEL,
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}),
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Limit: qdrant.PtrOf(uint64(10)),
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})
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```
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+144
@@ -0,0 +1,144 @@
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```java
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import static io.qdrant.client.PointIdFactory.id;
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import static io.qdrant.client.QueryFactory.nearest;
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import static io.qdrant.client.ValueFactory.value;
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import static io.qdrant.client.VectorFactory.vector;
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import static io.qdrant.client.VectorsFactory.vectors;
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import io.qdrant.client.WithPayloadSelectorFactory;
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import io.qdrant.client.WithVectorsSelectorFactory;
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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.Distance;
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import io.qdrant.client.grpc.Collections.VectorParams;
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import io.qdrant.client.grpc.JsonWithInt.Value;
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import io.qdrant.client.grpc.Points.Document;
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import io.qdrant.client.grpc.Points.PointStruct;
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import io.qdrant.client.grpc.Points.QueryPoints;
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import io.qdrant.client.grpc.Points.UpsertPoints;
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import io.qdrant.client.grpc.Points.ScrollPoints;
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import io.qdrant.client.grpc.Points.UpdateMode;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.Map;
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client.createCollectionAsync(NEW_COLLECTION,
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VectorParams.newBuilder()
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.setSize(512) // Size of the new embedding vectors
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.setDistance(Distance.Cosine) // Similarity function for the new model
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.build()).get();
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client.upsertAsync(OLD_COLLECTION, List.of(
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PointStruct.newBuilder()
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.setId(id(1))
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.setVectors(
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vectors(
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vector(
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Document.newBuilder()
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.setText("Example document")
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.setModel(OLD_MODEL)
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.build())))
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.putAllPayload(Map.of("text", value("Example document")))
|
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.build())).get();
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client.upsertAsync(NEW_COLLECTION, List.of(
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PointStruct.newBuilder()
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.setId(id(1))
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// Use the new embedding model to encode the document
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.setVectors(
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vectors(
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vector(
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Document.newBuilder()
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.setText("Example document")
|
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.setModel(NEW_MODEL)
|
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.build())))
|
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.putAllPayload(Map.of("text", value("Example document")))
|
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.build())).get();
|
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|
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int batchSize = 100; // Number of points to read in each batch
|
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boolean reachedEnd = false;
|
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|
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// Get the next batch of points from the old collection
|
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var scrollBuilder = ScrollPoints.newBuilder()
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.setCollectionName(OLD_COLLECTION)
|
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.setLimit(batchSize)
|
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// Include payloads in the response, as we need them to re-embed the vectors
|
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.setWithPayload(WithPayloadSelectorFactory.enable(true))
|
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// We don't need the old vectors, so let's save on the bandwidth
|
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.setWithVectors(WithVectorsSelectorFactory.enable(false));
|
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|
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while (!reachedEnd) {
|
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var scrollResult = client.scrollAsync(scrollBuilder.build()).get();
|
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|
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var records = scrollResult.getResultList();
|
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|
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// Re-embed the points using the new model
|
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List<PointStruct> points = new ArrayList<>();
|
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for (var record : records) {
|
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String text = record.getPayloadMap().containsKey("text")
|
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? record.getPayloadMap().get("text").getStringValue()
|
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: "";
|
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|
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points.add(
|
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PointStruct.newBuilder()
|
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// Keep the original ID to ensure consistency
|
||||
.setId(record.getId())
|
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// Use the new embedding model to encode the text from the payload,
|
||||
// assuming that was the original source of the embedding
|
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.setVectors(
|
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vectors(
|
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vector(
|
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Document.newBuilder()
|
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.setText(text)
|
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.setModel(NEW_MODEL)
|
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.build())))
|
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// Keep the original payload
|
||||
.putAllPayload(record.getPayloadMap())
|
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.build());
|
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}
|
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|
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// Upsert the re-embedded points into the new collection
|
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client.upsertAsync(
|
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UpsertPoints.newBuilder()
|
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.setCollectionName(NEW_COLLECTION)
|
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.addAllPoints(points)
|
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// Only insert the point if a point with this ID does not already exist.
|
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.setUpdateMode(UpdateMode.InsertOnly)
|
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.build()).get();
|
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|
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// Check if we reached the end of the collection
|
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if (scrollResult.hasNextPageOffset()) {
|
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scrollBuilder.setOffset(scrollResult.getNextPageOffset());
|
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} else {
|
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reachedEnd = true;
|
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}
|
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}
|
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|
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QueryPoints oldRequest =
|
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QueryPoints.newBuilder()
|
||||
.setCollectionName(OLD_COLLECTION)
|
||||
.setQuery(
|
||||
nearest(
|
||||
Document.newBuilder()
|
||||
.setText("my query")
|
||||
.setModel(OLD_MODEL)
|
||||
.build()))
|
||||
.setLimit(10)
|
||||
.build();
|
||||
|
||||
var results = client.queryAsync(oldRequest).get();
|
||||
|
||||
QueryPoints newRequest =
|
||||
QueryPoints.newBuilder()
|
||||
.setCollectionName(NEW_COLLECTION)
|
||||
.setQuery(
|
||||
nearest(
|
||||
Document.newBuilder()
|
||||
.setText("my query")
|
||||
.setModel(NEW_MODEL)
|
||||
.build()))
|
||||
.setLimit(10)
|
||||
.build();
|
||||
|
||||
results = client.queryAsync(newRequest).get();
|
||||
```
|
||||
+60
@@ -0,0 +1,60 @@
|
||||
```csharp
|
||||
PointId? lastOffset = null;
|
||||
uint limit = 100; // Number of points to read in each batch
|
||||
bool reachedEnd = false;
|
||||
|
||||
while (!reachedEnd)
|
||||
{
|
||||
// Get the next batch of points from the old collection
|
||||
var scrollResult = await client.ScrollAsync(
|
||||
collectionName: OLD_COLLECTION,
|
||||
limit: limit,
|
||||
offset: lastOffset,
|
||||
// Include payloads in the response, as we need them to re-embed the vectors
|
||||
payloadSelector: true,
|
||||
// We don't need the old vectors, so let's save on the bandwidth
|
||||
vectorsSelector: false
|
||||
);
|
||||
|
||||
var records = scrollResult.Result;
|
||||
lastOffset = scrollResult.NextPageOffset;
|
||||
|
||||
// Re-embed the points using the new model
|
||||
var points = new List<PointStruct>();
|
||||
foreach (var record in records)
|
||||
{
|
||||
var text = record.Payload.ContainsKey("text")
|
||||
? record.Payload["text"].StringValue
|
||||
: "";
|
||||
|
||||
points.Add(new PointStruct
|
||||
{
|
||||
// Keep the original ID to ensure consistency
|
||||
Id = record.Id,
|
||||
// Use the new embedding model to encode the text from the payload,
|
||||
// assuming that was the original source of the embedding
|
||||
Vectors = new Document
|
||||
{
|
||||
Text = text,
|
||||
Model = NEW_MODEL
|
||||
},
|
||||
// Keep the original payload
|
||||
Payload = { record.Payload }
|
||||
});
|
||||
}
|
||||
|
||||
// Upsert the re-embedded points into the new collection
|
||||
await client.UpsertAsync(
|
||||
new()
|
||||
{
|
||||
CollectionName = NEW_COLLECTION,
|
||||
Points = { points },
|
||||
// Only insert the point if a point with this ID does not already exist.
|
||||
UpdateMode = UpdateMode.InsertOnly
|
||||
}
|
||||
);
|
||||
|
||||
// Check if we reached the end of the collection
|
||||
reachedEnd = (lastOffset == null);
|
||||
}
|
||||
```
|
||||
+53
@@ -0,0 +1,53 @@
|
||||
```go
|
||||
var lastOffset *qdrant.PointId
|
||||
batchSize := uint32(100) // Number of points to read in each batch
|
||||
reachedEnd := false
|
||||
|
||||
for !reachedEnd {
|
||||
// Get the next batch of points from the old collection
|
||||
scrollResult, err := client.Scroll(context.Background(), &qdrant.ScrollPoints{
|
||||
CollectionName: OLD_COLLECTION,
|
||||
Limit: qdrant.PtrOf(batchSize),
|
||||
Offset: lastOffset,
|
||||
// Include payloads in the response, as we need them to re-embed the vectors
|
||||
WithPayload: qdrant.NewWithPayload(true),
|
||||
// We don't need the old vectors, so let's save on the bandwidth
|
||||
WithVectors: qdrant.NewWithVectors(false),
|
||||
})
|
||||
|
||||
records := scrollResult
|
||||
|
||||
// Re-embed the points using the new model
|
||||
points := make([]*qdrant.PointStruct, len(records))
|
||||
for idx, record := range records {
|
||||
text := ""
|
||||
if val, ok := record.Payload["text"]; ok {
|
||||
text = val.GetStringValue()
|
||||
}
|
||||
|
||||
points[idx] = &qdrant.PointStruct{
|
||||
// Keep the original ID to ensure consistency
|
||||
Id: record.Id,
|
||||
// Use the new embedding model to encode the text from the payload,
|
||||
// assuming that was the original source of the embedding
|
||||
Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
|
||||
Text: text,
|
||||
Model: NEW_MODEL,
|
||||
}),
|
||||
// Keep the original payload
|
||||
Payload: record.Payload,
|
||||
}
|
||||
}
|
||||
|
||||
// Upsert the re-embedded points into the new collection
|
||||
client.Upsert(context.Background(), &qdrant.UpsertPoints{
|
||||
CollectionName: NEW_COLLECTION,
|
||||
Points: points,
|
||||
// Only insert the point if a point with this ID does not already exist.
|
||||
UpdateMode: qdrant.UpdateMode_InsertOnly.Enum(),
|
||||
})
|
||||
|
||||
// Check if we reached the end of the collection
|
||||
reachedEnd = (lastOffset == nil)
|
||||
}
|
||||
```
|
||||
+60
@@ -0,0 +1,60 @@
|
||||
```java
|
||||
int batchSize = 100; // Number of points to read in each batch
|
||||
boolean reachedEnd = false;
|
||||
|
||||
// Get the next batch of points from the old collection
|
||||
var scrollBuilder = ScrollPoints.newBuilder()
|
||||
.setCollectionName(OLD_COLLECTION)
|
||||
.setLimit(batchSize)
|
||||
// Include payloads in the response, as we need them to re-embed the vectors
|
||||
.setWithPayload(WithPayloadSelectorFactory.enable(true))
|
||||
// We don't need the old vectors, so let's save on the bandwidth
|
||||
.setWithVectors(WithVectorsSelectorFactory.enable(false));
|
||||
|
||||
while (!reachedEnd) {
|
||||
var scrollResult = client.scrollAsync(scrollBuilder.build()).get();
|
||||
|
||||
var records = scrollResult.getResultList();
|
||||
|
||||
// Re-embed the points using the new model
|
||||
List<PointStruct> points = new ArrayList<>();
|
||||
for (var record : records) {
|
||||
String text = record.getPayloadMap().containsKey("text")
|
||||
? record.getPayloadMap().get("text").getStringValue()
|
||||
: "";
|
||||
|
||||
points.add(
|
||||
PointStruct.newBuilder()
|
||||
// Keep the original ID to ensure consistency
|
||||
.setId(record.getId())
|
||||
// Use the new embedding model to encode the text from the payload,
|
||||
// assuming that was the original source of the embedding
|
||||
.setVectors(
|
||||
vectors(
|
||||
vector(
|
||||
Document.newBuilder()
|
||||
.setText(text)
|
||||
.setModel(NEW_MODEL)
|
||||
.build())))
|
||||
// Keep the original payload
|
||||
.putAllPayload(record.getPayloadMap())
|
||||
.build());
|
||||
}
|
||||
|
||||
// Upsert the re-embedded points into the new collection
|
||||
client.upsertAsync(
|
||||
UpsertPoints.newBuilder()
|
||||
.setCollectionName(NEW_COLLECTION)
|
||||
.addAllPoints(points)
|
||||
// Only insert the point if a point with this ID does not already exist.
|
||||
.setUpdateMode(UpdateMode.InsertOnly)
|
||||
.build()).get();
|
||||
|
||||
// Check if we reached the end of the collection
|
||||
if (scrollResult.hasNextPageOffset()) {
|
||||
scrollBuilder.setOffset(scrollResult.getNextPageOffset());
|
||||
} else {
|
||||
reachedEnd = true;
|
||||
}
|
||||
}
|
||||
```
|
||||
+45
@@ -0,0 +1,45 @@
|
||||
```python
|
||||
last_offset = None
|
||||
batch_size = 100 # Number of points to read in each batch
|
||||
reached_end = False
|
||||
|
||||
while not reached_end:
|
||||
# Get the next batch of points from the old collection
|
||||
records, last_offset = client.scroll(
|
||||
collection_name=OLD_COLLECTION,
|
||||
limit=batch_size,
|
||||
offset=last_offset,
|
||||
# Include payloads in the response, as we need them to re-embed the vectors
|
||||
with_payload=True,
|
||||
# We don't need the old vectors, so let's save on the bandwidth
|
||||
with_vectors=False,
|
||||
)
|
||||
|
||||
# Re-embed the points using the new model
|
||||
points = [
|
||||
models.PointStruct(
|
||||
# Keep the original ID to ensure consistency
|
||||
id=record.id,
|
||||
# Use the new embedding model to encode the text from the payload,
|
||||
# assuming that was the original source of the embedding
|
||||
vector=models.Document(
|
||||
text=(record.payload or {}).get("text", ""),
|
||||
model=NEW_MODEL,
|
||||
),
|
||||
# Keep the original payload
|
||||
payload=record.payload
|
||||
)
|
||||
for record in records
|
||||
]
|
||||
|
||||
# Upsert the re-embedded points into the new collection
|
||||
client.upsert(
|
||||
collection_name=NEW_COLLECTION,
|
||||
points=points,
|
||||
# Only insert the point if a point with this ID does not already exist.
|
||||
update_mode=models.UpdateMode.INSERT_ONLY
|
||||
)
|
||||
|
||||
# Check if we reached the end of the collection
|
||||
reached_end = (last_offset == None)
|
||||
```
|
||||
+58
@@ -0,0 +1,58 @@
|
||||
```rust
|
||||
let mut last_offset = None;
|
||||
let batch_size = 100; // Number of points to read in each batch
|
||||
|
||||
loop {
|
||||
// Get the next batch of points from the old collection
|
||||
let mut scroll_builder = ScrollPointsBuilder::new(old_collection)
|
||||
.limit(batch_size)
|
||||
// Include payloads in the response, as we need them to re-embed the vectors
|
||||
.with_payload(true)
|
||||
// We don't need the old vectors, so let's save on the bandwidth
|
||||
.with_vectors(false);
|
||||
|
||||
if let Some(offset) = last_offset {
|
||||
scroll_builder = scroll_builder.offset(offset);
|
||||
}
|
||||
|
||||
let scroll_result = client.scroll(scroll_builder).await?;
|
||||
|
||||
let records = scroll_result.result;
|
||||
last_offset = scroll_result.next_page_offset;
|
||||
|
||||
// Re-embed the points using the new model
|
||||
let points: Vec<PointStruct> = records
|
||||
.iter()
|
||||
.map(|record| {
|
||||
PointStruct::new(
|
||||
// Keep the original ID to ensure consistency
|
||||
record.id.clone().unwrap(),
|
||||
// Use the new embedding model to encode the text from the payload,
|
||||
// assuming that was the original source of the embedding
|
||||
Document::new(
|
||||
record.payload.get("text")
|
||||
.and_then(|v| v.as_str())
|
||||
.map_or("", |v| v),
|
||||
new_model,
|
||||
),
|
||||
// Keep the original payload
|
||||
record.payload.clone(),
|
||||
)
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Upsert the re-embedded points into the new collection
|
||||
client
|
||||
.upsert_points(
|
||||
// Only insert the point if a point with this ID does not already exist.
|
||||
UpsertPointsBuilder::new(new_collection, points)
|
||||
.update_mode(UpdateMode::InsertOnly),
|
||||
)
|
||||
.await?;
|
||||
|
||||
// Check if we reached the end of the collection
|
||||
if last_offset.is_none() {
|
||||
break;
|
||||
}
|
||||
}
|
||||
```
|
||||
+44
@@ -0,0 +1,44 @@
|
||||
```typescript
|
||||
let lastOffset: number | string | undefined = undefined;
|
||||
const batchSize = 100; // Number of points to read in each batch
|
||||
let reachedEnd = false;
|
||||
|
||||
while (!reachedEnd) {
|
||||
// Get the next batch of points from the old collection
|
||||
const scrollResult = await client.scroll(OLD_COLLECTION, {
|
||||
limit: batchSize,
|
||||
offset: lastOffset,
|
||||
// Include payloads in the response, as we need them to re-embed the vectors
|
||||
with_payload: true,
|
||||
// We don't need the old vectors, so let's save on the bandwidth
|
||||
with_vector: false,
|
||||
});
|
||||
|
||||
const records = scrollResult.points;
|
||||
lastOffset = scrollResult.next_page_offset as number | string | undefined;
|
||||
|
||||
// Re-embed the points using the new model
|
||||
const points = records.map((record) => ({
|
||||
// Keep the original ID to ensure consistency
|
||||
id: record.id,
|
||||
// Use the new embedding model to encode the text from the payload,
|
||||
// assuming that was the original source of the embedding
|
||||
vector: {
|
||||
text: ((record.payload?.text as string) ?? ""),
|
||||
model: NEW_MODEL,
|
||||
},
|
||||
// Keep the original payload
|
||||
payload: record.payload,
|
||||
}));
|
||||
|
||||
// Upsert the re-embedded points into the new collection
|
||||
await client.upsert(NEW_COLLECTION, {
|
||||
points,
|
||||
// Only insert the point if a point with this ID does not already exist.
|
||||
update_mode: "insert_only" as const,
|
||||
});
|
||||
|
||||
// Check if we reached the end of the collection
|
||||
reachedEnd = lastOffset == null;
|
||||
}
|
||||
```
|
||||
+98
@@ -0,0 +1,98 @@
|
||||
```python
|
||||
from qdrant_client import QdrantClient, models
|
||||
|
||||
client.create_collection(
|
||||
collection_name=NEW_COLLECTION,
|
||||
vectors_config=(
|
||||
models.VectorParams(
|
||||
size=512, # Size of the new embedding vectors
|
||||
distance=models.Distance.COSINE # Similarity function for the new model
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
client.upsert(
|
||||
collection_name=OLD_COLLECTION,
|
||||
points=[
|
||||
models.PointStruct(
|
||||
id=1,
|
||||
vector=models.Document(
|
||||
text="Example document",
|
||||
model=OLD_MODEL,
|
||||
),
|
||||
payload={"text": "Example document"}
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
client.upsert(
|
||||
collection_name=NEW_COLLECTION,
|
||||
points=[
|
||||
models.PointStruct(
|
||||
id=1,
|
||||
# Use the new embedding model to encode the document
|
||||
vector=models.Document(
|
||||
text="Example document",
|
||||
model=NEW_MODEL,
|
||||
),
|
||||
payload={"text": "Example document"}
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
last_offset = None
|
||||
batch_size = 100 # Number of points to read in each batch
|
||||
reached_end = False
|
||||
|
||||
while not reached_end:
|
||||
# Get the next batch of points from the old collection
|
||||
records, last_offset = client.scroll(
|
||||
collection_name=OLD_COLLECTION,
|
||||
limit=batch_size,
|
||||
offset=last_offset,
|
||||
# Include payloads in the response, as we need them to re-embed the vectors
|
||||
with_payload=True,
|
||||
# We don't need the old vectors, so let's save on the bandwidth
|
||||
with_vectors=False,
|
||||
)
|
||||
|
||||
# Re-embed the points using the new model
|
||||
points = [
|
||||
models.PointStruct(
|
||||
# Keep the original ID to ensure consistency
|
||||
id=record.id,
|
||||
# Use the new embedding model to encode the text from the payload,
|
||||
# assuming that was the original source of the embedding
|
||||
vector=models.Document(
|
||||
text=(record.payload or {}).get("text", ""),
|
||||
model=NEW_MODEL,
|
||||
),
|
||||
# Keep the original payload
|
||||
payload=record.payload
|
||||
)
|
||||
for record in records
|
||||
]
|
||||
|
||||
# Upsert the re-embedded points into the new collection
|
||||
client.upsert(
|
||||
collection_name=NEW_COLLECTION,
|
||||
points=points,
|
||||
# Only insert the point if a point with this ID does not already exist.
|
||||
update_mode=models.UpdateMode.INSERT_ONLY
|
||||
)
|
||||
|
||||
# Check if we reached the end of the collection
|
||||
reached_end = (last_offset == None)
|
||||
|
||||
results = client.query_points(
|
||||
collection_name=OLD_COLLECTION,
|
||||
query=models.Document(text="my query", model=OLD_MODEL),
|
||||
limit=10,
|
||||
)
|
||||
|
||||
results = client.query_points(
|
||||
collection_name=NEW_COLLECTION,
|
||||
query=models.Document(text="my query", model=NEW_MODEL),
|
||||
limit=10,
|
||||
)
|
||||
```
|
||||
+110
@@ -0,0 +1,110 @@
|
||||
```rust
|
||||
use qdrant_client::qdrant::{
|
||||
CreateCollectionBuilder, Distance, Document, PointStruct, Query, QueryPointsBuilder,
|
||||
ScrollPointsBuilder, UpdateMode, UpsertPointsBuilder, VectorParamsBuilder,
|
||||
};
|
||||
use qdrant_client::Qdrant;
|
||||
|
||||
client
|
||||
.create_collection(
|
||||
CreateCollectionBuilder::new(new_collection)
|
||||
.vectors_config(VectorParamsBuilder::new(512, Distance::Cosine)), // Size of the new embedding vectors
|
||||
)
|
||||
.await?;
|
||||
|
||||
client
|
||||
.upsert_points(UpsertPointsBuilder::new(
|
||||
old_collection,
|
||||
vec![PointStruct::new(
|
||||
1,
|
||||
Document::new("Example document", old_model),
|
||||
[("text", "Example document".into())],
|
||||
)],
|
||||
))
|
||||
.await?;
|
||||
|
||||
client
|
||||
.upsert_points(UpsertPointsBuilder::new(
|
||||
new_collection,
|
||||
vec![PointStruct::new(
|
||||
1,
|
||||
// Use the new embedding model to encode the document
|
||||
Document::new("Example document", new_model),
|
||||
[("text", "Example document".into())],
|
||||
)],
|
||||
))
|
||||
.await?;
|
||||
|
||||
let mut last_offset = None;
|
||||
let batch_size = 100; // Number of points to read in each batch
|
||||
|
||||
loop {
|
||||
// Get the next batch of points from the old collection
|
||||
let mut scroll_builder = ScrollPointsBuilder::new(old_collection)
|
||||
.limit(batch_size)
|
||||
// Include payloads in the response, as we need them to re-embed the vectors
|
||||
.with_payload(true)
|
||||
// We don't need the old vectors, so let's save on the bandwidth
|
||||
.with_vectors(false);
|
||||
|
||||
if let Some(offset) = last_offset {
|
||||
scroll_builder = scroll_builder.offset(offset);
|
||||
}
|
||||
|
||||
let scroll_result = client.scroll(scroll_builder).await?;
|
||||
|
||||
let records = scroll_result.result;
|
||||
last_offset = scroll_result.next_page_offset;
|
||||
|
||||
// Re-embed the points using the new model
|
||||
let points: Vec<PointStruct> = records
|
||||
.iter()
|
||||
.map(|record| {
|
||||
PointStruct::new(
|
||||
// Keep the original ID to ensure consistency
|
||||
record.id.clone().unwrap(),
|
||||
// Use the new embedding model to encode the text from the payload,
|
||||
// assuming that was the original source of the embedding
|
||||
Document::new(
|
||||
record.payload.get("text")
|
||||
.and_then(|v| v.as_str())
|
||||
.map_or("", |v| v),
|
||||
new_model,
|
||||
),
|
||||
// Keep the original payload
|
||||
record.payload.clone(),
|
||||
)
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Upsert the re-embedded points into the new collection
|
||||
client
|
||||
.upsert_points(
|
||||
// Only insert the point if a point with this ID does not already exist.
|
||||
UpsertPointsBuilder::new(new_collection, points)
|
||||
.update_mode(UpdateMode::InsertOnly),
|
||||
)
|
||||
.await?;
|
||||
|
||||
// Check if we reached the end of the collection
|
||||
if last_offset.is_none() {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
let results = client
|
||||
.query(
|
||||
QueryPointsBuilder::new(old_collection)
|
||||
.query(Query::new_nearest(Document::new("my query", old_model)))
|
||||
.limit(10),
|
||||
)
|
||||
.await?;
|
||||
|
||||
let results = client
|
||||
.query(
|
||||
QueryPointsBuilder::new(new_collection)
|
||||
.query(Query::new_nearest(Document::new("my query", new_model)))
|
||||
.limit(10),
|
||||
)
|
||||
.await?;
|
||||
```
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
```csharp
|
||||
results = await client.QueryAsync(
|
||||
collectionName: NEW_COLLECTION,
|
||||
query: new Document
|
||||
{
|
||||
Text = "my query",
|
||||
Model = NEW_MODEL
|
||||
},
|
||||
limit: 10
|
||||
);
|
||||
```
|
||||
+10
@@ -0,0 +1,10 @@
|
||||
```go
|
||||
results, err = client.Query(context.Background(), &qdrant.QueryPoints{
|
||||
CollectionName: NEW_COLLECTION,
|
||||
Query: qdrant.NewQueryDocument(&qdrant.Document{
|
||||
Text: "my query",
|
||||
Model: NEW_MODEL,
|
||||
}),
|
||||
Limit: qdrant.PtrOf(uint64(10)),
|
||||
})
|
||||
```
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
```java
|
||||
QueryPoints newRequest =
|
||||
QueryPoints.newBuilder()
|
||||
.setCollectionName(NEW_COLLECTION)
|
||||
.setQuery(
|
||||
nearest(
|
||||
Document.newBuilder()
|
||||
.setText("my query")
|
||||
.setModel(NEW_MODEL)
|
||||
.build()))
|
||||
.setLimit(10)
|
||||
.build();
|
||||
|
||||
results = client.queryAsync(newRequest).get();
|
||||
```
|
||||
+7
@@ -0,0 +1,7 @@
|
||||
```python
|
||||
results = client.query_points(
|
||||
collection_name=NEW_COLLECTION,
|
||||
query=models.Document(text="my query", model=NEW_MODEL),
|
||||
limit=10,
|
||||
)
|
||||
```
|
||||
+9
@@ -0,0 +1,9 @@
|
||||
```rust
|
||||
let results = client
|
||||
.query(
|
||||
QueryPointsBuilder::new(new_collection)
|
||||
.query(Query::new_nearest(Document::new("my query", new_model)))
|
||||
.limit(10),
|
||||
)
|
||||
.await?;
|
||||
```
|
||||
+9
@@ -0,0 +1,9 @@
|
||||
```typescript
|
||||
const resultsNew = await client.query(NEW_COLLECTION, {
|
||||
query: {
|
||||
text: "my query",
|
||||
model: NEW_MODEL,
|
||||
},
|
||||
limit: 10,
|
||||
});
|
||||
```
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
```csharp
|
||||
var results = await client.QueryAsync(
|
||||
collectionName: OLD_COLLECTION,
|
||||
query: new Document
|
||||
{
|
||||
Text = "my query",
|
||||
Model = OLD_MODEL
|
||||
},
|
||||
limit: 10
|
||||
);
|
||||
```
|
||||
+10
@@ -0,0 +1,10 @@
|
||||
```go
|
||||
results, err := client.Query(context.Background(), &qdrant.QueryPoints{
|
||||
CollectionName: OLD_COLLECTION,
|
||||
Query: qdrant.NewQueryDocument(&qdrant.Document{
|
||||
Text: "my query",
|
||||
Model: OLD_MODEL,
|
||||
}),
|
||||
Limit: qdrant.PtrOf(uint64(10)),
|
||||
})
|
||||
```
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
```java
|
||||
QueryPoints oldRequest =
|
||||
QueryPoints.newBuilder()
|
||||
.setCollectionName(OLD_COLLECTION)
|
||||
.setQuery(
|
||||
nearest(
|
||||
Document.newBuilder()
|
||||
.setText("my query")
|
||||
.setModel(OLD_MODEL)
|
||||
.build()))
|
||||
.setLimit(10)
|
||||
.build();
|
||||
|
||||
var results = client.queryAsync(oldRequest).get();
|
||||
```
|
||||
+7
@@ -0,0 +1,7 @@
|
||||
```python
|
||||
results = client.query_points(
|
||||
collection_name=OLD_COLLECTION,
|
||||
query=models.Document(text="my query", model=OLD_MODEL),
|
||||
limit=10,
|
||||
)
|
||||
```
|
||||
+9
@@ -0,0 +1,9 @@
|
||||
```rust
|
||||
let results = client
|
||||
.query(
|
||||
QueryPointsBuilder::new(old_collection)
|
||||
.query(Query::new_nearest(Document::new("my query", old_model)))
|
||||
.limit(10),
|
||||
)
|
||||
.await?;
|
||||
```
|
||||
+9
@@ -0,0 +1,9 @@
|
||||
```typescript
|
||||
const results = await client.query(OLD_COLLECTION, {
|
||||
query: {
|
||||
text: "my query",
|
||||
model: OLD_MODEL,
|
||||
},
|
||||
limit: 10,
|
||||
});
|
||||
```
|
||||
+96
@@ -0,0 +1,96 @@
|
||||
```typescript
|
||||
import { QdrantClient } from "@qdrant/js-client-rest";
|
||||
|
||||
await client.createCollection(NEW_COLLECTION, {
|
||||
vectors: {
|
||||
size: 512, // Size of the new embedding vectors
|
||||
distance: "Cosine", // Similarity function for the new model
|
||||
},
|
||||
});
|
||||
|
||||
await client.upsert(OLD_COLLECTION, {
|
||||
points: [
|
||||
{
|
||||
id: 1,
|
||||
vector: {
|
||||
text: "Example document",
|
||||
model: OLD_MODEL,
|
||||
},
|
||||
payload: { text: "Example document" },
|
||||
},
|
||||
],
|
||||
});
|
||||
|
||||
await client.upsert(NEW_COLLECTION, {
|
||||
points: [
|
||||
{
|
||||
id: 1,
|
||||
// Use the new embedding model to encode the document
|
||||
vector: {
|
||||
text: "Example document",
|
||||
model: NEW_MODEL,
|
||||
},
|
||||
payload: { text: "Example document" },
|
||||
},
|
||||
],
|
||||
});
|
||||
|
||||
let lastOffset: number | string | undefined = undefined;
|
||||
const batchSize = 100; // Number of points to read in each batch
|
||||
let reachedEnd = false;
|
||||
|
||||
while (!reachedEnd) {
|
||||
// Get the next batch of points from the old collection
|
||||
const scrollResult = await client.scroll(OLD_COLLECTION, {
|
||||
limit: batchSize,
|
||||
offset: lastOffset,
|
||||
// Include payloads in the response, as we need them to re-embed the vectors
|
||||
with_payload: true,
|
||||
// We don't need the old vectors, so let's save on the bandwidth
|
||||
with_vector: false,
|
||||
});
|
||||
|
||||
const records = scrollResult.points;
|
||||
lastOffset = scrollResult.next_page_offset as number | string | undefined;
|
||||
|
||||
// Re-embed the points using the new model
|
||||
const points = records.map((record) => ({
|
||||
// Keep the original ID to ensure consistency
|
||||
id: record.id,
|
||||
// Use the new embedding model to encode the text from the payload,
|
||||
// assuming that was the original source of the embedding
|
||||
vector: {
|
||||
text: ((record.payload?.text as string) ?? ""),
|
||||
model: NEW_MODEL,
|
||||
},
|
||||
// Keep the original payload
|
||||
payload: record.payload,
|
||||
}));
|
||||
|
||||
// Upsert the re-embedded points into the new collection
|
||||
await client.upsert(NEW_COLLECTION, {
|
||||
points,
|
||||
// Only insert the point if a point with this ID does not already exist.
|
||||
update_mode: "insert_only" as const,
|
||||
});
|
||||
|
||||
// Check if we reached the end of the collection
|
||||
reachedEnd = lastOffset == null;
|
||||
}
|
||||
|
||||
const results = await client.query(OLD_COLLECTION, {
|
||||
query: {
|
||||
text: "my query",
|
||||
model: OLD_MODEL,
|
||||
},
|
||||
limit: 10,
|
||||
});
|
||||
|
||||
const resultsNew = await client.query(NEW_COLLECTION, {
|
||||
query: {
|
||||
text: "my query",
|
||||
model: NEW_MODEL,
|
||||
},
|
||||
limit: 10,
|
||||
});
|
||||
```
|
||||
+19
@@ -0,0 +1,19 @@
|
||||
```csharp
|
||||
await client.UpsertAsync(
|
||||
collectionName: NEW_COLLECTION,
|
||||
points: new List<PointStruct>
|
||||
{
|
||||
new()
|
||||
{
|
||||
Id = 1,
|
||||
// Use the new embedding model to encode the document
|
||||
Vectors = new Document
|
||||
{
|
||||
Text = "Example document",
|
||||
Model = NEW_MODEL
|
||||
},
|
||||
Payload = { ["text"] = "Example document" }
|
||||
}
|
||||
}
|
||||
);
|
||||
```
|
||||
+16
@@ -0,0 +1,16 @@
|
||||
```go
|
||||
client.Upsert(context.Background(), &qdrant.UpsertPoints{
|
||||
CollectionName: NEW_COLLECTION,
|
||||
Points: []*qdrant.PointStruct{
|
||||
{
|
||||
Id: qdrant.NewIDNum(1),
|
||||
// Use the new embedding model to encode the document
|
||||
Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
|
||||
Text: "Example document",
|
||||
Model: NEW_MODEL,
|
||||
}),
|
||||
Payload: qdrant.NewValueMap(map[string]any{"text": "Example document"}),
|
||||
},
|
||||
},
|
||||
})
|
||||
```
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
```java
|
||||
client.upsertAsync(NEW_COLLECTION, List.of(
|
||||
PointStruct.newBuilder()
|
||||
.setId(id(1))
|
||||
// Use the new embedding model to encode the document
|
||||
.setVectors(
|
||||
vectors(
|
||||
vector(
|
||||
Document.newBuilder()
|
||||
.setText("Example document")
|
||||
.setModel(NEW_MODEL)
|
||||
.build())))
|
||||
.putAllPayload(Map.of("text", value("Example document")))
|
||||
.build())).get();
|
||||
```
|
||||
+16
@@ -0,0 +1,16 @@
|
||||
```python
|
||||
client.upsert(
|
||||
collection_name=NEW_COLLECTION,
|
||||
points=[
|
||||
models.PointStruct(
|
||||
id=1,
|
||||
# Use the new embedding model to encode the document
|
||||
vector=models.Document(
|
||||
text="Example document",
|
||||
model=NEW_MODEL,
|
||||
),
|
||||
payload={"text": "Example document"}
|
||||
)
|
||||
]
|
||||
)
|
||||
```
|
||||
+13
@@ -0,0 +1,13 @@
|
||||
```rust
|
||||
client
|
||||
.upsert_points(UpsertPointsBuilder::new(
|
||||
new_collection,
|
||||
vec![PointStruct::new(
|
||||
1,
|
||||
// Use the new embedding model to encode the document
|
||||
Document::new("Example document", new_model),
|
||||
[("text", "Example document".into())],
|
||||
)],
|
||||
))
|
||||
.await?;
|
||||
```
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
```typescript
|
||||
await client.upsert(NEW_COLLECTION, {
|
||||
points: [
|
||||
{
|
||||
id: 1,
|
||||
// Use the new embedding model to encode the document
|
||||
vector: {
|
||||
text: "Example document",
|
||||
model: NEW_MODEL,
|
||||
},
|
||||
payload: { text: "Example document" },
|
||||
},
|
||||
],
|
||||
});
|
||||
```
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
```csharp
|
||||
await client.UpsertAsync(
|
||||
collectionName: OLD_COLLECTION,
|
||||
points: new List<PointStruct>
|
||||
{
|
||||
new()
|
||||
{
|
||||
Id = 1,
|
||||
Vectors = new Document
|
||||
{
|
||||
Text = "Example document",
|
||||
Model = OLD_MODEL
|
||||
},
|
||||
Payload = { ["text"] = "Example document" }
|
||||
}
|
||||
}
|
||||
);
|
||||
```
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
```go
|
||||
client.Upsert(context.Background(), &qdrant.UpsertPoints{
|
||||
CollectionName: OLD_COLLECTION,
|
||||
Points: []*qdrant.PointStruct{
|
||||
{
|
||||
Id: qdrant.NewIDNum(1),
|
||||
Vectors: qdrant.NewVectorsDocument(&qdrant.Document{
|
||||
Text: "Example document",
|
||||
Model: OLD_MODEL,
|
||||
}),
|
||||
Payload: qdrant.NewValueMap(map[string]any{"text": "Example document"}),
|
||||
},
|
||||
},
|
||||
})
|
||||
```
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
```java
|
||||
client.upsertAsync(OLD_COLLECTION, List.of(
|
||||
PointStruct.newBuilder()
|
||||
.setId(id(1))
|
||||
.setVectors(
|
||||
vectors(
|
||||
vector(
|
||||
Document.newBuilder()
|
||||
.setText("Example document")
|
||||
.setModel(OLD_MODEL)
|
||||
.build())))
|
||||
.putAllPayload(Map.of("text", value("Example document")))
|
||||
.build())).get();
|
||||
```
|
||||
+15
@@ -0,0 +1,15 @@
|
||||
```python
|
||||
client.upsert(
|
||||
collection_name=OLD_COLLECTION,
|
||||
points=[
|
||||
models.PointStruct(
|
||||
id=1,
|
||||
vector=models.Document(
|
||||
text="Example document",
|
||||
model=OLD_MODEL,
|
||||
),
|
||||
payload={"text": "Example document"}
|
||||
)
|
||||
]
|
||||
)
|
||||
```
|
||||
+12
@@ -0,0 +1,12 @@
|
||||
```rust
|
||||
client
|
||||
.upsert_points(UpsertPointsBuilder::new(
|
||||
old_collection,
|
||||
vec![PointStruct::new(
|
||||
1,
|
||||
Document::new("Example document", old_model),
|
||||
[("text", "Example document".into())],
|
||||
)],
|
||||
))
|
||||
.await?;
|
||||
```
|
||||
+14
@@ -0,0 +1,14 @@
|
||||
```typescript
|
||||
await client.upsert(OLD_COLLECTION, {
|
||||
points: [
|
||||
{
|
||||
id: 1,
|
||||
vector: {
|
||||
text: "Example document",
|
||||
model: OLD_MODEL,
|
||||
},
|
||||
payload: { text: "Example document" },
|
||||
},
|
||||
],
|
||||
});
|
||||
```
|
||||
Reference in New Issue
Block a user