From e41a97c34a41fb7e277dc3205346936fc5004fc8 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Mon, 12 Aug 2024 18:55:34 -0700 Subject: [PATCH 01/19] add version 1.11 article --- .../content/blog/azure-marketplace.md | 2 +- qdrant-landing/content/blog/qdrant-1.11.x.md | 237 ++++++++++++++++++ .../blog/qdrant-cpu-intel-benchmark.md | 2 +- .../blog/qdrant-stars-announcement copy.md | 3 +- .../blog/qdrant-1.11.x/defragmentation.png | Bin 0 -> 90716 bytes .../blog/qdrant-1.11.x/on-disk-payload.png | Bin 0 -> 107643 bytes .../blog/qdrant-1.11.x/social_preview.png | Bin 0 -> 1066663 bytes 7 files changed, 240 insertions(+), 4 deletions(-) create mode 100644 qdrant-landing/content/blog/qdrant-1.11.x.md create mode 100644 qdrant-landing/static/blog/qdrant-1.11.x/defragmentation.png create mode 100644 qdrant-landing/static/blog/qdrant-1.11.x/on-disk-payload.png create mode 100644 qdrant-landing/static/blog/qdrant-1.11.x/social_preview.png diff --git a/qdrant-landing/content/blog/azure-marketplace.md b/qdrant-landing/content/blog/azure-marketplace.md index 72f261584..e893b8ef1 100644 --- a/qdrant-landing/content/blog/azure-marketplace.md +++ b/qdrant-landing/content/blog/azure-marketplace.md @@ -6,7 +6,7 @@ description: Discover the power of Qdrant on Azure Marketplace! Get started toda preview_image: /blog/azure-marketplace/azure-marketplace.png date: 2024-03-26T10:30:00Z author: David Myriel -featured: true +featured: false weight: 0 tags: - Qdrant diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md new file mode 100644 index 000000000..841eab891 --- /dev/null +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -0,0 +1,237 @@ +--- +title: "Qdrant 1.11 - The Vector Stronghold: Optimizing Data Structures." +draft: false +short_description: "On-Disk Payload Index. UUID Payload Support. Tenant Defragmentation." +description: "Enhanced payload flexibility with on-disk indexing, UUID support, and tenant-based defragmentation." +preview_image: /blog/qdrant-1.11.x/social_preview.png +social_preview_image: /blog/qdrant-1.11.x/social_preview.png +date: 2024-08-12T00:00:00-08:00 +author: David Myriel +featured: true +tags: + - vector search + - on-disk payload index + - tenant defragmentation + - group-by search + - random sampling +--- + +[Qdrant 1.11.0 is out!](https://github.com/qdrant/qdrant/releases/tag/v1.11.0) This release largely focuses on improvements that imrpove memory usage and optimize segments. However, there are a few cool minor features, so let's look at the whole list: + +Optimized Data Structures:
+**Defragmentation:** Storage for multitenant workloads is more optimized and scales better.
+**On-Disk Payload:** You can now store less frequently used data on disk, rather than on RAM.
+**UUID Support:** Additional data type for payload can result in significant memory savings. + +Improved Query API:
+**GroupBy Endpoint:** Use this query method to group results by a certain payload field.
+**Random Sampling:** Select a subset of data points from a larger dataset in a random manner.
+**Hybrid Search Fusion:** We are adding the Distribution-Based Score Fusion (DBSF) method.
+ +New Web UI Tools:
+**Search Quality Tool:** Test the precision of your semantic search requests in real time.
+**Graph Exploration Tool:** Visualize vector search in context-based exploratory scenarios.
+ +### Quick Recap: Multitenant Workloads + +Before we dive into the specifics of our optimizations, let's first go over Multitenancy. This is one of our most significant features, [best used for at scaling and data isolation](https://qdrant.tech/articles/multitenancy/). + +If you’re using Qdrant to manage data for multiple users, regions, or workspaces (tenants), we suggest setting up a [multitenant environment](/documentation/guides/multiple-partitions/). This approach keeps all tenant data in a single global collection, with points separated and isolated by their payload. + +To avoid slow and unnecessary indexing, it’s better to create an index for each relevant payload rather than indexing the entire collection globally. Since some data is indexed more frequently, you can focus on building indexes for specific regions, workspaces, or users. + +*For more details on scaling best practices, read [How to Implement Multitenancy and Custom Sharding](https://qdrant.tech/articles/multitenancy/).* + +### Defragmentation of Tenant Storage + +With version 1.11, Qdrant changes how vectors from the same tenant are stored on disk, placing them **closer together** for faster bulk reading and reduced scaling costs. This approach optimizes storage and retrieval operations for different tenants, leading to more efficient system performance and better resource utilization. + +> Defragmentation can significantly improve performance. In the coming weeks, we will share **benchmark data** to demonstrate performance gains. + +**Figure 1:** Re-ordering by payload can significantly speed up access to hot and cold data. + +![defragmentation](/blog/qdrant-1.11.x/defragmentation.png) + +**Example:** When creating an index, you may set `is_tenant=true`. This configuration will optimize the storage based on your collection’s usage patterns. + +```http +PUT /collections/{collection_name}/index +{ + "field_name": "workspace_2", + "field_schema": { + "type": "keyword", + "is_tenant": true + } +} +``` +As a result, the storage structure will be organized in a way to co-locate vectors of the same tenant together. + +*To learn more about defragmentation, read the [Multitenancy documentation](/documentation/guides/multiple-partitions/).* + +### On-Disk Support for the Payload Index + +When managing billions of records across millions of tenants, keeping all data in RAM is inefficient, especially when only a small subset is frequently accessed. As of 1.11, you can offload "cold" data to disk and cache the “hot” data in RAM. + +*This feature can help you manage a high number of different payload indexes, which is beneficial if you are working with large varied datasets.* + +**Figure 2:** By moving the Workspace 2 index to disk, the system can free up valuable memory resources for Workspaces 1,3 and 4, that are accessed more frequently. + +![on-disk-payload](/blog/qdrant-1.11.x/on-disk-payload.png) + +**Example:** As you create an index for Workspace 2, set the `on_disk` parameter. + +```html +PUT /collections/{collection_name}/index +{ + "field_name": "workspace_2", + "field_schema": { + "type": "keyword", + "is_tenant": true, + "on_disk": true + } +} +``` + +By moving the index to disk, Qdrant can handle larger datasets that exceed the capacity of RAM, making the system more scalable and capable of storing more data without being constrained by memory limitations. + +*To learn more about this, read the [Indexing documentation](/documentation/concepts/index/).* + +### UUID Datatype for the Payload Index + +Many Qdrant users rely on UUIDs in their payloads, but storing these as strings comes with a substantial memory overhead—approximately 60 bytes per UUID. In reality, UUIDs only require 16 bytes of storage when stored as raw bytes. + +To address this inefficiency, we’ve developed a new index type tailored specifically for UUIDs that stores them internally as bytes, **reducing memory usage by up to 3.75x.** + +**Example:** When adding two separate points, indicate their UUID in the payload. In this example, both data points belong to the same user (with the same UUID). + +```html +PUT /collections/{collection_name}/points +{ + "points": [ + { + "id": 1, + "vector": [0.05, 0.61, 0.76, 0.74], + "payload": {"id": 550e8400-e29b-41d4-a716-446655440000} + }, + { + "id": 2, + "vector": [0.19, 0.81, 0.75, 0.11], + "payload": {"id": 550e8400-e29b-41d4-a716-446655440000} + }, + ] +} +``` + +> For organizations that have numerous users and UUIDs, this simple fix can significantly reduce the cluster size and improve efficiency. + +*To learn more about this, read the [Payload documentation](/documentation/concepts/payload/).* + +### Query API: Groups Endpoint + +When searching over data, you can group results by specific payload field, which is useful when you have multiple data points for the same item and you want to avoid redundant entries in the results. + +**Example:** If a large document is divided into several chunks, and you need to search or make recommendations on a per-document basis, you can group the results by the `document_id`. + +```python +from qdrant_client import QdrantClient, models + +client = QdrantClient(url="http://localhost:6333") + +client.query_point_groups( + collection_name="{collection_name}", + query=[0.01, 0.45, 0.67], + group_by="document_id", + limit=4, + group_size=2, +) +``` + +This endpoint will retrieve the best N points for each document, assuming that the payload of the points contains the document ID. Sometimes, the best N points cannot be fulfilled due to lack of points or a big distance with respect to the query. In every case, the `group_size` is a best-effort parameter, similar to the limit parameter. + +*For more information on grouping capabilities refer to our [Hybrid Queries documentation](/documentation/concepts/hybrid-queries/).* + + +### Query API: Random Sampling + +Our [Food Discovery Demo](https://food-discovery.qdrant.tech) always shows a random sample of foods from the larger dataset. Now you can set the randomization from a basic Query API endpoint. + +When calling the Query API, you will be able to select a subset of data points from a larger dataset in a random manner. + +*This technique is often used to reduce the computational load, improve query response times, or provide a representative sample of the data for various analytical purposes.* + +**Example:** When querying the collection, you can configure it to retrieve a random sample of data. + +```python +from qdrant_client import QdrantClient, models + +client = QdrantClient(url="http://localhost:6333") + +# Random sampling (as of 1.11.0) +sampled = client.query_points( + collection_name="{collection_name}", + query=models.SampleQuery(sample=models.Sample.Random) +) +``` +*To learn more, check out the [Query API documentation](/documentation/concepts/hybrid-queries/).* + +### Query API: Distribution-Based Score Fusion + +In version 1.10, we added Reciprocal Rank Fusion (RRF) as a way of fusing results from Hybrid Queries. Now we are adding Distribution-Based Score Fusion (DBSF). Michelangiolo Mazzeschi talks more about this fusion method in his latest [Medium article](https://medium.com/plain-simple-software/distribution-based-score-fusion-dbsf-a-new-approach-to-vector-search-ranking-f87c37488b18). + +*DBSF normalizes the scores of the points in each query, using the mean +/- the 3rd standard deviation as limits, and then sums the scores of the same point across different queries.* + + +**Example:** To fuse `prefetch` results from sparse and dense queries, set the `"fusion": "dbsf"` +```html +POST /collections/{collection_name}/points/query +{ + "prefetch": [ + { + "query": { + "indices": [1, 42], // <┐ + "values": [0.22, 0.8] // <┴─Sparse vector + }, + "using": "sparse", + "limit": 20 + }, + { + "query": [0.01, 0.45, 0.67, ...], // <-- Dense vector + "using": "dense", + "limit": 20 + } + ], + "query": { "fusion": “dbsf" }, // <--- Distribution Based Score Fusion + "limit": 10 +} +``` + +Note that `dbsf` is stateless and calculates the normalization limits only based on the results of each query, not on all the scores that it has seen. + +*To learn more, check out the [Hybrid Queries documentation](/documentation/concepts/hybrid-queries/).* + +## Web UI: Search Quality Tool + +We have updated the Qdrant Web UI with additional testing functionality. Now you can check the quality of your search requests in real time and measure it against exact search. + +**Try it:** In the Dashboard, go to collection settings and test the **Precision** from the Search Quality menu tab. + +> The feature will conduct semantic search for each point and produce a report below. + + + + +## Web UI: Graph Exploration Tool + +Deeper exploration is highly dependant on expanding context. This is something we previously covered in the [Discovery Needs Context](/articles/discovery-search/) article earlier this year. Now, we have developed a UI feature to help you visualize how semantic search can be used for exploratory and recommendation purposes. + +**Try it:** Using the feature is pretty self-explanatory. Each collection's dataset can be explored from the **Graph** tab. As you see the images change, you can steer your search in the direction of specific characteristics that interest you. + +*Search results will become more "distilled" and tailored to your preferences.* + + + +## Next Steps + +If you’re new to Qdrant, now is the perfect time to start. Check out our [documentation](/documentation/) guides and see why Qdrant is the go-to solution for vector search. + +We’re very happy to bring you this latest version of Qdrant, and we can’t wait to see what you build with it. As always, your feedback is invaluable—feel free to reach out with any questions or comments on our [community forum](https://qdrant.to/discord). \ No newline at end of file diff --git a/qdrant-landing/content/blog/qdrant-cpu-intel-benchmark.md b/qdrant-landing/content/blog/qdrant-cpu-intel-benchmark.md index 01fc6e1e7..42d6d1573 100644 --- a/qdrant-landing/content/blog/qdrant-cpu-intel-benchmark.md +++ b/qdrant-landing/content/blog/qdrant-cpu-intel-benchmark.md @@ -8,7 +8,7 @@ preview_image: /blog/qdrant-cpu-intel-benchmark/social_preview.jpg social_preview_image: /blog/qdrant-cpu-intel-benchmark/social_preview.jpg date: 2024-05-10T00:00:00-08:00 author: David Myriel, Kumar Shivendu -featured: false +featured: true tags: - vector search - intel benchmark diff --git a/qdrant-landing/content/blog/qdrant-stars-announcement copy.md b/qdrant-landing/content/blog/qdrant-stars-announcement copy.md index 599b1fadc..1328d213a 100644 --- a/qdrant-landing/content/blog/qdrant-stars-announcement copy.md +++ b/qdrant-landing/content/blog/qdrant-stars-announcement copy.md @@ -6,10 +6,9 @@ short_description: Qdrant Stars recognizes and supports key contributors to the description: Say hello to the first Qdrant Stars and learn more about our new ambassador program! preview_image: /blog/qdrant-stars-announcement/preview-image.png social_preview_image: /blog/qdrant-stars-announcement/preview-image.png - date: 2024-05-19T11:57:37-03:00 author: Sabrina Aquino -featured: true +featured: false tags: - news - vector search diff --git a/qdrant-landing/static/blog/qdrant-1.11.x/defragmentation.png b/qdrant-landing/static/blog/qdrant-1.11.x/defragmentation.png new file mode 100644 index 0000000000000000000000000000000000000000..7d6a84061cec75965bd81bfa379afe5426d26bdd GIT binary patch literal 90716 zcmeFZcT|(<*Df4!1QiAS0ipup;0#5&NC#0zM3Ew3=n#r@=~4p;ii%Qos8W?G2@ray z2~wpiy@y^xFQFtPIZv1YXMNvV-}=^B=d82d_ssJLgz((=z4yMaeeLp;caJqx&Yr$} 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zCbCX!H$6XnIdS#@V}(@}a^#Zad`l^lO5%REp%ww}b=yxqWsXbavUu(`J^RhAm{sS> zA|)%KNF`ylp#VNts_8Pfu(Dky~((^*1J z7^m1-M=ZiTUvj}%$Jtk6T;%aOk(~M%2sxuE!U6?cZ!w*e;K2u?h36Exd=4Ca;QO1N z?{^)y+m4%5@ae^Rq*jH`iIk$KRg46clp@KArKy@?`?;{}ns={}0$5jd^{}Q2Xn!f0 zzno`Mo|?C5=GEBo-KM9^9;#TdxX$5IWv>k~k<7ojS#+uFg5{sTJ+K)D z79Th+fpdgU#|vklsFzCTB2|lo(mKuOIT3uI?+kBmb{t}2nrCuRoQtrY4}Hh&*t2z( zSGyi+<>Ui}OlOr?SS*RlI%qNuw{g6?9oY4TKYzOLrw7^kvq`4_0000 Date: Mon, 12 Aug 2024 18:59:13 -0700 Subject: [PATCH 02/19] fix link --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 841eab891..e506b0952 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -94,7 +94,7 @@ PUT /collections/{collection_name}/index By moving the index to disk, Qdrant can handle larger datasets that exceed the capacity of RAM, making the system more scalable and capable of storing more data without being constrained by memory limitations. -*To learn more about this, read the [Indexing documentation](/documentation/concepts/index/).* +*To learn more about this, read the [Indexing documentation](/documentation/concepts/indexing/).* ### UUID Datatype for the Payload Index From 293e912096d15cc57292be313112669aa62867c5 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Mon, 12 Aug 2024 19:02:57 -0700 Subject: [PATCH 03/19] fix title --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index e506b0952..fe6a414f2 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -1,5 +1,5 @@ --- -title: "Qdrant 1.11 - The Vector Stronghold: Optimizing Data Structures." +title: "Qdrant 1.11 - The Vector Stronghold: Optimizing Data Structures for Scale and Efficiency" draft: false short_description: "On-Disk Payload Index. UUID Payload Support. Tenant Defragmentation." description: "Enhanced payload flexibility with on-disk indexing, UUID support, and tenant-based defragmentation." From 61bd7ffc725968a01a86960dfd37f1f48e51faf2 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Mon, 12 Aug 2024 19:18:45 -0700 Subject: [PATCH 04/19] add snippets --- qdrant-landing/content/blog/qdrant-1.11.x.md | 303 ++++++++++++++++++- 1 file changed, 302 insertions(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index fe6a414f2..3554a6fc7 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -34,7 +34,7 @@ New Web UI Tools:
### Quick Recap: Multitenant Workloads -Before we dive into the specifics of our optimizations, let's first go over Multitenancy. This is one of our most significant features, [best used for at scaling and data isolation](https://qdrant.tech/articles/multitenancy/). +Before we dive into the specifics of our optimizations, let's first go over Multitenancy. This is one of our most significant features, [best used for scaling and data isolation](https://qdrant.tech/articles/multitenancy/). If you’re using Qdrant to manage data for multiple users, regions, or workspaces (tenants), we suggest setting up a [multitenant environment](/documentation/guides/multiple-partitions/). This approach keeps all tenant data in a single global collection, with points separated and isolated by their payload. @@ -63,6 +63,96 @@ PUT /collections/{collection_name}/index "is_tenant": true } } +``` + +```python +client.create_payload_index( + collection_name="{collection_name}", + field_name="workspace_2", + field_schema=models.KeywordIndexParams( + type="keywprd", + is_tenant=True, + ), +) +``` + +```typescript +client.createPayloadIndex("{collection_name}", { + field_name: "workspace_2", + field_schema: { + type: "keyword", + is_tenant: true, + }, +}); +``` + +```rust +use qdrant_client::qdrant::{ + CreateFieldIndexCollectionBuilder, + KeywordIndexParamsBuilder, + FieldType +}; +use qdrant_client::{Qdrant, QdrantError}; + +let client = Qdrant::from_url("http://localhost:6334").build()?; + +client.create_field_index( + CreateFieldIndexCollectionBuilder::new( + "{collection_name}", + "workspace_2", + FieldType::Keyword, + ).field_index_params( + KeywordIndexParamsBuilder::default() + .is_tenant(true) + ) + ).await?; +``` + +```java +import io.qdrant.client.QdrantClient; +import io.qdrant.client.QdrantGrpcClient; +import io.qdrant.client.grpc.Collections.PayloadIndexParams; +import io.qdrant.client.grpc.Collections.PayloadSchemaType; +import io.qdrant.client.grpc.Collections.KeywordIndexParams; + +QdrantClient client = + new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build()); + +client + .createPayloadIndexAsync( + "{collection_name}", + "workspace_2", + PayloadSchemaType.Keyword, + PayloadIndexParams.newBuilder() + .setKeywordIndexParams( + KeywordIndexParams.newBuilder() + .setIsTenant(true) + .build()) + .build(), + null, + null, + null) + .get(); +``` + +```csharp +using Qdrant.Client; + +var client = new QdrantClient("localhost", 6334); + +await client.CreatePayloadIndexAsync( + collectionName: "{collection_name}", + fieldName: "workspace_2", + schemaType: PayloadSchemaType.Keyword, + indexParams: new PayloadIndexParams + { + KeywordIndexParams = new KeywordIndexParams + { + IsTenant = true + } + } +); + ``` As a result, the storage structure will be organized in a way to co-locate vectors of the same tenant together. @@ -132,6 +222,18 @@ When searching over data, you can group results by specific payload field, which **Example:** If a large document is divided into several chunks, and you need to search or make recommendations on a per-document basis, you can group the results by the `document_id`. +```http +POST /collections/{collection_name}/points/query/groups +{ + # Same as in the regular query() API + "query": [0.01, 0.45, 0.67], + # Grouping parameters + group_by="document_id", # Path of the field to group by + limit=4, # Max amount of groups + group_size=2, # Max amount of points per group +} +``` + ```python from qdrant_client import QdrantClient, models @@ -146,6 +248,72 @@ client.query_point_groups( ) ``` +```typescript +import { QdrantClient } from "@qdrant/js-client-rest"; + +const client = new QdrantClient({ host: "localhost", port: 6333 }); + +client.query_point_groups("{collection_name}", { + query: [0.01, 0.45, 0.67], + group_by: "document_id", + limit: 4, + group_size: 2, +}); +``` + +```rust +use qdrant_client::Qdrant; +use qdrant_client::qdrant::{Query, QueryPointsBuilder}; + +let client = Qdrant::from_url("http://localhost:6334").build()?; + +client.query( + QueryPointGroupsBuilder::new("{collection_name}", "document_id") + .query(Query::from(vec![0.01, 0.45, 0.67])) + .limit(4) + .group_size(2) +).await?; +``` + +```java +import static io.qdrant.client.QueryFactory.nearest; + +import io.qdrant.client.QdrantClient; +import io.qdrant.client.QdrantGrpcClient; +import io.qdrant.client.grpc.Points.QueryPointGroups; + +QdrantClient client = + new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build()); + +client + .queryGroupsAsync( + QueryPointGroups.newBuilder() + .setCollectionName("{collection_name}") + .setGroupBy("document_id") + .setQuery(nearest(0.01f, 0.45f, 0.67f)) + .setLimit(4) + .setGroupSize(2) + .build()) + .get(); +``` + +```csharp +using Qdrant.Client; +using Qdrant.Client.Grpc; + +var client = new QdrantClient("localhost", 6334); + +await client.QueryGroupsAsync( + collectionName: "{collection_name}", + groupBy: "document_id", + query: new float[] { + 0.01f, 0.45f, 0.67f + }, + limit: 4, + groupSize: 2 +); +``` + This endpoint will retrieve the best N points for each document, assuming that the payload of the points contains the document ID. Sometimes, the best N points cannot be fulfilled due to lack of points or a big distance with respect to the query. In every case, the `group_size` is a best-effort parameter, similar to the limit parameter. *For more information on grouping capabilities refer to our [Hybrid Queries documentation](/documentation/concepts/hybrid-queries/).* @@ -205,6 +373,139 @@ POST /collections/{collection_name}/points/query } ``` +```python +from qdrant_client import QdrantClient, models + +client = QdrantClient(url="http://localhost:6333") + +client.query_points( + collection_name="{collection_name}", + prefetch=[ + models.Prefetch( + query=models.SparseVector(indices=[1, 42], values=[0.22, 0.8]), + using="sparse", + limit=20, + ), + models.Prefetch( + query=[0.01, 0.45, 0.67, ...], # <-- dense vector + using="dense", + limit=20, + ), + ], + query=models.FusionQuery(fusion=models.Fusion.DBSF), +) +``` + +```typescript +import { QdrantClient } from "@qdrant/js-client-rest"; + +const client = new QdrantClient({ host: "localhost", port: 6333 }); + +client.query("{collection_name}", { + prefetch: [ + { + query: { + values: [0.22, 0.8], + indices: [1, 42], + }, + using: 'sparse', + limit: 20, + }, + { + query: [0.01, 0.45, 0.67], + using: 'dense', + limit: 20, + }, + ], + query: { + fusion: 'dbsf', + }, +}); +``` + +```rust +use qdrant_client::Qdrant; +use qdrant_client::qdrant::{Fusion, PrefetchQueryBuilder, Query, QueryPointsBuilder}; + +let client = Qdrant::from_url("http://localhost:6334").build()?; + +client.query( + QueryPointsBuilder::new("{collection_name}") + .add_prefetch(PrefetchQueryBuilder::default() + .query(Query::new_nearest([(1, 0.22), (42, 0.8)].as_slice())) + .using("sparse") + .limit(20u64) + ) + .add_prefetch(PrefetchQueryBuilder::default() + .query(Query::new_nearest(vec![0.01, 0.45, 0.67])) + .using("dense") + .limit(20u64) + ) + .query(Query::new_fusion(Fusion::Dbsf)) +).await?; +``` + +```java +import static io.qdrant.client.QueryFactory.nearest; + +import java.util.List; + +import static io.qdrant.client.QueryFactory.fusion; + +import io.qdrant.client.QdrantClient; +import io.qdrant.client.QdrantGrpcClient; +import io.qdrant.client.grpc.Points.Fusion; +import io.qdrant.client.grpc.Points.PrefetchQuery; +import io.qdrant.client.grpc.Points.QueryPoints; + +QdrantClient client = new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build()); + +client.queryAsync( + QueryPoints.newBuilder() + .setCollectionName("{collection_name}") + .addPrefetch(PrefetchQuery.newBuilder() + .setQuery(nearest(List.of(0.22f, 0.8f), List.of(1, 42))) + .setUsing("sparse") + .setLimit(20) + .build()) + .addPrefetch(PrefetchQuery.newBuilder() + .setQuery(nearest(List.of(0.01f, 0.45f, 0.67f))) + .setUsing("dense") + .setLimit(20) + .build()) + .setQuery(fusion(Fusion.DBSF)) + .build()) + .get(); +``` + +```csharp +using Qdrant.Client; +using Qdrant.Client.Grpc; + +var client = new QdrantClient("localhost", 6334); + +await client.QueryAsync( + collectionName: "{collection_name}", + prefetch: new List < PrefetchQuery > { + new() { + Query = new(float, uint)[] { + (0.22f, 1), (0.8f, 42), + }, + Using = "sparse", + Limit = 20 + }, + new() { + Query = new float[] { + 0.01f, 0.45f, 0.67f + }, + Using = "dense", + Limit = 20 + } + }, + query: Fusion.Dbsf +); +``` + Note that `dbsf` is stateless and calculates the normalization limits only based on the results of each query, not on all the scores that it has seen. *To learn more, check out the [Hybrid Queries documentation](/documentation/concepts/hybrid-queries/).* From 948796beaf7653a460c9089b7a2968182b595fb6 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Mon, 12 Aug 2024 19:26:10 -0700 Subject: [PATCH 05/19] fix word --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 3554a6fc7..7f0b9f1ae 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -16,7 +16,7 @@ tags: - random sampling --- -[Qdrant 1.11.0 is out!](https://github.com/qdrant/qdrant/releases/tag/v1.11.0) This release largely focuses on improvements that imrpove memory usage and optimize segments. However, there are a few cool minor features, so let's look at the whole list: +[Qdrant 1.11.0 is out!](https://github.com/qdrant/qdrant/releases/tag/v1.11.0) This release largely focuses on features that improve memory usage and optimize segments. However, there are a few cool minor features, so let's look at the whole list: Optimized Data Structures:
**Defragmentation:** Storage for multitenant workloads is more optimized and scales better.
From 789325697d39e6a5ecbc4e2a6d676e076581e9f6 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Mon, 12 Aug 2024 19:35:44 -0700 Subject: [PATCH 06/19] example fix --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 7f0b9f1ae..1c0167466 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -225,9 +225,7 @@ When searching over data, you can group results by specific payload field, which ```http POST /collections/{collection_name}/points/query/groups { - # Same as in the regular query() API "query": [0.01, 0.45, 0.67], - # Grouping parameters group_by="document_id", # Path of the field to group by limit=4, # Max amount of groups group_size=2, # Max amount of points per group From 6a24ada7dd240dc5ff445e8511ad60c6608008e7 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Mon, 12 Aug 2024 19:45:07 -0700 Subject: [PATCH 07/19] add bit --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 1c0167466..5693a44eb 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -319,7 +319,7 @@ This endpoint will retrieve the best N points for each document, assuming that t ### Query API: Random Sampling -Our [Food Discovery Demo](https://food-discovery.qdrant.tech) always shows a random sample of foods from the larger dataset. Now you can set the randomization from a basic Query API endpoint. +Our [Food Discovery Demo](https://food-discovery.qdrant.tech) always shows a random sample of foods from the larger dataset. Now you can do the same and set the randomization from a basic Query API endpoint. When calling the Query API, you will be able to select a subset of data points from a larger dataset in a random manner. From b49f79a4da10ede83982fb4a5a75383893f9bdc6 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Mon, 12 Aug 2024 19:59:53 -0700 Subject: [PATCH 08/19] fix example --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 5693a44eb..6d0274571 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -347,7 +347,7 @@ In version 1.10, we added Reciprocal Rank Fusion (RRF) as a way of fusing result *DBSF normalizes the scores of the points in each query, using the mean +/- the 3rd standard deviation as limits, and then sums the scores of the same point across different queries.* -**Example:** To fuse `prefetch` results from sparse and dense queries, set the `"fusion": "dbsf"` +**Example:** To fuse `prefetch` results from sparse and dense queries, set `"fusion": "dbsf"` ```html POST /collections/{collection_name}/points/query { From 202ac35634f9a274d0c820c3e0f72fd7088fee53 Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Mon, 12 Aug 2024 20:00:51 -0700 Subject: [PATCH 09/19] fix note --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 6d0274571..5282a4a18 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -525,7 +525,7 @@ Deeper exploration is highly dependant on expanding context. This is something w **Try it:** Using the feature is pretty self-explanatory. Each collection's dataset can be explored from the **Graph** tab. As you see the images change, you can steer your search in the direction of specific characteristics that interest you. -*Search results will become more "distilled" and tailored to your preferences.* +> Search results will become more "distilled" and tailored to your preferences. From 93a8e8cc9ec57db151e9e990f4d6eff721067b72 Mon Sep 17 00:00:00 2001 From: Anush008 Date: Tue, 13 Aug 2024 09:05:27 +0530 Subject: [PATCH 10/19] docs: Added code snippets --- qdrant-landing/content/blog/qdrant-1.11.x.md | 219 ++++++++++++++++--- 1 file changed, 186 insertions(+), 33 deletions(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 5282a4a18..a27accf77 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -42,7 +42,7 @@ To avoid slow and unnecessary indexing, it’s better to create an index for eac *For more details on scaling best practices, read [How to Implement Multitenancy and Custom Sharding](https://qdrant.tech/articles/multitenancy/).* -### Defragmentation of Tenant Storage +### Defragmentation of Tenant Storage With version 1.11, Qdrant changes how vectors from the same tenant are stored on disk, placing them **closer together** for faster bulk reading and reduced scaling costs. This approach optimizes storage and retrieval operations for different tenants, leading to more efficient system performance and better resource utilization. @@ -70,7 +70,7 @@ client.create_payload_index( collection_name="{collection_name}", field_name="workspace_2", field_schema=models.KeywordIndexParams( - type="keywprd", + type="keyword", is_tenant=True, ), ) @@ -137,30 +137,32 @@ client ```csharp using Qdrant.Client; +using Qdrant.Client.Grpc; var client = new QdrantClient("localhost", 6334); await client.CreatePayloadIndexAsync( - collectionName: "{collection_name}", - fieldName: "workspace_2", - schemaType: PayloadSchemaType.Keyword, - indexParams: new PayloadIndexParams - { - KeywordIndexParams = new KeywordIndexParams - { - IsTenant = true - } - } + collectionName: "{collection_name}", + fieldName: "workspace_2", + schemaType: PayloadSchemaType.Keyword, + indexParams: new PayloadIndexParams + { + KeywordIndexParams = new KeywordIndexParams + { + IsTenant = true + } + } ); ``` -As a result, the storage structure will be organized in a way to co-locate vectors of the same tenant together. + +As a result, the storage structure will be organized in a way to co-locate vectors of the same tenant together. *To learn more about defragmentation, read the [Multitenancy documentation](/documentation/guides/multiple-partitions/).* ### On-Disk Support for the Payload Index -When managing billions of records across millions of tenants, keeping all data in RAM is inefficient, especially when only a small subset is frequently accessed. As of 1.11, you can offload "cold" data to disk and cache the “hot” data in RAM. +When managing billions of records across millions of tenants, keeping all data in RAM is inefficient, especially when only a small subset is frequently accessed. As of 1.11, you can offload "cold" data to disk and cache the “hot” data in RAM. *This feature can help you manage a high number of different payload indexes, which is beneficial if you are working with large varied datasets.* @@ -168,9 +170,9 @@ When managing billions of records across millions of tenants, keeping all data i ![on-disk-payload](/blog/qdrant-1.11.x/on-disk-payload.png) -**Example:** As you create an index for Workspace 2, set the `on_disk` parameter. +**Example:** As you create an index for Workspace 2, set the `on_disk` parameter. -```html +```http PUT /collections/{collection_name}/index { "field_name": "workspace_2", @@ -182,19 +184,116 @@ PUT /collections/{collection_name}/index } ``` +```python +client.create_payload_index( + collection_name="{collection_name}", + field_name="workspace_2", + field_schema=models.KeywordIndexParams( + type="keyword", + is_tenant=True, + on_disk=True, + ), +) +``` + +```typescript +client.createPayloadIndex("{collection_name}", { + field_name: "workspace_2", + field_schema: { + type: "keyword", + is_tenant: true, + on_disk: true + }, +}); +``` + +```rust +use qdrant_client::qdrant::{ + CreateFieldIndexCollectionBuilder, + KeywordIndexParamsBuilder, + FieldType +}; +use qdrant_client::{Qdrant, QdrantError}; + +let client = Qdrant::from_url("http://localhost:6334").build()?; + +client.create_field_index( + CreateFieldIndexCollectionBuilder::new( + "{collection_name}", + "workspace_2", + FieldType::Keyword, + ) + .field_index_params( + KeywordIndexParamsBuilder::default() + .is_tenant(true) + .on_disk(true), + ), +); +``` + +```java +import io.qdrant.client.QdrantClient; +import io.qdrant.client.QdrantGrpcClient; +import io.qdrant.client.grpc.Collections.PayloadIndexParams; +import io.qdrant.client.grpc.Collections.PayloadSchemaType; +import io.qdrant.client.grpc.Collections.KeywordIndexParams; + +QdrantClient client = + new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build()); + +client + .createPayloadIndexAsync( + "{collection_name}", + "workspace_2", + PayloadSchemaType.Keyword, + PayloadIndexParams.newBuilder() + .setKeywordIndexParams( + KeywordIndexParams.newBuilder() + .setIsTenant(true) + .setOnDisk(true) + .build()) + .build(), + null, + null, + null) + .get(); +``` + +```csharp +using Qdrant.Client; +using Qdrant.Client.Grpc; + +var client = new QdrantClient("localhost", 6334); + +await client.CreatePayloadIndexAsync( + collectionName: "{collection_name}", + fieldName: "workspace_2", + schemaType: PayloadSchemaType.Keyword, + indexParams: new PayloadIndexParams + { + KeywordIndexParams = new KeywordIndexParams + { + IsTenant = true, + OnDisk = true + } + } +); + +``` + By moving the index to disk, Qdrant can handle larger datasets that exceed the capacity of RAM, making the system more scalable and capable of storing more data without being constrained by memory limitations. *To learn more about this, read the [Indexing documentation](/documentation/concepts/indexing/).* ### UUID Datatype for the Payload Index -Many Qdrant users rely on UUIDs in their payloads, but storing these as strings comes with a substantial memory overhead—approximately 60 bytes per UUID. In reality, UUIDs only require 16 bytes of storage when stored as raw bytes. +Many Qdrant users rely on UUIDs in their payloads, but storing these as strings comes with a substantial memory overhead—approximately 60 bytes per UUID. In reality, UUIDs only require 16 bytes of storage when stored as raw bytes. To address this inefficiency, we’ve developed a new index type tailored specifically for UUIDs that stores them internally as bytes, **reducing memory usage by up to 3.75x.** **Example:** When adding two separate points, indicate their UUID in the payload. In this example, both data points belong to the same user (with the same UUID). -```html +```http PUT /collections/{collection_name}/points { "points": [ @@ -216,7 +315,7 @@ PUT /collections/{collection_name}/points *To learn more about this, read the [Payload documentation](/documentation/concepts/payload/).* -### Query API: Groups Endpoint +### Query API: Groups Endpoint When searching over data, you can group results by specific payload field, which is useful when you have multiple data points for the same item and you want to avoid redundant entries in the results. @@ -316,14 +415,13 @@ This endpoint will retrieve the best N points for each document, assuming that t *For more information on grouping capabilities refer to our [Hybrid Queries documentation](/documentation/concepts/hybrid-queries/).* - -### Query API: Random Sampling +### Query API: Random Sampling Our [Food Discovery Demo](https://food-discovery.qdrant.tech) always shows a random sample of foods from the larger dataset. Now you can do the same and set the randomization from a basic Query API endpoint. -When calling the Query API, you will be able to select a subset of data points from a larger dataset in a random manner. +When calling the Query API, you will be able to select a subset of data points from a larger dataset in a random manner. -*This technique is often used to reduce the computational load, improve query response times, or provide a representative sample of the data for various analytical purposes.* +*This technique is often used to reduce the computational load, improve query response times, or provide a representative sample of the data for various analytical purposes.* **Example:** When querying the collection, you can configure it to retrieve a random sample of data. @@ -338,17 +436,73 @@ sampled = client.query_points( query=models.SampleQuery(sample=models.Sample.Random) ) ``` + +```typescript +import { QdrantClient } from "@qdrant/js-client-rest"; + +const client = new QdrantClient({ host: "localhost", port: 6333 }); + +let sampled = client.query("{collection_name}", { + query: { sample: "random" }, +}); +``` + +```rust +use qdrant_client::Qdrant; +use qdrant_client::qdrant::{Query, QueryPointsBuilder, Sample}; + +let client = Qdrant::from_url("http://localhost:6334").build()?; + +let sampled = client + .query( + QueryPointsBuilder::new("{collection_name}").query(Query::new_sample(Sample::Random)), + ) + .await?; +``` + +```java +import static io.qdrant.client.QueryFactory.sample; + +import io.qdrant.client.QdrantClient; +import io.qdrant.client.QdrantGrpcClient; +import io.qdrant.client.grpc.Points.Sample; +import io.qdrant.client.grpc.Points.QueryPoints; + +QdrantClient client = + new QdrantClient(QdrantGrpcClient.newBuilder("localhost", 6334, false).build()); + +client + .queryAsync( + QueryPoints.newBuilder() + .setCollectionName("{collection_name}") + .setQuery(sample(Sample.Random)) + .build()) + .get(); +``` + +```csharp +using Qdrant.Client; +using Qdrant.Client.Grpc; + +var client = new QdrantClient("localhost", 6334); + +await client.QueryAsync( + collectionName: "{collection_name}", + query: Sample.Random +); +``` + *To learn more, check out the [Query API documentation](/documentation/concepts/hybrid-queries/).* ### Query API: Distribution-Based Score Fusion In version 1.10, we added Reciprocal Rank Fusion (RRF) as a way of fusing results from Hybrid Queries. Now we are adding Distribution-Based Score Fusion (DBSF). Michelangiolo Mazzeschi talks more about this fusion method in his latest [Medium article](https://medium.com/plain-simple-software/distribution-based-score-fusion-dbsf-a-new-approach-to-vector-search-ranking-f87c37488b18). -*DBSF normalizes the scores of the points in each query, using the mean +/- the 3rd standard deviation as limits, and then sums the scores of the same point across different queries.* - +*DBSF normalizes the scores of the points in each query, using the mean +/- the 3rd standard deviation as limits, and then sums the scores of the same point across different queries.* **Example:** To fuse `prefetch` results from sparse and dense queries, set `"fusion": "dbsf"` -```html + +```http POST /collections/{collection_name}/points/query { "prefetch": [ @@ -508,22 +662,21 @@ Note that `dbsf` is stateless and calculates the normalization limits only based *To learn more, check out the [Hybrid Queries documentation](/documentation/concepts/hybrid-queries/).* -## Web UI: Search Quality Tool +## Web UI: Search Quality Tool -We have updated the Qdrant Web UI with additional testing functionality. Now you can check the quality of your search requests in real time and measure it against exact search. +We have updated the Qdrant Web UI with additional testing functionality. Now you can check the quality of your search requests in real time and measure it against exact search. -**Try it:** In the Dashboard, go to collection settings and test the **Precision** from the Search Quality menu tab. +**Try it:** In the Dashboard, go to collection settings and test the **Precision** from the Search Quality menu tab. > The feature will conduct semantic search for each point and produce a report below. - ## Web UI: Graph Exploration Tool -Deeper exploration is highly dependant on expanding context. This is something we previously covered in the [Discovery Needs Context](/articles/discovery-search/) article earlier this year. Now, we have developed a UI feature to help you visualize how semantic search can be used for exploratory and recommendation purposes. +Deeper exploration is highly dependant on expanding context. This is something we previously covered in the [Discovery Needs Context](/articles/discovery-search/) article earlier this year. Now, we have developed a UI feature to help you visualize how semantic search can be used for exploratory and recommendation purposes. -**Try it:** Using the feature is pretty self-explanatory. Each collection's dataset can be explored from the **Graph** tab. As you see the images change, you can steer your search in the direction of specific characteristics that interest you. +**Try it:** Using the feature is pretty self-explanatory. Each collection's dataset can be explored from the **Graph** tab. As you see the images change, you can steer your search in the direction of specific characteristics that interest you. > Search results will become more "distilled" and tailored to your preferences. @@ -533,4 +686,4 @@ Deeper exploration is highly dependant on expanding context. This is something w If you’re new to Qdrant, now is the perfect time to start. Check out our [documentation](/documentation/) guides and see why Qdrant is the go-to solution for vector search. -We’re very happy to bring you this latest version of Qdrant, and we can’t wait to see what you build with it. As always, your feedback is invaluable—feel free to reach out with any questions or comments on our [community forum](https://qdrant.to/discord). \ No newline at end of file +We’re very happy to bring you this latest version of Qdrant, and we can’t wait to see what you build with it. As always, your feedback is invaluable—feel free to reach out with any questions or comments on our [community forum](https://qdrant.to/discord). From bfb2aaa3e664b67d5175a52159bf65198755fe19 Mon Sep 17 00:00:00 2001 From: Anush Date: Tue, 13 Aug 2024 09:22:13 +0530 Subject: [PATCH 11/19] docs: nit fix --- qdrant-landing/content/blog/qdrant-1.11.x.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index a27accf77..258f9c89a 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -21,15 +21,15 @@ tags: Optimized Data Structures:
**Defragmentation:** Storage for multitenant workloads is more optimized and scales better.
**On-Disk Payload:** You can now store less frequently used data on disk, rather than on RAM.
-**UUID Support:** Additional data type for payload can result in significant memory savings. +**UUID Support:** Additional data types for payload can result in significant memory savings. Improved Query API:
**GroupBy Endpoint:** Use this query method to group results by a certain payload field.
-**Random Sampling:** Select a subset of data points from a larger dataset in a random manner.
+**Random Sampling:** Select a subset of data points from a larger dataset randomly.
**Hybrid Search Fusion:** We are adding the Distribution-Based Score Fusion (DBSF) method.
New Web UI Tools:
-**Search Quality Tool:** Test the precision of your semantic search requests in real time.
+**Search Quality Tool:** Test the precision of your semantic search requests in real-time.
**Graph Exploration Tool:** Visualize vector search in context-based exploratory scenarios.
### Quick Recap: Multitenant Workloads @@ -44,7 +44,7 @@ To avoid slow and unnecessary indexing, it’s better to create an index for eac ### Defragmentation of Tenant Storage -With version 1.11, Qdrant changes how vectors from the same tenant are stored on disk, placing them **closer together** for faster bulk reading and reduced scaling costs. This approach optimizes storage and retrieval operations for different tenants, leading to more efficient system performance and better resource utilization. +With version 1.11, Qdrant changes how vectors from the same tenant are stored on disk, placing them **closer together** for faster bulk reading and reduced scaling costs. This approach optimizes storage and retrieval operations for different tenants, leading to more efficient system performance and resource utilization. > Defragmentation can significantly improve performance. In the coming weeks, we will share **benchmark data** to demonstrate performance gains. @@ -166,7 +166,7 @@ When managing billions of records across millions of tenants, keeping all data i *This feature can help you manage a high number of different payload indexes, which is beneficial if you are working with large varied datasets.* -**Figure 2:** By moving the Workspace 2 index to disk, the system can free up valuable memory resources for Workspaces 1,3 and 4, that are accessed more frequently. +**Figure 2:** By moving the Workspace 2 index to disk, the system can free up valuable memory resources for Workspaces 1, 3 and 4, which are accessed more frequently. ![on-disk-payload](/blog/qdrant-1.11.x/on-disk-payload.png) @@ -419,7 +419,7 @@ This endpoint will retrieve the best N points for each document, assuming that t Our [Food Discovery Demo](https://food-discovery.qdrant.tech) always shows a random sample of foods from the larger dataset. Now you can do the same and set the randomization from a basic Query API endpoint. -When calling the Query API, you will be able to select a subset of data points from a larger dataset in a random manner. +When calling the Query API, you will be able to select a subset of data points from a larger dataset randomly. *This technique is often used to reduce the computational load, improve query response times, or provide a representative sample of the data for various analytical purposes.* @@ -668,13 +668,13 @@ We have updated the Qdrant Web UI with additional testing functionality. Now you **Try it:** In the Dashboard, go to collection settings and test the **Precision** from the Search Quality menu tab. -> The feature will conduct semantic search for each point and produce a report below. +> The feature will conduct a semantic search for each point and produce a report below. ## Web UI: Graph Exploration Tool -Deeper exploration is highly dependant on expanding context. This is something we previously covered in the [Discovery Needs Context](/articles/discovery-search/) article earlier this year. Now, we have developed a UI feature to help you visualize how semantic search can be used for exploratory and recommendation purposes. +Deeper exploration is highly dependent on expanding context. This is something we previously covered in the [Discovery Needs Context](/articles/discovery-search/) article earlier this year. Now, we have developed a UI feature to help you visualize how semantic search can be used for exploratory and recommendation purposes. **Try it:** Using the feature is pretty self-explanatory. Each collection's dataset can be explored from the **Graph** tab. As you see the images change, you can steer your search in the direction of specific characteristics that interest you. From f5a9286633f12663ea0e30cc4bbeb1f3f952a74f Mon Sep 17 00:00:00 2001 From: timvisee Date: Tue, 13 Aug 2024 10:03:44 +0200 Subject: [PATCH 12/19] UUIDs are 36 bytes in string representation --- qdrant-landing/content/blog/qdrant-1.11.x.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 258f9c89a..b4001cdc5 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -287,9 +287,9 @@ By moving the index to disk, Qdrant can handle larger datasets that exceed the c ### UUID Datatype for the Payload Index -Many Qdrant users rely on UUIDs in their payloads, but storing these as strings comes with a substantial memory overhead—approximately 60 bytes per UUID. In reality, UUIDs only require 16 bytes of storage when stored as raw bytes. +Many Qdrant users rely on UUIDs in their payloads, but storing these as strings comes with a substantial memory overhead—approximately 36 bytes per UUID. In reality, UUIDs only require 16 bytes of storage when stored as raw bytes. -To address this inefficiency, we’ve developed a new index type tailored specifically for UUIDs that stores them internally as bytes, **reducing memory usage by up to 3.75x.** +To address this inefficiency, we’ve developed a new index type tailored specifically for UUIDs that stores them internally as bytes, **reducing memory usage by up to 2.25x.** **Example:** When adding two separate points, indicate their UUID in the payload. In this example, both data points belong to the same user (with the same UUID). From f58bba2a2fa7f8b2eb24ff0149fbbd1dbb25bc79 Mon Sep 17 00:00:00 2001 From: Anush Date: Tue, 13 Aug 2024 14:04:18 +0530 Subject: [PATCH 13/19] fix: queryGroups TS --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index b4001cdc5..139fe8f3d 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -350,7 +350,7 @@ import { QdrantClient } from "@qdrant/js-client-rest"; const client = new QdrantClient({ host: "localhost", port: 6333 }); -client.query_point_groups("{collection_name}", { +client.queryGroups("{collection_name}", { query: [0.01, 0.45, 0.67], group_by: "document_id", limit: 4, From 9979086b1895fa20fd2b40988224ae466b6f6dcb Mon Sep 17 00:00:00 2001 From: David Myriel Date: Tue, 13 Aug 2024 01:58:18 -0700 Subject: [PATCH 14/19] Update qdrant-landing/content/blog/qdrant-1.11.x.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Tim Visée --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 139fe8f3d..8911d7766 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -20,7 +20,7 @@ tags: Optimized Data Structures:
**Defragmentation:** Storage for multitenant workloads is more optimized and scales better.
-**On-Disk Payload:** You can now store less frequently used data on disk, rather than on RAM.
+**On-Disk Payload:** You can now store less frequently used data on disk, rather than in RAM.
**UUID Support:** Additional data types for payload can result in significant memory savings. Improved Query API:
From 46119e9b19d467fcde5a98749871d4dcee23b5ed Mon Sep 17 00:00:00 2001 From: David Myriel Date: Tue, 13 Aug 2024 01:58:41 -0700 Subject: [PATCH 15/19] Update qdrant-landing/content/blog/qdrant-1.11.x.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Tim Visée --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 8911d7766..94956b0a1 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -46,7 +46,7 @@ To avoid slow and unnecessary indexing, it’s better to create an index for eac With version 1.11, Qdrant changes how vectors from the same tenant are stored on disk, placing them **closer together** for faster bulk reading and reduced scaling costs. This approach optimizes storage and retrieval operations for different tenants, leading to more efficient system performance and resource utilization. -> Defragmentation can significantly improve performance. In the coming weeks, we will share **benchmark data** to demonstrate performance gains. +> Defragmentation improves performance. In the coming weeks, we will share **benchmark data** to demonstrate performance gains. **Figure 1:** Re-ordering by payload can significantly speed up access to hot and cold data. From d0c680b831038ee7425cbb8418204fa0b0a3869f Mon Sep 17 00:00:00 2001 From: David Myriel Date: Tue, 13 Aug 2024 01:59:32 -0700 Subject: [PATCH 16/19] Update qdrant-landing/content/blog/qdrant-1.11.x.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Tim Visée --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 94956b0a1..a1a986f33 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -156,7 +156,7 @@ await client.CreatePayloadIndexAsync( ``` -As a result, the storage structure will be organized in a way to co-locate vectors of the same tenant together. +As a result, the storage structure will be organized in a way to co-locate vectors of the same tenant together at the next optimization. *To learn more about defragmentation, read the [Multitenancy documentation](/documentation/guides/multiple-partitions/).* From b42dbbfd0c76384c846c404cff2810c7708f5417 Mon Sep 17 00:00:00 2001 From: David Myriel Date: Tue, 13 Aug 2024 01:59:59 -0700 Subject: [PATCH 17/19] Update qdrant-landing/content/blog/qdrant-1.11.x.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Tim Visée --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index a1a986f33..0f2e34b1b 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -162,7 +162,7 @@ As a result, the storage structure will be organized in a way to co-locate vecto ### On-Disk Support for the Payload Index -When managing billions of records across millions of tenants, keeping all data in RAM is inefficient, especially when only a small subset is frequently accessed. As of 1.11, you can offload "cold" data to disk and cache the “hot” data in RAM. +When managing billions of records across millions of tenants, keeping all data in RAM is inefficient. That is especially true when only a small subset is frequently accessed. As of 1.11, you can offload "cold" data to disk and cache the “hot” data in RAM. *This feature can help you manage a high number of different payload indexes, which is beneficial if you are working with large varied datasets.* From 89dc2e6bdb3b1e9385b684e498028c1412ad1c06 Mon Sep 17 00:00:00 2001 From: David Myriel Date: Tue, 13 Aug 2024 02:00:38 -0700 Subject: [PATCH 18/19] Update qdrant-landing/content/blog/qdrant-1.11.x.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Tim Visée --- qdrant-landing/content/blog/qdrant-1.11.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 0f2e34b1b..352b920b1 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -166,7 +166,7 @@ When managing billions of records across millions of tenants, keeping all data i *This feature can help you manage a high number of different payload indexes, which is beneficial if you are working with large varied datasets.* -**Figure 2:** By moving the Workspace 2 index to disk, the system can free up valuable memory resources for Workspaces 1, 3 and 4, which are accessed more frequently. +**Figure 2:** By moving the data from Workspace 2 to disk, the system can free up valuable memory resources for Workspaces 1, 3 and 4, which are accessed more frequently. ![on-disk-payload](/blog/qdrant-1.11.x/on-disk-payload.png) From 237fec4c34856f75c6d990b9c097d01a4118d5aa Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Tue, 13 Aug 2024 02:07:04 -0700 Subject: [PATCH 19/19] add last fixes --- qdrant-landing/content/blog/qdrant-1.11.x.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/qdrant-landing/content/blog/qdrant-1.11.x.md b/qdrant-landing/content/blog/qdrant-1.11.x.md index 352b920b1..534d2b982 100644 --- a/qdrant-landing/content/blog/qdrant-1.11.x.md +++ b/qdrant-landing/content/blog/qdrant-1.11.x.md @@ -20,8 +20,8 @@ tags: Optimized Data Structures:
**Defragmentation:** Storage for multitenant workloads is more optimized and scales better.
-**On-Disk Payload:** You can now store less frequently used data on disk, rather than in RAM.
-**UUID Support:** Additional data types for payload can result in significant memory savings. +**On-Disk Payload Index:** Store less frequently used data on disk, rather than in RAM.
+**UUID for Payload Index:** Additional data types for payload can result in big memory savings. Improved Query API:
**GroupBy Endpoint:** Use this query method to group results by a certain payload field.
@@ -46,8 +46,6 @@ To avoid slow and unnecessary indexing, it’s better to create an index for eac With version 1.11, Qdrant changes how vectors from the same tenant are stored on disk, placing them **closer together** for faster bulk reading and reduced scaling costs. This approach optimizes storage and retrieval operations for different tenants, leading to more efficient system performance and resource utilization. -> Defragmentation improves performance. In the coming weeks, we will share **benchmark data** to demonstrate performance gains. - **Figure 1:** Re-ordering by payload can significantly speed up access to hot and cold data. ![defragmentation](/blog/qdrant-1.11.x/defragmentation.png)