From bfb2aaa3e664b67d5175a52159bf65198755fe19 Mon Sep 17 00:00:00 2001 From: Anush Date: Tue, 13 Aug 2024 09:22:13 +0530 Subject: [PATCH] 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.