update faq list

2026-09-28 23:48:31 +02:00 · 2024-07-17 13:33:08 -07:00
parent 8d335e2aa0
commit bc7c69842f
2 changed files with 2 additions and 19 deletions
@@ -7,7 +7,7 @@ weight: 3

 ### How do I reduce memory usage?

-The primary source of memory usage vector data. There are several ways to address that:
+The primary source of memory usage is vector data. There are several ways to address that:

 - Configure [Quantization](../../guides/quantization/) to reduce the memory usage of vectors.
 - Configure on-disk vector storage
@@ -40,10 +40,4 @@ There are several possible reasons for that:

 - **Using filters without payload index** -- If you're performing a search with a filter but you don't have a payload index, Qdrant will have to load whole payload data from disk to check the filtering condition. Ensure you have adequately configured [payload indexes](../../concepts/indexing/#payload-index).
 - **Usage of on-disk vector storage with slow disks** -- If you're using on-disk vector storage, ensure you have fast enough disks. We recommend using local SSDs with at least 50k IOPS. Read more about the influence of the disk speed on the search latency in the article about [Memory Consumption](../../../articles/memory-consumption/).
- **Large limit or non-optimal query parameters** -- A large limit or offset might lead to significant performance degradation. Please pay close attention to the query/collection parameters that significantly diverge from the defaults. They might be the reason for the performance issues.
-
-### How can I enhance search performance when applying filters to their queries?
-
-To enhance the search performance with the filters in qdrant, it's important to optimize the indexing strategies. Users can get a combination of vector and traditional indexes from qdrant, where the vector indexes reduce the time taken for the vector search and the payload indexes quicken the pace of filtering. Users need to strategically mark the fields as indexable as well as prioritize the fields that appear frequently in the filtering conditions in order to efficiently utilize the memory resources. Through thorough cosideration of the memory constraints as well as careful index configuration, users can effectively enhance search performance with filters in qdrant.
-
-Read more about [indexing](../../concepts/indexing/) in Qdrant.
+- **Large limit or non-optimal query parameters** -- A large limit or offset might lead to significant performance degradation. Please pay close attention to the query/collection parameters that significantly diverge from the defaults. They might be the reason for the performance issues.
@@ -81,14 +81,3 @@ We only guarantee compatibility if you update between consecutive versions. You

 In case your version is older, we only guarantee compatibility between two consecutive minor versions. This also applies to client versions. Ensure your client version is never more than one minor version away from your cluster version.
 While we will assist with break/fix troubleshooting of issues and errors specific to our products, Qdrant is not accountable for reviewing, writing (or rewriting), or debugging custom code.
-
-### Which embedding models does FastEmbed support, and how does it utilize memory?
-
-You can find the list of supported models [here](https://qdrant.github.io/fastembed/examples/Supported_Models/).
-
-Memory usage in fastEmbed depends on several factors:
-1. **Size of the Text**: Everything, including the text and its vectors, is loaded into memory. As vectors are computed, RAM consumption increases accordingly.
-2. **Data Parallelism**: fastEmbed utilizes Python's multiprocessing to split large lists of strings into smaller ones and process them in parallel. While this enhances processing speed, it can also lead to higher RAM consumption.
-3. **Model Used**: Memory usage varies depending on the model used to embed your data.
-
-For optimal performance and memory management, consider these factors when using fastEmbed.