krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-28 23:48:31 +02:00
Code Issues Packages Projects Releases Wiki Activity

docs: Which embedding models does FastEmbed support, and how does it utilize memory?

Browse Source
This commit is contained in:
kartik-gupta-ij
2024-05-13 00:51:44 +05:30
parent 0b22e2008d
commit ee1f955416
2 changed files with 12 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/faq/database-optimization.md
+1
View File
@@ -59,4 +59,5 @@ Read more about [optimizing](../../guides/optimize/), and [quantization](../../g
### 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.
qdrant-landing/content/documentation/faq/qdrant-fundamentals.md
+11
View File
@@ -81,3 +81,14 @@ 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.