From 7e7ed72e75fee2bd5035f0e899a5531cee2255cd Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Wed, 17 Jul 2024 15:53:06 -0700 Subject: [PATCH] add more questions --- .../faq/database-optimization.md | 4 +- .../documentation/faq/qdrant-fundamentals.md | 75 +++++++++++++++---- 2 files changed, 62 insertions(+), 17 deletions(-) diff --git a/qdrant-landing/content/documentation/faq/database-optimization.md b/qdrant-landing/content/documentation/faq/database-optimization.md index ae1f05188..02b53e1d2 100644 --- a/qdrant-landing/content/documentation/faq/database-optimization.md +++ b/qdrant-landing/content/documentation/faq/database-optimization.md @@ -1,6 +1,6 @@ --- title: Database Optimization -weight: 3 +weight: 2 --- ## Database Optimization Strategies @@ -15,7 +15,7 @@ The primary source of memory usage is vector data. There are several ways to add The choice of the approach depends on your requirements. Read more about [configuring the optimal](../../tutorials/optimize/) use of Qdrant. -### How do you choose machine configuration? +### How do you choose the machine configuration? There are two main scenarios of Qdrant usage in terms of resource consumption: diff --git a/qdrant-landing/content/documentation/faq/qdrant-fundamentals.md b/qdrant-landing/content/documentation/faq/qdrant-fundamentals.md index 4cf01b072..18fd85049 100644 --- a/qdrant-landing/content/documentation/faq/qdrant-fundamentals.md +++ b/qdrant-landing/content/documentation/faq/qdrant-fundamentals.md @@ -1,18 +1,48 @@ --- -title: Fundamentals +title: Qdrant Fundamentals weight: 1 --- -## Qdrant Fundamentals +# Frequently Asked Questions: General Topics +|||||| +|-|-|-|-|-| +|[Vectors](/documentation/faq/qdrant-fundamentals/#vectors)|[Search](/documentation/faq/qdrant-fundamentals/#search)|[Collections](/documentation/faq/qdrant-fundamentals/#collections)|[Compatibility](/documentation/faq/qdrant-fundamentals/#compatibility)|[Cloud](/documentation/faq/qdrant-fundamentals/#cloud)| -### How many collections can I create? +## Vectors -As much as you want, but be aware that each collection requires additional resources. -It is _highly_ recommended not to create many small collections, as it will lead to significant resource consumption overhead. +### What is the maximum vector dimension supported by Qdrant? -We consider creating a collection for each user/dialog/document as an antipattern. +Qdrant supports up to 65,535 dimensions by default, but this can be configured to support higher dimensions. -Please read more about collections, isolation, and multiple users in our [Multitenancy](../../tutorials/multiple-partitions/) tutorial. +### What is the maximum size of vector metadata that can be stored? + +There is no inherent limitation on metadata size, but it should be [optimized for performance and resource usage](/documentation/guides/optimize/). Users can set upper limits in the configuration. + +### Can the same similarity search query yield different results on different machines? + +Yes, due to differences in hardware configurations and parallel processing, results may vary slightly. + +### What to do with documents with small chunks using a fixed chunk strategy? + +For documents with small chunks, consider merging chunks or using variable chunk sizes to optimize vector representation and search performance. + +### How do I choose the right vector embeddings for my use case? + +This depends on the nature of your data and the specific application. Consider factors like dimensionality, domain-specific models, and the performance characteristics of different embeddings. + +### How does Qdrant handle different vector embeddings from various providers in the same collection? + +Qdrant natively [supports multiple vectors per data point](/documentation/concepts/vectors/#multivectors), allowing different embeddings from various providers to coexist within the same collection. + +### Can I migrate my embeddings from another vector store to Qdrant? + +Yes, Qdrant supports migration of embeddings from other vector stores, facilitating easy transitions and adoption of Qdrant’s features. + +## Search + +### How does Qdrant handle real-time data updates and search? + +Qdrant supports live updates for vector data, with newly inserted, updated and deleted vectors available for immediate search. The system uses full-scan search on unindexed segments during background index updates. ### My search results contain vectors with null values. Why? @@ -51,6 +81,17 @@ What Qdrant doesn't plan to support: Of course, you can always combine Qdrant with any specialized tool you need, including full-text search engines. Read more about [our approach](../../../articles/hybrid-search/) to hybrid search. +## Collections + +### How many collections can I create? + +As many as you want, but be aware that each collection requires additional resources. +It is _highly_ recommended not to create many small collections, as it will lead to significant resource consumption overhead. + +We consider creating a collection for each user/dialog/document as an antipattern. + +Please read more about collections, isolation, and multiple users in our [Multitenancy](../../tutorials/multiple-partitions/) tutorial. + ### How do I upload a large number of vectors into a Qdrant collection? Read about our recommendations in the [bulk upload](../../tutorials/bulk-upload/) tutorial. @@ -59,14 +100,16 @@ Read about our recommendations in the [bulk upload](../../tutorials/bulk-upload/ No, Qdrant requires full precision vectors for operations like reindexing, rescoring, etc. -## Qdrant Cloud +## Compatibility -### Is it possible to scale down a Qdrant Cloud cluster? +### Is Qdrant compatible with CPUs or GPUs for vector computation? -In general, no. There's no way to scale down the underlying disk storage. -But in some cases, we might be able to help you with that through manual intervention, but it's not guaranteed. +Qdrant primarily relies on CPU acceleration for scalability and efficiency, with no current support for GPU acceleration. -## Versioning +### Do you guarantee compatibility across versions? + +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. ### Do you support downgrades? @@ -77,7 +120,9 @@ data is automatically migrated to the newer storage format. This migration is no We only guarantee compatibility if you update between consecutive versions. You would need to upgrade versions one at a time: `1.1 -> 1.2`, then `1.2 -> 1.3`, then `1.3 -> 1.4`. -### Do you guarantee compatibility across versions? +## Cloud -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. +### Is it possible to scale down a Qdrant Cloud cluster? + +In general, no. There's no way to scale down the underlying disk storage. +But in some cases, we might be able to help you with that through manual intervention, but it's not guaranteed.