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78 lines
3.4 KiB
Markdown
78 lines
3.4 KiB
Markdown
---
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title: Fundamentals
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weight: 1
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---
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## Qdrant Fundamentals
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### How many collections can I create?
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As much as you want, but be aware that each collection requires additional resources.
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It is _highly_ recommended not to create many small collections, as it will lead to significant resource consumption overhead.
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We consider creating a collection for each user/dialog/document as an antipattern.
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Please read more about collections, isolation, and multiple users in our [Multitenancy](../../tutorials/multiple-partitions/) tutorial.
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### My search results contain vectors with null values. Why?
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By default, Qdrant tries to minimize network traffic and doesn't return vectors in search results.
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But you can force Qdrant to do so by setting the `with_vector` parameter of the Search/Scroll to `true`.
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If you're still seeing `"vector": null` in your results, it might be that the vector you're passing is not in the correct format, or there's an issue with how you're calling the upsert method.
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### How can I search without a vector?
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You are likely looking for the [scroll](../../concepts/points/#scroll-points) method. It allows you to retrieve the records based on filters or even iterate over all the records in the collection.
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### Does Qdrant support a full-text search or a hybrid search?
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Qdrant is a vector search engine in the first place, and we only implement full-text support as long as it doesn't compromise the vector search use case.
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That includes both the interface and the performance.
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What Qdrant can do:
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- Search with full-text filters
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- Apply full-text filters to the vector search (i.e., perform vector search among the records with specific words or phrases)
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- Do prefix search and semantic [search-as-you-type](../../../articles/search-as-you-type/)
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What Qdrant plans to introduce in the future:
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- Support for sparse vectors, as used in [SPLADE](https://github.com/naver/splade) or similar models
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What Qdrant doesn't plan to support:
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- BM25 or other non-vector-based retrieval or ranking functions
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- Built-in ontologies or knowledge graphs
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- Query analyzers and other NLP tools
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Of course, you can always combine Qdrant with any specialized tool you need, including full-text search engines.
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Read more about [our approach](../../../articles/hybrid-search/) to hybrid search.
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### How do I upload a large number of vectors into a Qdrant collection?
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Read about our recommendations in the [bulk upload](../../tutorials/bulk-upload/) tutorial.
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### Can I only store quantized vectors and discard full precision vectors?
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No, Qdrant requires full precision vectors for operations like reindexing, rescoring, etc.
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## Qdrant Cloud
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### Is it possible to scale down a Qdrant Cloud cluster?
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In general, no. There's no way to scale down the underlying disk storage.
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But in some cases, we might be able to help you with that through manual intervention, but it's not guaranteed.
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## Versioning
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### How do I avoid issues when updating to the latest version?
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We only guarantee compatibility if you update between consequent 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`.
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### Do you guarantee compatibility across versions?
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In case your version is older, we guarantee only compatibility between two consecutive minor versions.
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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.
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