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Refactor query structure in qdrant-1.16.x.md

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Updated the query structure to use a predefined QUERY object for consistency and clarity.
This commit is contained in:
Andrey Vasnetsov
2025-11-18 13:12:07 +01:00
committed by GitHub
parent b63e7df933
commit 1bd937aa5b
1 changed files with 16 additions and 23 deletions
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qdrant-landing/content/blog/qdrant-1.16.x.md
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@@ -162,41 +162,34 @@ The `text_any` condition matches text fields that contain any of the query terms
A good example of using the `text_any` condition is in e-commerce applications, where users often search for products using multiple keywords. By combining a vector query with a series of increasingly lenient full-text filters, you can ensure that users receive relevant results even if their initial search terms are too restrictive. A good example of using the `text_any` condition is in e-commerce applications, where users often search for products using multiple keywords. By combining a vector query with a series of increasingly lenient full-text filters, you can ensure that users receive relevant results even if their initial search terms are too restrictive.
```json ```python
batch [ QUERY = {
"text": "best smartphone ever",
"model": "sentence-transformers/all-MiniLM-L6-v2"
}
batch_query = [
{ {
"query": { "query": QUERY,
"text": "best smartphone ever",
"model": "sentence-transformers/all-MiniLM-L6-v2"
},
"filter": { "filter": {
"must": { "must": {
"key": "description", "key": "description",
"match": { "match": { "text": "5G 5000mAh OLED" }
"text": "5G 5000mAh OLED"
}
} }
} }
}, },
{ { # fallback to at least one token
"query": { "query": QUERY,
"text": "best smartphone ever",
"model": "sentence-transformers/all-MiniLM-L6-v2"
},
"filter": { "filter": {
"must": { "must": {
"key": "description", "key": "description",
"match": { "match": { "text_any": "5G 5000mAh OLED" }
"text_any": "5G 5000mAh OLED"
}
} }
} }
}, },
{ { # fallback to just similarity
"query": { "query": QUERY,
"text": "best smartphone ever", "filter": null
"model": "sentence-transformers/all-MiniLM-L6-v2"
}
} }
] ]
``` ```
@@ -273,4 +266,4 @@ We recommend upgrading versions one by one, for example, 1.14->1.15->1.16.
![Engage](/blog/qdrant-1.16.x/section-9.png) ![Engage](/blog/qdrant-1.16.x/section-9.png)
We would love to hear your thoughts on this release. If you have any questions or feedback, join our [Discord](https://discord.gg/qdrant) or create an issue on [GitHub](https://github.com/qdrant/qdrant/issues). We would love to hear your thoughts on this release. If you have any questions or feedback, join our [Discord](https://discord.gg/qdrant) or create an issue on [GitHub](https://github.com/qdrant/qdrant/issues).