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Refactor query structure in qdrant-1.16.x.md
Updated the query structure to use a predefined QUERY object for consistency and clarity.
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@@ -162,41 +162,34 @@ The `text_any` condition matches text fields that contain any of the query terms
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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.
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```json
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batch [
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```python
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QUERY = {
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"text": "best smartphone ever",
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"model": "sentence-transformers/all-MiniLM-L6-v2"
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}
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batch_query = [
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{
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"query": {
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"text": "best smartphone ever",
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"model": "sentence-transformers/all-MiniLM-L6-v2"
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},
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"query": QUERY,
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"filter": {
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"must": {
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"key": "description",
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"match": {
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"text": "5G 5000mAh OLED"
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}
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"match": { "text": "5G 5000mAh OLED" }
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}
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}
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},
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{
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"query": {
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"text": "best smartphone ever",
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"model": "sentence-transformers/all-MiniLM-L6-v2"
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},
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{ # fallback to at least one token
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"query": QUERY,
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"filter": {
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"must": {
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"key": "description",
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"match": {
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"text_any": "5G 5000mAh OLED"
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}
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"match": { "text_any": "5G 5000mAh OLED" }
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}
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}
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},
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{
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"query": {
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"text": "best smartphone ever",
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"model": "sentence-transformers/all-MiniLM-L6-v2"
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}
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{ # fallback to just similarity
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"query": QUERY,
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"filter": null
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}
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]
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```
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@@ -273,4 +266,4 @@ We recommend upgrading versions one by one, for example, 1.14->1.15->1.16.
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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).
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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).
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