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p
Enhanced Autocomplete: Further refining the autocomplete feature to support key-value suggestions in JSON structures could greatly enhance the user experience.
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[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Areas for Improvement and Future Enhancements" ]
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Error Detection in Console: Integrating the console’s error checker with OpenAPI could enhance its accuracy in identifying errors and offering precise suggestions for improvement.
[ "articles", "articles/web-ui-gsoc" ]
1,901
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Error Detection in Console: Integrating the console’s error checker with OpenAPI could enhance its accuracy in identifying errors and offering precise suggestions for improvement.
[ "articles", "articles/web-ui-gsoc" ]
1,902
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Areas for Improvement and Future Enhancements" ]
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol > li:nth-of-type(3)
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Expanded Vector Visualization: Exploring additional visualization methods and optimizing their performance could elevate the utility of the vector visualization route.
[ "articles", "articles/web-ui-gsoc" ]
1,903
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Areas for Improvement and Future Enhancements" ]
/articles/web-ui-gsoc/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol > li:nth-of-type(3) > p
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Expanded Vector Visualization: Exploring additional visualization methods and optimizing their performance could elevate the utility of the vector visualization route.
[ "articles", "articles/web-ui-gsoc" ]
1,904
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant" ]
/articles/web-ui-gsoc/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(6)
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h2
Conclusion
[ "articles", "articles/web-ui-gsoc" ]
1,905
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Conclusion" ]
/articles/web-ui-gsoc/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29)
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p
Participating in the Google Summer of Code 2023 and working on the “Web UI for Visualization and Exploration” project has been an immensely rewarding experience.
[ "articles", "articles/web-ui-gsoc" ]
1,906
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Conclusion" ]
/articles/web-ui-gsoc/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29)
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p
I am grateful for the opportunity to contribute to Qdrant and develop a user-friendly interface for vector data exploration.
[ "articles", "articles/web-ui-gsoc" ]
1,907
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Conclusion" ]
/articles/web-ui-gsoc/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(30)
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p
I want to express my gratitude to my mentors and the entire Qdrant community for their support and guidance throughout this journey.
[ "articles", "articles/web-ui-gsoc" ]
1,908
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Conclusion" ]
/articles/web-ui-gsoc/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(30)
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p
This experience has not only improved my coding skills but also instilled a deeper passion for web development and data analysis.
[ "articles", "articles/web-ui-gsoc" ]
1,909
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Conclusion" ]
/articles/web-ui-gsoc/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(31)
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p
As my coding journey continues beyond this project, I look forward to applying the knowledge and experience gained here to future endeavours.
[ "articles", "articles/web-ui-gsoc" ]
1,910
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Conclusion" ]
/articles/web-ui-gsoc/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(31)
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p
I am excited to see how Qdrant evolves with the newly developed web UI and how it positively impacts users worldwide.
[ "articles", "articles/web-ui-gsoc" ]
1,911
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Conclusion" ]
/articles/web-ui-gsoc/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(32)
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p
Thank you for joining me on this coding adventure, and I hope to share more exciting projects in the future!
[ "articles", "articles/web-ui-gsoc" ]
1,912
[ "Google Summer of Code 2023 - Web UI for Visualization and Exploration - Qdrant", "Conclusion" ]
/articles/web-ui-gsoc/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(32)
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p
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li
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[ "articles", "articles/search-as-you-type" ]
1,915
[ "Semantic Search As You Type - Qdrant" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(3)
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1,917
[ "Semantic Search As You Type - Qdrant" ]
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul > li:nth-of-type(5)
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li
Semantic Search As You Type
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1,918
[ "Semantic Search As You Type - Qdrant" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h1
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h1
Semantic Search As You Type
[ "articles", "articles/search-as-you-type" ]
1,919
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
Qdrant is one of the fastest vector search engines out there, so while looking for a demo to show off, we came upon the idea to do a search-as-you-type box with a fully semantic search backend.
[ "articles", "articles/search-as-you-type" ]
1,920
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
Now we already have a semantic/keyword hybrid search on our website.
[ "articles", "articles/search-as-you-type" ]
1,921
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
But that one is written in Python, which incurs some overhead for the interpreter.
[ "articles", "articles/search-as-you-type" ]
1,922
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
Naturally, I wanted to see how fast I could go using Rust.
[ "articles", "articles/search-as-you-type" ]
1,923
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
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p
Since Qdrant doesn’t embed by itself, I had to decide on an embedding model.
[ "articles", "articles/search-as-you-type" ]
1,924
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
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p
The prior version used the SentenceTransformers package, which in turn employs Bert-based All-MiniLM-L6-V2 model.
[ "articles", "articles/search-as-you-type" ]
1,925
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
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p
This model is battle-tested and delivers fair results at speed, so not experimenting on this front I took an ONNX version and ran that within the service.
[ "articles", "articles/search-as-you-type" ]
1,926
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
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p
The workflow looks like this:
[ "articles", "articles/search-as-you-type" ]
1,927
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
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p
This will, after tokenizing and embedding send a /collections/site/points/search POST request to Qdrant, sending the following JSON:
[ "articles", "articles/search-as-you-type" ]
1,928
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6)
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p
Even with avoiding a network round-trip, the embedding still takes some time.
[ "articles", "articles/search-as-you-type" ]
1,929
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6)
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p
As always in optimization, if you cannot do the work faster, a good solution is to avoid work altogether (please don’t tell my employer).
[ "articles", "articles/search-as-you-type" ]
1,930
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6)
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p
This can be done by pre-computing common prefixes and calculating embeddings for them, then storing them in a prefix_cache collection.
[ "articles", "articles/search-as-you-type" ]
1,931
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6)
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p
Now the recommend API method can find the best matches without doing any embedding.
[ "articles", "articles/search-as-you-type" ]
1,932
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6)
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p
For now, I use short (up to and including 5 letters) prefixes, but I can also parse the logs to get the most common search terms and add them to the cache later.
[ "articles", "articles/search-as-you-type" ]
1,933
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8)
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p
Making that work requires setting up the prefix_cache collection with points that have the prefix as their point_id and the embedding as their vector, which lets us do the lookup with no search or index.
[ "articles", "articles/search-as-you-type" ]
1,934
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8)
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p
The prefix_to_id function currently uses the u64 variant of PointId, which can hold eight bytes, enough for this use.
[ "articles", "articles/search-as-you-type" ]
1,935
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8)
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p
If the need arises, one could instead encode the names as UUID, hashing the input.
[ "articles", "articles/search-as-you-type" ]
1,936
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8)
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p
Since I know all our prefixes are within 8 bytes, I decided against this for now.
[ "articles", "articles/search-as-you-type" ]
1,937
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
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p
The recommend endpoint works roughly the same as search_points, but instead of searching for a vector, Qdrant searches for one or more points (you can also give negative example points the search engine will try to avoid in the results).
[ "articles", "articles/search-as-you-type" ]
1,938
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
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p
It was built to help drive recommendation engines, saving the round-trip of sending the current point’s vector back to Qdrant to find more similar ones.
[ "articles", "articles/search-as-you-type" ]
1,939
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
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p
However Qdrant goes a bit further by allowing us to select a different collection to lookup the points, which allows us to keep our prefix_cache collection separate from the site data.
[ "articles", "articles/search-as-you-type" ]
1,940
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
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p
So in our case, Qdrant first looks up the point from the prefix_cache, takes its vector and searches for that in the site collection, using the precomputed embeddings from the cache.
[ "articles", "articles/search-as-you-type" ]
1,941
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
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p
The API endpoint expects a POST of the following JSON to /collections/site/points/recommend:
[ "articles", "articles/search-as-you-type" ]
1,942
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10)
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p
Now I have, in the best Rust tradition, a blazingly fast semantic search.
[ "articles", "articles/search-as-you-type" ]
1,943
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
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p
To demo it, I used our Qdrant documentation website’s page search, replacing our previous Python implementation.
[ "articles", "articles/search-as-you-type" ]
1,944
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
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p
So in order to not just spew empty words, here is a benchmark, showing different queries that exercise different code paths.
[ "articles", "articles/search-as-you-type" ]
1,945
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
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p
Since the operations themselves are far faster than the network whose fickle nature would have swamped most measurable differences, I benchmarked both the Python and Rust services locally.
[ "articles", "articles/search-as-you-type" ]
1,946
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
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p
I’m measuring both versions on the same AMD Ryzen 9 5900HX with 16GB RAM running Linux.
[ "articles", "articles/search-as-you-type" ]
1,947
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
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p
The table shows the average time and error bound in milliseconds.
[ "articles", "articles/search-as-you-type" ]
1,948
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
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p
I only measured up to a thousand concurrent requests.
[ "articles", "articles/search-as-you-type" ]
1,949
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
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p
None of the services showed any slowdown with more requests in that range.
[ "articles", "articles/search-as-you-type" ]
1,950
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
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p
I do not expect our service to become DDOS’d, so I didn’t benchmark with more load.
[ "articles", "articles/search-as-you-type" ]
1,951
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
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p
Without further ado, here are the results:
[ "articles", "articles/search-as-you-type" ]
1,952
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
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p
The Rust version consistently outperforms the Python version and offers a semantic search even on few-character queries.
[ "articles", "articles/search-as-you-type" ]
1,953
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
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p
If the prefix cache is hit (as in the short query length), the semantic search can even get more than ten times faster than the Python version.
[ "articles", "articles/search-as-you-type" ]
1,954
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
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p
The general speed-up is due to both the relatively lower overhead of Rust + Actix Web compared to Python + FastAPI (even if that already performs admirably), as well as using ONNX Runtime instead of SentenceTransformers for the embedding.
[ "articles", "articles/search-as-you-type" ]
1,955
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
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p
The prefix cache gives the Rust version a real boost by doing a semantic search without doing any embedding work.
[ "articles", "articles/search-as-you-type" ]
1,956
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15)
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p
As an aside, while the millisecond differences shown here may mean relatively little for our users, whose latency will be dominated by the network in between, when typing, every millisecond more or less can make a difference in user perception.
[ "articles", "articles/search-as-you-type" ]
1,957
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15)
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p
Also search-as-you-type generates between three and five times as much load as a plain search, so the service will experience more traffic.
[ "articles", "articles/search-as-you-type" ]
1,958
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15)
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p
Less time per request means being able to handle more of them.
[ "articles", "articles/search-as-you-type" ]
1,959
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16)
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p
Mission accomplished!
[ "articles", "articles/search-as-you-type" ]
1,960
[ "Semantic Search As You Type - Qdrant", "Semantic Search As You Type" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16)
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p
But wait, there’s more!
[ "articles", "articles/search-as-you-type" ]
1,961
[ "Semantic Search As You Type - Qdrant" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3
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h3
Prioritizing Exact Matches and Headings
[ "articles", "articles/search-as-you-type" ]
1,962
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17)
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p
To improve on the quality of the results, Qdrant can do multiple searches in parallel, and then the service puts the results in sequence, taking the first best matches.
[ "articles", "articles/search-as-you-type" ]
1,963
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17)
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p
The extended code searches:
[ "articles", "articles/search-as-you-type" ]
1,964
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol > li:nth-of-type(1)
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol > li:nth-of-type(2)
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Text matches in body (paragraphs or lists)
[ "articles", "articles/search-as-you-type" ]
1,966
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol > li:nth-of-type(3)
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li
Semantic matches in titles
[ "articles", "articles/search-as-you-type" ]
1,967
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol > li:nth-of-type(4)
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li
Any Semantic matches
[ "articles", "articles/search-as-you-type" ]
1,968
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
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p
Those are put together by taking them in the above order, deduplicating as necessary.
[ "articles", "articles/search-as-you-type" ]
1,969
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20)
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p
Instead of sending a search or recommend request, one can also send a search/batch or recommend/batch request, respectively.
[ "articles", "articles/search-as-you-type" ]
1,970
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20)
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p
Each of those contain a "searches" property with any number of search/recommend JSON requests:
[ "articles", "articles/search-as-you-type" ]
1,971
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
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p
As the queries are done in a batch request, there isn’t any additional network overhead and only very modest computation overhead, yet the results will be better in many cases.
[ "articles", "articles/search-as-you-type" ]
1,972
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
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p
The only additional complexity is to flatten the result lists and take the first 5 results, deduplicating by point ID.
[ "articles", "articles/search-as-you-type" ]
1,973
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
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p
Now there is one final problem: The query may be short enough to take the recommend code path, but still not be in the prefix cache.
[ "articles", "articles/search-as-you-type" ]
1,974
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
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p
In that case, doing the search sequentially would mean two round-trips between the service and the Qdrant instance.
[ "articles", "articles/search-as-you-type" ]
1,975
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
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p
The solution is to concurrently start both requests and take the first successful non-empty result.
[ "articles", "articles/search-as-you-type" ]
1,976
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
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p
While this means more load for the Qdrant vector search engine, this is not the limiting factor.
[ "articles", "articles/search-as-you-type" ]
1,977
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
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p
The relevant data is already in cache in many cases, so the overhead stays within acceptable bounds, and the maximum latency in case of prefix cache misses is measurably reduced.
[ "articles", "articles/search-as-you-type" ]
1,978
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25)
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p
The code is available on the Qdrant github
[ "articles", "articles/search-as-you-type" ]
1,979
[ "Semantic Search As You Type - Qdrant", "Prioritizing Exact Matches and Headings" ]
/articles/search-as-you-type/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(26)
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p
To sum up: Rust is fast, recommend lets us use precomputed embeddings, batch requests are awesome and one can do a semantic search in mere milliseconds.
[ "articles", "articles/search-as-you-type" ]
1,980
[ "Vector Similarity beyond Search - Qdrant" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(1) > li:nth-of-type(1)
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(1) > li:nth-of-type(3)
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Articles
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1,983
[ "Vector Similarity beyond Search - Qdrant" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(1) > li:nth-of-type(2)
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[ "articles", "articles/vector-similarity-beyond-search" ]
1,984
[ "Vector Similarity beyond Search - Qdrant" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(1) > li:nth-of-type(5)
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li
Vector Similarity beyond Search
[ "articles", "articles/vector-similarity-beyond-search" ]
1,985
[ "Vector Similarity beyond Search - Qdrant" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h1
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h1
Vector Similarity beyond Search
[ "articles", "articles/vector-similarity-beyond-search" ]
1,986
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(1)
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p
When making use of unstructured data, there are traditional go-to solutions that are well-known for developers:
[ "articles", "articles/vector-similarity-beyond-search" ]
1,987
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1)
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1,988
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(2)
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li
Vector search when you need to find documents that are semantically similar to a given query.
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1,989
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
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p
Sometimes people mix those two approaches, so it might look like the vector similarity is just an extension of full-text search.
[ "articles", "articles/vector-similarity-beyond-search" ]
1,990
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
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However, in this article, we will explore some promising new techniques that can be used to expand the use-case of unstructured data and demonstrate that vector similarity creates its own stack of data exploration tools.
[ "articles", "articles/vector-similarity-beyond-search" ]
1,991
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > figure:nth-of-type(1) > figcaption > p
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Full-text search and Vector Similarity Functionality overlap
[ "articles", "articles/vector-similarity-beyond-search" ]
1,992
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
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p
While there is an intersection in the functionality of these two approaches, there is also a vast area of functions that is unique to each of them.
[ "articles", "articles/vector-similarity-beyond-search" ]
1,993
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
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p
For example, the exact phrase matching and counting of results are native to full-text search, while vector similarity support for this type of operation is limited.
[ "articles", "articles/vector-similarity-beyond-search" ]
1,994
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
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p
On the other hand, vector similarity easily allows cross-modal retrieval of images by text or vice-versa, which is impossible with full-text search.
[ "articles", "articles/vector-similarity-beyond-search" ]
1,995
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4)
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p
This mismatch in expectations might sometimes lead to confusion.
[ "articles", "articles/vector-similarity-beyond-search" ]
1,996
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4)
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p
Attempting to use a vector similarity as a full-text search can result in a range of frustrations, from slow response times to poor search results, to limited functionality.
[ "articles", "articles/vector-similarity-beyond-search" ]
1,997
[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(4)
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p
As an outcome, they are getting only a fraction of the benefits of vector similarity.
[ "articles", "articles/vector-similarity-beyond-search" ]
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[ "Vector Similarity beyond Search - Qdrant", "Vector Similarity beyond Search" ]
/articles/vector-similarity-beyond-search/
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
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p
Below we will explore why vector similarity stack deserves new interfaces and design patterns that will unlock the full potential of this technology, which can still be used in conjunction with full-text search.
[ "articles", "articles/vector-similarity-beyond-search" ]
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