101f5a20d2
3757: Adjust the cost of edges in the `position` ranking rule by bucketing positions more aggressively r=loiclec a=loiclec This PR significantly improves the performance of the `position` ranking rule when: 1. a query contains many words 2. the `position` ranking rule needs to be called many times 3. the score of the documents according to `position` is high These conditions greatly increase: 1. the number of edge traversals that are needed to find a valid path from the `start` node to the `end` node 2. the number of edges that need to be deleted from the graph, and therefore the number of times that we need to recompute all the possible costs from START to END As a result, a majority of the search time is spent in `visit_condition`, `visit_node`, and `update_all_costs_before_node`. This is frustrating because it often happens when the "universe" given to the rule consists of only a handful of document ids. By limiting the number of possible edges between two nodes from `20` to `10`, we: 1. reduce the number of possible costs from START to END 2. reduce the number of edges that will be deleted 3. make it faster to update the costs after deleting an edge 4. reduce the number of buckets that need to be computed In terms of relevancy, I don't think we lose or gain much. We still prefer terms that are in a lower positions, with decreasing precision as we go further. The previous choice of bucketing wasn't chosen in a principled way, and neither is this one. They both "feel" right to me. Co-authored-by: Loïc Lecrenier <loic.lecrenier@me.com> Co-authored-by: meili-bors[bot] <89034592+meili-bors[bot]@users.noreply.github.com> |
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Website | Roadmap | Blog | Documentation | FAQ | Discord
⚡ A lightning-fast search engine that fits effortlessly into your apps, websites, and workflow 🔍
Meilisearch helps you shape a delightful search experience in a snap, offering features that work out-of-the-box to speed up your workflow.
🔥 Try it! 🔥
✨ Features
- Search-as-you-type: find search results in less than 50 milliseconds
- Typo tolerance: get relevant matches even when queries contain typos and misspellings
- Filtering and faceted search: enhance your user's search experience with custom filters and build a faceted search interface in a few lines of code
- Sorting: sort results based on price, date, or pretty much anything else your users need
- Synonym support: configure synonyms to include more relevant content in your search results
- Geosearch: filter and sort documents based on geographic data
- Extensive language support: search datasets in any language, with optimized support for Chinese, Japanese, Hebrew, and languages using the Latin alphabet
- Security management: control which users can access what data with API keys that allow fine-grained permissions handling
- Multi-Tenancy: personalize search results for any number of application tenants
- Highly Customizable: customize Meilisearch to your specific needs or use our out-of-the-box and hassle-free presets
- RESTful API: integrate Meilisearch in your technical stack with our plugins and SDKs
- Easy to install, deploy, and maintain
📖 Documentation
You can consult Meilisearch's documentation at https://www.meilisearch.com/docs.
🚀 Getting started
For basic instructions on how to set up Meilisearch, add documents to an index, and search for documents, take a look at our Quick Start guide.
You may also want to check out Meilisearch 101 for an introduction to some of Meilisearch's most popular features.
☁️ Meilisearch cloud
Let us manage your infrastructure so you can focus on integrating a great search experience. Try Meilisearch Cloud today.
🧰 SDKs & integration tools
Install one of our SDKs in your project for seamless integration between Meilisearch and your favorite language or framework!
Take a look at the complete Meilisearch integration list.
⚙️ Advanced usage
Experienced users will want to keep our API Reference close at hand.
We also offer a wide range of dedicated guides to all Meilisearch features, such as filtering, sorting, geosearch, API keys, and tenant tokens.
Finally, for more in-depth information, refer to our articles explaining fundamental Meilisearch concepts such as documents and indexes.
📊 Telemetry
Meilisearch collects anonymized data from users to help us improve our product. You can deactivate this whenever you want.
To request deletion of collected data, please write to us at privacy@meilisearch.com. Don't forget to include your Instance UID in the message, as this helps us quickly find and delete your data.
If you want to know more about the kind of data we collect and what we use it for, check the telemetry section of our documentation.
📫 Get in touch!
Meilisearch is a search engine created by Meili, a software development company based in France and with team members all over the world. Want to know more about us? Check out our blog!
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💌 Want to make a suggestion or give feedback? Here are some of the channels where you can reach us:
- For feature requests, please visit our product repository
- Found a bug? Open an issue!
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Thank you for your support!
👩💻 Contributing
Meilisearch is, and will always be, open-source! If you want to contribute to the project, please take a look at our contribution guidelines.
📦 Versioning
Meilisearch releases and their associated binaries are available in this GitHub page.
The binaries are versioned following SemVer conventions. To know more, read our versioning policy.
Differently from the binaries, crates in this repository are not currently available on crates.io and do not follow SemVer conventions.