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4815: Rest embedder api mk2 r=ManyTheFish a=dureuill # Pull Request ## Related issue Fixes https://github.com/meilisearch/meilisearch/issues/4756 - [x] [REST API parameter names and behavior are unclear](https://github.com/meilisearch/documentation/pull/2824#issuecomment-2124073720) - unclear names are removed. There remain only two parameters: `request`, a template of what Meilisearch's request to the embedding server should be, and `response`, a template of what the embedding server's response to Meilisearch should look like - [x] [Bad error message or bad default value when we don't specify the `query` parameter](85d8455c11/meilisearch/tests/vector/rest.rs (L105-L140)) - The replacement for `query`, which is `request`, is now a mandatory parameter. Omitting it will result in the following error message : "`.embedders.rest`: Missing field `request` (note: this field is mandatory for source rest)", which is clear - [x] [Bad error message when both `pathToEmbeddings` and `embeddingObject` are missing](2141cb3b69/meilisearch/tests/vector/rest.rs (L142-L178)) - These parameters no longer exist. Now, the point of extraction is given directly by the location of an `{{embedding}}` placeholder in the `response` parameter. - [x] [Unexpected error when we don't specify both `pathToEmbeddings` and `embeddingObject` (only once should be required)](2141cb3b69/meilisearch/tests/vector/rest.rs (L180-L260)) - These parameters no longer exist. Now, the point of extraction is given directly by the location of an `{{embedding}}` placeholder in the `response` parameter. - [x] [Should not panic when the dimensions specified do not work with the model](2141cb3b69/meilisearch/tests/vector/rest.rs (L262-L299)) - This no longer panics, instead returns "While embedding documents for embedder `rest`: runtime error: was expecting embeddings of dimension `2`, got embeddings of dimensions `3`" - [x] [Be more flexible on the type of data that is accepted](https://github.com/meilisearch/meilisearch/issues/4757#issuecomment-2201948531) - [x] Always accept arrays of embeddings even if `inputType` is set to `text` - This is controlled by the repeat placeholder `"{..}"`, an array of embeddings can be configured even if the input is not in an array. - [x] Accept arrays of result at the root level and texts/array of text at the root level. - doable with `request: "{{text}}"` and `response: "{{embedding}}"` or `response: ["{{embedding}}"]` (see test `vector::rest::server_raw`) ## What does this PR do? - [See public usage](https://meilisearch.notion.site/v1-10-AI-search-changes-737c9d7d010d4dd685582bf5dab579e2#8de842673ffa4a139210094a89c1ec3e) - Add new `milli::vector::json_template` module to parse JSON templates with an injection placeholder and a repeat placeholder - Change rest embedder to use two JSON templates - Change ollama and openai embedders to use the new rest embedder - Update settings - Update and add tests ## Breaking change > [!CAUTION] > This PR is a breaking change to the REST embedder. > Importing a dump containing a REST embedder configuration will fail in v1.10 with an error: "Error: unknown field `query`, expected one of `source`, `model`, `revision`, `apiKey`, `dimensions`, `documentTemplate`, `url`, `request`, `response`, `distribution` at line 1 column 752". Upgrade procedure: 1. Remove any embedder with source "rest" 2. Create a dump 3. Import that dump in a v1.10 4. Re-add any removed embedder, using the new settings. Co-authored-by: Louis Dureuil <louis@meilisearch.com> Co-authored-by: Louis Dureuil <louis.dureuil@xinra.net> Co-authored-by: Tamo <tamo@meilisearch.com>
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⚡ 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.
🖥 Examples
- Movies — An application to help you find streaming platforms to watch movies using hybrid search.
- Ecommerce — Ecommerce website using disjunctive facets, range and rating filtering, and pagination.
- Songs — Search through 47 million of songs.
- SaaS — Search for contacts, deals, and companies in this multi-tenant CRM application.
See the list of all our example apps in our demos repository.
✨ Features
- Hybrid search: Combine the best of both semantic & full-text search to get the most relevant results
- Search-as-you-type: Find & display results in less than 50 milliseconds to provide an intuitive experience
- Typo tolerance: get relevant matches even when queries contain typos and misspellings
- Filtering and faceted search: enhance your users' 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
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- Easy to install, deploy, and maintain
📖 Documentation
You can consult Meilisearch's documentation at 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 documentation guide.
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🧰 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.
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Meilisearch collects anonymized user data to help us improve our product. You can deactivate this whenever you want.
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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.
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Differently from the binaries, crates in this repository are not currently available on crates.io and do not follow SemVer conventions.