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[v1.10.0] AI-powered search changes #561
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I don't see that anything needs to be done here. In fact, this gem does not have any real support for AI search at all. If you like, we could create convenience methods for setting embedders, but until we do the only way to set them is Should I open an issue for this? |
@brunoocasali something needs to happen here, your documentation points to methods that don't exist client.index('INDEX_NAME').update_embedders( |
or wait, does @ellnix also work for MS? |
it doesn't appear that
|
looks like update_settings is real |
Unfortunately, the documentation website seems to work off of the This should probably change, but I'm not aware of anything I can do on my end to fix it. The SDKs repos are set up in very particular ways with the bors (the meili bot) and I cannot change the branches or the structure.
I do not, I just maintain this gem (and the rails one) along with Bruno, who works for MS and coordinates the work on the various SDKs. |
I do not see that in the documentation anywhere, please link it for me and I'll make a PR to update the syntax. Edit: I see that I said it in my above message, apologies, that was just something I wrote quickly. The correct method is indeed |
@ellnix so who can make a release? Also, can you show me an example of how to create an embedder with the I can put together a blog post documenting how to get semantic search running locally if so |
I can trigger it, but I would need my PR that changes the version number to be approved by a member of MS, since I don't have write access to the repository. That PR is just a convenience method though, you can access the full capability of MS without it.
You would simply have to client.index(INDEX_UID).update_settings({
embedders: {
# embedder configuration goes here
}
}) So the example in the documentation would become: client.index(INDEX_UID).update_settings({
embedders: {
default: {
source: 'openAi',
api_key: 'anOpenAiApiKey',
model: 'text-embedding-3-small',
document_template: "A document titled '{{doc.title}}' whose description starts with {{doc.overview|truncatewords: 20}}"
}
}
}) That is all the ruby-specific knowledge you need. You can then get started with https://www.meilisearch.com/docs/learn/ai_powered_search/getting_started_with_ai_search In the ruby gem, the option keys are passed as snake_cased symbols, but you can use camelCased strings/symbols (that most of the documentation uses) if that confuses you. You'll get a warning but everything will be fine. |
Also, you would need to enable vector store currently, since it is considered an experimental feature in the latest You are probably tired of hearing it, but the method to change experimental settings is currently an open PR waiting for a review #594 If you are using Meilisearch Cloud, you could use the dashboard, or if you are hosting MS elsewhere you can simply run the curl example in the documentation once and it will be enabled: curl \
-X PATCH 'MEILISEARCH_URL/experimental-features/' \
-H 'Content-Type: application/json' \
--data-binary '{
"vectorStore": true
}' |
Following this central issue
Explanation of the feature
query
,inputField
,inputType
,pathToEmbeddings
andembeddingObject
.request
andresponse
headers
url
parameter to the OpenAI embedderdimensions
is now available as an optional parameter forollama
embedders.Usage: https://meilisearch.notion.site/v1-10-AI-search-changes-737c9d7d010d4dd685582bf5dab579e2?pvs=74
TODO
query
,inputField
,inputType
,pathToEmbeddings
andembeddingObject
.request
,response
andheaders
(cf usage for more information)url
parameter to the OpenAI embedderdimensions
for Ollama embedders.The text was updated successfully, but these errors were encountered: