Skip to content

Update README.md #213

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
murrayhu-mdb wants to merge 1 commit into from
+2 −2
Closed

Update README.md #213

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions apps/lyric-semantic-search/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ This repo is support the [Building a Semantic Search Service With Spring AI and

Spring AI is an application framework from [Spring](https://spring.io/) that allows you to combine various AI services and plugins with your applications. With support for many chat, text-to-image, and embedding models, you can get your AI powered Java application set up for a variety of AI use cases.

With Spring AI, MongoDB Atlas is supported as a vector database, all with [Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) to power your semantic search and implement your RAG applications. To learn more about RAG and other key concepts in AI, check out the [MongoDB AI integration docs](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/#std-label-ai-key-concepts).
With Spring AI, MongoDB Atlas is supported as a vector database, all with [MongoDB Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search) to power your semantic search and implement your RAG applications. To learn more about RAG and other key concepts in AI, check out the [MongoDB AI integration docs](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/#std-label-ai-key-concepts).

In this tutorial, we’ll go through what you need to get started with Spring AI and MongoDB. Adding documents to your database with the vectorised content (embeddings), and searching this content with semantic search. The full code for this tutorial is available in this [Github repository](https://github.com/timotheekelly/lyric-semantic-search).

Expand Down Expand Up @@ -75,7 +75,7 @@ curl -X GET "http://localhost:8080/search?query=small%20celestial%20bodie&topK=5

### Filter by Metadata

In order to filter our data, we need to head over to our index in MongoDB. You can do this through the Atlas UI by selecting the collection where your data is stored, and going to the search indexes. You can edit this index by selecting the three dots on the right of the index name and we will add our filter for the artist.
In order to filter our data, we need to head over to our index in MongoDB. You can do this through the UI by selecting the collection where your data is stored, and going to the search indexes. You can edit this index by selecting the three dots on the right of the index name and we will add our filter for the artist.

```json
{
Expand Down
Loading