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VirajAgarwal1 merged 4 commits into from
Feb 9, 2026
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Update langchain4j #87

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8 changes: 4 additions & 4 deletions tutorial/markdown/java/langchain4j.md
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Expand Up @@ -54,7 +54,7 @@ acquired from pretrained model embeddings in a dedicated storage, developers can
AI-based applications.

## Couchbase Embedding Store
Couchbase langchain4j integration stores each embedding in a separate document and uses an FTS vector index to perform
Couchbase langchain4j integration stores each embedding in a separate document and uses a search vector index to perform
queries against stored vectors. Currently, it supports storing embeddings and their metadata, as well as removing
embeddings. Filtering selected by vector search embeddings by their metadata was not supported at the moment of writing
this tutorial. Please note that the embedding store integration is still under active development and the default
Expand All @@ -69,7 +69,7 @@ initialization:
- name of the bucket in which embeddings should be stored
- name of the scope in which embeddings should be stored
- name of the collection in which embeddings should be stored
- name of an FTS vector index to be used by the embedding store
- name of an search vector index to be used by the embedding store
- dimensionality (length) of vectors to be stored

The following sample code illustrates how to initialize an embedding store that connects to a locally running Couchbase
Expand Down Expand Up @@ -109,10 +109,10 @@ CouchbaseEmbeddingStore embeddingStore = new CouchbaseEmbeddingStore.Builder(cou
```

### Vector Index
The embedding store uses an FTS vector index in order to perform vector similarity lookups. If provided with a name for
The embedding store uses a search vector index in order to perform vector similarity lookups. If provided with a name for
vector index that does not exist on the cluster, the store will attempt to create a new index with default
configuration based on the provided initialization settings. It is recommended to manually review the settings for the
created index and adjust them according to specific use cases. More information about vector search and FTS index
created index and adjust them according to specific use cases. More information about vector search and search vector index
configuration can be found at [Couchbase Documentation](https://docs.couchbase.com/server/current/vector-search/vector-search.html).

### Embedding Documents
Expand Down