Skip to content

DOC-12116 Typo in Server + Capella Release Notes for Partner #3573

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

Merged
merged 1 commit into from
May 1, 2024
Merged

DOC-12116 Typo in Server + Capella Release Notes for Partner #3573

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 modules/introduction/partials/new-features-76.adoc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -155,7 +155,7 @@ See xref:learn:clusters-and-availability/rebalance.adoc#index-rebalance-methods[
* Couchbase Server 7.6 introduces Vector Search to enable AI integration, semantic search, and the RAG framework.
A developer-friendly vector indexing engine exposes a vector database and search functionality.
With Couchbase Vector Search, you can enable fast and highly accurate semantic search, ground LLM responses in relevant data to reduce hallucinations, and enhance or enable use cases like personalized searches in e-commerce and media & entertainment, product recommendations, fraud detection, and reverse image search.
You can also enable full access to an AI ecosystem with a Langchain integration, the most popular open-source framework for LLM-driven applications.
You can also enable full access to an AI ecosystem with a LangChain integration, the most popular open-source framework for LLM-driven applications.
+
A Vector Search database includes:
+
Expand All @@ -164,7 +164,7 @@ A Vector Search database includes:
** Storage of raw Embedding Vectors in the Data Service in the documents themselves
** Querying Vector Indexes (REST and UI via a JSON object/fragment, Couchbase SDKs, and {sqlpp})
** {sqlpp}/N1QL integration
** Third-party framework integration: Langchain (later Llamaindex + others)
** Third-party framework integration: LangChain (later LlamaIndex + others)
** Full support for Replicas Partitions and file-based Rebalance

NOTE: Vector Search is currently only supported on Couchbase Server 7.6.0 deployments running on Linux platforms.
Expand Down