Check out the blogs that falls under the category Vector Search. Learn more about the vector search best practices – interacting with AI LLMs and more
Category: Vector Search
Building Smarter Agents: How Vector Search Drives Semantic Intelligence
Vector search has become essential and Couchbase is enabling this transformation with Full Text Search (FTS) and Eventing.
How I Built a Plant RAG Application with Couchbase Vector Search on iOS
Everything runs on-device using Couchbase vector search. No internet required, no photos sent to servers, just pure local plant identification magic.
Vector Database vs. Graph Database: Differences & Similarities
Explore the differences and similarities between vector and graph databases, and learn which is best for your desired use case.
Introducing Couchbase as a Vector Store in Agno
This integration brings together the best of Agno’s agent orchestration capabilities and Couchbase’s high-performance, scalable vector store.
Building a Simple Travel Agent Workflow with n8n and Couchbase Vector Search
Build a travel agent chatbot with n8n and Couchbase Vector Search using semantic search and workflow automation.
Unlocking the Power of AWS Bedrock with Couchbase
In this blog, we explore how Couchbase’s vector store, when integrated with AWS Bedrock, creates a powerful, scalable, and cost-effective AI solution.
Semantic Similarity with Focused Selectivity
Discover how selective pre-filtering enhances semantic similarity search in Couchbase by combining vector and metadata queries.
Introducing Couchbase as a Vector Store in Flowise
Integrate Couchbase as a high-performance vector store in Flowise. Upsert data, run vector searches, and build AI applications with this no-code solution.
Extending RAG capabilities to Excel with Couchbase, LLamaIndex, and Amazon Bedrock
Extend Retrieval Augmented Generation (RAG) capabilities to Excel using Couchbase, LlamaIndex, and Amazon Bedrock. Make spreadsheets searchable.
Chat With Your Git History, Thanks to RAG and Couchbase Shell
Turn your Git history into a chat-ready knowledge base using RAG and Couchbase Shell.
A Guide to LLM Embeddings
Learn how LLMs generate and use embeddings to enhance natural language processing, improve search relevance, and enable AI-driven applications.
AI-Ready Data: Automate Embeddings with Capella’s Vectorization Service
Capella Vectorization Service automates embeddings, AI development is faster, easier with seamless semantic search, RAG apps, smart data retrieval.
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