Add AI essentials like RAG and ML to dev workflow
Challenge
Building agents and agentic applications requires an entirely new workflow. Do you have a plan to manage RAG processes, prompt engineering, and LLM response validation?
Solution
Capella DBaaS provides flexible data modeling and support for transactional, analytic, and AI data access patterns. You can include your enterprise data in AI embeddings and run RAG alongside the platform to capture every input and output and then use the information to validate the application’s next activity.
Support for all popular AI/ML languages and frameworks
Ensure security in agentic applications
Challenge
AI-powered applications often use sensitive data that requires robust security and compliance to protect data integrity and lower the risk of unauthorized publication.
Solution
Capella gives developers advanced security features without sacrificing high performance. It provides encryption, role-based access control (RBAC), and auditing. Plus it hosts private language models like Llama 3 and Mistral so developers can train and build vector embeddings without compromising sensitive data.
Support for all popular AI/ML languages and frameworks
Integrate with AI tools
Challenge
Integrating GenAI tools and frameworks with your database is often complex and time-consuming work.
Solution
Capella integrates with popular AI tools like LangChain and LlamaIndex to simplify your workflow and give you access to vector search and all the other rich features of an AI framework. Couchbase also makes it straightforward to build and deploy AI/ML models using SQL++ and other familiar tools and languages.
Support for all popular AI/ML languages and frameworks
Manage the backend without slowing down
Challenge
Having a complex backend with separate systems for SQL, full-text, and vector search slows development and complicates high-performance search integration.
Solution
Couchbase’s multi-model hybrid search reduces backend systems by unifying traditional SQL++ queries, key-value, caching, full-text search, and vector search in one platform. By executing complex queries in one place, you can simplify and accelerate development, enhance flexibility, and reduce infrastructure costs.
