Multiple database support
Connect to popular vector databases, including Elasticsearch and OpenSearch.
Semantic memory and intelligence for your processes
The Embeddings Vector DB connector provides bidirectional access to vector stores, enabling Camunda processes and AI agents to write new embeddings and retrieve the most relevant chunks at runtime. Typical use cases include long-term conversational memory, Retrieval-Augmented Generation (RAG), and semantic search.
Features and Benefits
Embedding operations
Store document embeddings and retrieve relevant content based on semantic meaning.
Camunda document support
Integrate with Camunda's document management system to handle process-related documents.
AI agent support
The Vector Database connector works seamlessly with the AI Agent connector to provide RAG capabilities. Use it to retrieve relevant context documents and past interactions that can be provided to AI agents for more informed and accurate responses.
Details
- Marketplace release date -
- Last Github commit -
-
Associated Product Group Categories:
- AI Services
-
Version Compatibility:
-
Used resources: