Announcing the IBM Db2 Vector Store integration for LlamaIndex
Published 22 October 2025
We are pleased to announce the release of the IBM Db2 Vector Store integration for LlamaIndex, an open-source Python package that enables developers to use Db2 as a vector store in large language model (LLM) application workflows built with LlamaIndex.
Built on Db2’s native vector data type, this integration combines the scalability and reliability of Db2 with the flexibility of one of the most widely used open-source frameworks for LLM development.
This release builds on our earlier Db2 LangChain integration, expanding Db2’s support for the most popular Python frameworks used to build retrieval-augmented and agentic AI applications. Together, these integrations make it easier for developers to prototype, experiment and deploy with Db2 as a trusted foundation for open-source AI innovation.
The Python connector simplifies the development of retrieval-augmented generation (RAG) and AI-powered applications by using Db2 as the vector store for semantic search and context retrieval. This integration addresses a critical need in the AI developer community: seamless access to enterprise-grade vector storage capabilities in Db2 through a familiar, Python-based framework.
By combining LlamaIndex’s data orchestration capabilities with Db2’s enterprise-grade reliability and performance, developers can build intelligent applications faster while maintaining the governance, security and scale required for production environments.
LlamaIndex provides a flexible framework for building and managing context-aware AI applications. The Db2 Vector Store integration extends this framework with a native Python interface that enables developers to:
- Create tables with vector columns in Db2 through intuitive Python commands
- Insert, store and efficiently manage embedding vectors at scale
- Perform similarity search using supported distance metrics, including cosine, dot product and Euclidean
- Leverage Db2’s enterprise-grade performance, security, and reliability features
All operations are supported through familiar Python workflows, making it easy to integrate Db2 into modern GenAI and agentic AI applications without requiring database expertise.
The Db2 Vector Store integration for LlamaIndex simplifies how developers build retrieval-augmented and AI-powered applications. With Db2’s vector data type, teams can store and query embeddings at scale using familiar Python workflows without complex setup or custom vector logic.
This integration bridges rapid experimentation and enterprise deployment, combining LlamaIndex’s flexibility with Db2’s reliability, security and governance so developers can focus on innovation, not infrastructure.
The connector is available for download from PyPI using standard Python package management tools. Installation is straightforward and requires minimal configuration to begin working with Db2 vector capabilities.
To help you get started, we’ve published a tutorial notebook demonstrating how to use the Db2 LlamaIndex integration as part of a Python workflow. The tutorial covers common usage scenarios, including document embedding, semantic search and RAG pipeline construction.
This release reinforces our commitment to supporting the open-source AI community while providing access to enterprise-grade data management capabilities that scale with your applications.