Infinispan AI Integrations: Spring AI and LangChain Python
I am very happy to announce two new releases that bring Infinispan closer to the AI ecosystem: Spring AI Infinispan 1.0.0 and LangChain Infinispan 0.1.0 for Python.
These two integrations are the result of the work we have been doing to make Infinispan a first-class citizen in the AI world. Infinispan has supported vector search since version 15, and now we are making it easier than ever to use it from the most popular AI frameworks.
Spring AI Integration 1.0.0
Spring AI is the Spring ecosystem’s answer to building AI-powered applications in Java. With the release of Spring AI Infinispan 1.0.0, you can now use Infinispan as a backend for two key Spring AI capabilities:
Vector Store
Store and search document embeddings using Infinispan’s vector search capabilities. The integration supports cosine, L2 and inner-product similarity metrics, metadata filtering, and auto-creates Protobuf schemas and indexed caches on startup. If you have been using Infinispan for caching or data storage, you can now also use it as your vector database without adding another component to your architecture.
Chat Memory Repository
Persist conversation history in Infinispan, enabling stateful conversational AI applications. User, assistant, system and tool messages are all stored and retrieved, so your chatbot can remember what was said across sessions.
Getting Started
Both modules come with Spring Boot auto-configuration starters. Getting started is as simple as adding the dependency and pointing to your Infinispan server. For all the details, check out the Spring AI integration documentation.
LangChain Infinispan 0.1.0 for Python
LangChain is one of the most widely used frameworks for building AI applications, and it has a huge community in the Python world. With the release of LangChain Infinispan 0.1.0, Python developers can now use Infinispan as a vector store directly from LangChain.
This means you can store your embeddings in Infinispan, perform similarity searches, and combine them with all the tools LangChain provides for building RAG pipelines, agents, and AI-powered applications.
For documentation and usage examples, check out the LangChain Infinispan documentation.
Why Infinispan for AI?
If you are already using Infinispan for caching, session storage, or as a data grid, these integrations let you reuse the same infrastructure for your AI workloads. No need to add yet another database to your stack.
Infinispan brings several advantages as a vector store:
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Unified query language - Ickle combines relational queries, full-text search, and vector kNN in a single language
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Distributed by design - kNN queries work across a distributed cluster, with automatic fan-out and result merging
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Multiple access protocols - the same indexed cache is accessible via Hot Rod, REST, and RESP (Redis-compatible)
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Open source - fully open source under Apache 2.0, part of the Commonhaus foundation
Tutorials
If you want to get hands-on, we have tutorials available to help you get started:
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Spring AI with Infinispan - get started with the Spring AI vector store and chat memory repository
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Semantic Search with LangChain4j and Infinispan - for Java developers using LangChain4j
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Vector Search Quickstart - a complete example with hybrid vector and metadata queries
What’s next
We will continue working on AI integrations and improving the developer experience. If you have feedback or ideas, join the conversation on Zulip or GitHub Discussions.
Happy coding!
Get it, Use it, Ask us!
We’re hard at work on new features, improvements and fixes, so watch this space for more announcements!Please, download and test the latest release.
The source code is hosted on GitHub. If you need to report a bug or request a new feature, look for a similar one on our GitHub issues tracker. If you don’t find any, create a new issue.
If you have questions, are experiencing a bug or want advice on using Infinispan, you can use GitHub discussions. We will do our best to answer you as soon as we can.
The Infinispan community uses Zulip for real-time communications. Join us using either a web-browser or a dedicated application on the Infinispan chat.
Katia Aresti
Katia Aresti is part of the core Infinispan team, and she is responsible for the integration with other frameworks such as Spring-Boot, Vert.x and Quarkus, developing new features such as Clustered Counters, REST API endpoints and the Server Console.
