Hi, honestly I think this is more like tech preview v0.2.
Do you have any roadmap?
Issues: - no support for Anthropic and Google cache - you cannot make any serious Chat app without this because you keep sending whole context to AI API and it gets expensive - RAG does not have ReRank support, only Embeddings support (you need to support custom AI model for ReRank - it is quite basic thing for good quality RAG and you cannot have good Knowledge Base RAG support, you can right now only make simple single document RAG) - You should use Neo4J graph database for memory storage, not Redis! Look at mem0 python implementation how it is done. - in DOCS for Vector storage, you should have table for maximum vector size - for example PgVector does not support Large v3 OpenAI embedding models due to hardcoded vector size of 2000 - instead of per provider implementation, you should have ONE UNIFIED API like AI SDK by Vercel - you missed the important advantage Spring AI could give
Comment From: cyberluke
Why each class have its own cache manager while Spring ecosystem does have cache support already? Why reinvent the wheel? Why not global cache service?
Comment From: cyberluke
https://github.com/spring-projects/spring-ai/blob/main/spring-ai-model/src/main/java/org/springframework/ai/chat/memory/MessageWindowChatMemory.java
-- messages should not get evicted, the industry standard is to use AI API to create summary of old messages after pre-defined number of messages, for example 10 messages
Then you can have several options: - rolling window (like circular buffer) - keep start and end of context and summarize/truncate the middle - summarize everything after N messages - use Anthropic or Google cache control to move context messages to their server cache (default TTL is 5 minutes) to save money and have long-term memory conversation suitable for chat apps - this gives you unlimited messages feature with no limit
There are core concepts for AI SDKs that everyone have, but here they are not present.
Comment From: cyberluke
cannot have multiple vector store beans in classpath?
The bean 'vectorStore', defined in class path resource [org/springframework/ai/vectorstore/qdrant/autoconfigure/QdrantVectorStoreAutoConfiguration.class], could not be registered. A bean with that name has already been defined in class path resource [org/springframework/ai/vectorstore/pgvector/autoconfigure/PgVectorStoreAutoConfiguration.class] and overriding is disabled.