I am looking to build a documentation-based chatbot for my enterprise project. I want to use Spring Boot with the new Spring AI framework to implement a RAG pipeline. Specifically, how do I handle the ETL process for PDF documents and store them in a PGVector or Redis database? Any advice on managing the ChatClient for context-aware responses?
3 answers
To implement RAG in Spring Boot, the Spring AI framework provides a very intuitive ETL (Extract, Transform, Load) API. You start by using the TikaDocumentReader or PdfReader to extract text from your files. Once extracted, use a TokenTextSplitter to break the content into manageable chunks—this is crucial for staying within LLM token limits. For storage, Spring AI has first-class support for PGVector and Redis. You simply define a VectorStore bean. Finally, use the ChatClient to wrap your prompt with retrieved data to ensure the AI only answers based on your documents.
Have you looked into how the VectorStore handles metadata filtering? I’m struggling to restrict the search to specific document categories during the retrieval phase without losing performance.
The easiest way is using the spring-ai-openai-spring-boot-starter. It auto-configures the chat and embedding models so you can focus strictly on your data ingestion logic.
Exactly, Justin. I’d add that using ChatClient.Builder to create a fluent API client makes the code much cleaner than the older template-based approach used in 2023.
Brandon, you should check out the FilterExpression API in Spring AI. It allows you to pass a SQL-like metadata filter directly into your similarity search. For example, you can specify category == 'finance' in your query. This ensures the vector store only considers relevant chunks, which actually speeds up the search and improves response accuracy for your chatbot.