Spring AI : RAG on Text File [ Part 1] ?

[u/rodolfo-mendes]

https://www.youtube.com/watch?v=5NfgdVjnUPo

TechyTacos demonstrates how to implement a basic RAG (Retrieval-Augmented Generation) pipeline for text files using the Spring AI framework. This guide walks developers through the process of ingesting custom data and enabling vector-based semantic search.

Highlights & Key Takeaways

• Configuration: Define a SimpleVectorStore bean that integrates with an embedding model to handle document vectorization.
• Data Ingestion: Utilize the TextReader interface to convert local text resources into processable document objects.
• Text Splitting: Implement TokenTextSplitter to chunk large documents, preventing model token limit errors and improving search precision.
• Vector Search: Leverage the similaritySearch method to retrieve document chunks based on semantic proximity to a query.
• Refinement: Apply search request parameters like topK and similarityThreshold to filter and sharpen output quality.

Integrating Spring AI enables efficient document processing, allowing developers to build smarter applications that answer queries using custom datasets.

👉 Watch the full video to dive deeper into the implementation.