Build a RAG Chatbot with Spring Boot and Next.js

Updated 5 November 2026 · Standard formula, shown below

Build a chatbot that answers questions using your own documents: Spring Boot retrieves relevant passages and sends them to a language model, while a Next.js interface collects questions and displays answers. You’ll need a Java and Node.js development environment, an embedding and chat-model provider, and a small set of source documents to index.

What You’ll Need

A supported Java Development Kit, Maven or Gradle, and Node.js with npm.
A Spring Boot backend and a Next.js app; keep them in separate folders or repositories.
Credentials for a language model and embedding model, stored in environment variables rather than committed to source control.
A vector store, such as PostgreSQL with a vector extension, or another database supported by your chosen Spring AI integration.
Text documents you are permitted to process, plus a way to extract text from their formats. Start with plain text or Markdown to reduce setup.

Step-by-Step

Choose the retrieval components. Use Spring AI, or a comparable Spring-compatible library, to connect to chat and embedding models and a vector store. Check the library documentation for versions compatible with your Spring Boot version. Embeddings turn text into numerical representations; the vector store finds passages with meanings similar to a question. A chat model then writes a response from those passages.
Create the backend project. Generate a Spring Boot application with a web starter and the AI model and vector-store integrations you selected. Add the relevant database driver and migration tool if needed. Keep model-provider credentials and database connection details in environment variables or a local, ignored configuration file. Do not put secrets in application code or the Next.js bundle.
Set up the vector database. Create a database and enable the required vector support if your chosen store needs it. Configure Spring with the connection URL and credentials, then run schema migrations or the integration’s documented initialization. Confirm that the application can connect before adding ingestion logic.
Prepare and index documents. Build an ingestion service that reads each file, extracts its text, and splits it into overlapping chunks. Chunk size and overlap depend on document structure and model limits; begin with moderate chunks and tune using actual questions. Attach metadata such as filename, page, and a document identifier. Send each chunk to the embedding model and save the resulting vector, text, and metadata. Make ingestion repeatable: update or remove old chunks when a source document changes, and avoid indexing the same file repeatedly.
Implement retrieval and answering. Create a backend service that accepts a question, embeds it, and searches for a small number of relevant chunks. Build a prompt that clearly labels retrieved text as reference material, tells the model to answer from that material, and asks it to say when the sources do not support an answer. Include source metadata in the response. Do not treat retrieved document text as trusted instructions; it is content to analyze, not a command to override application rules.
Expose a chat endpoint. Add a POST endpoint such as /api/chat that accepts a JSON question and returns an answer plus source references. Validate that the question is present and within a sensible length. Handle model timeouts and provider errors with useful HTTP responses, and avoid returning stack traces or credentials. Add authentication and rate limits before making the endpoint publicly available.
Allow the frontend origin. During local development, configure Spring’s CORS policy to allow only the Next.js development origin and the required methods and headers. In production, allow the deployed site’s specific origin. CORS is not authentication; it does not prevent other clients from calling a public API.
Create the Next.js chat screen. Start a Next.js project and add a page with a message history, text input, submit button, and loading and error states. On submit, send the question as JSON to the Spring endpoint using fetch. Render the returned answer and source names, and disable repeated submissions while a request is pending. Keep provider keys exclusively on the backend; the browser should call your API, not the model provider directly.
Test the complete retrieval path. Index a few representative documents, then ask questions whose answers are present, absent, and phrased differently from the source. Verify that the retrieved passages actually support the response and that citations point to the right file or page. Try empty input, long input, unavailable services, and a new version of an indexed document.
Prepare for deployment. Set production environment variables, configure HTTPS, restrict CORS, and use a persistent vector database. Add logging that helps diagnose retrieval and latency without recording secrets or unnecessary personal data. Re-index documents through a controlled job or admin process, and monitor model and database failures.

Common Mistakes

Sending whole documents to the model: This can exceed context limits and makes relevant details harder to find. Split content and retrieve only useful chunks.
Assuming a fluent answer is a grounded answer: A model can invent details. Require answers to rely on retrieved context, show sources, and test questions with no supporting passage.
Using unsuitable chunks: Chunks that are too small lose context; chunks that are too large can bury the answer. Inspect retrieval results and tune chunking against real queries.
Re-indexing without cleanup: Old and duplicate chunks can compete with current content. Track document identifiers and replace or delete a document’s previous vectors on update.
Exposing secrets or an unrestricted endpoint: Frontend environment variables may be visible to browsers. Keep credentials server-side and protect the API with appropriate access controls.

Tips

Store useful metadata, especially filenames, headings, page numbers, and update dates, so answers can cite sources clearly.
Keep a small evaluation set of questions with expected source passages. Re-run it after changing chunking, prompts, models, or retrieval settings.
Log retrieval scores, selected source identifiers, response time, and errors where appropriate. These help distinguish retrieval problems from answer-generation problems.
For large or scanned PDFs, use a reliable text extraction or OCR step and check its output before indexing.

Troubleshooting

The backend cannot connect to the model or database: Check environment variable names, credentials, network access, and the configured URL. Read the server-side error logs, but do not paste secrets into shared logs or support requests.

The answer ignores relevant documents: Inspect the chunks returned by the vector search before changing the prompt. If they are wrong, check extraction quality, chunk boundaries, embedding configuration, and similarity-search settings. If they are right but the answer is weak, improve the prompt or reduce irrelevant context.

The browser reports a CORS error: Confirm the exact frontend origin, including scheme and port, is allowed by Spring. Check that preflight OPTIONS requests are handled and that allowed methods and headers match the frontend request. Do not solve this by allowing every origin in production.

The UI stays loading or shows a generic error: Inspect the browser network panel and backend logs. Verify the endpoint URL, JSON property names, HTTP status, and response format. Add request timeouts and display a retry option for temporary failures.

Frequently Asked Questions

Does a RAG chatbot train the language model?
No. It retrieves relevant text at question time and supplies it as context. The model’s weights are not changed by ordinary indexing.

Do I need a vector database?
For semantic search across embedded chunks, use a vector store. Some databases support vectors alongside regular data; the right option depends on scale, hosting, and your Spring integration.

Can I use PDFs?
Yes, if you extract their text first. Scanned pages may require OCR, and you should verify that headings, page numbers, and reading order survive extraction.

How can the chatbot show its sources?
Save source metadata with each chunk and return the metadata for retrieved passages alongside the answer. Display those references in the Next.js interface.

Why does it answer questions that are not in my documents?
The model may be relying on its general knowledge or guessing. Tighten the grounding instructions, set an appropriate retrieval threshold if supported, and explicitly test unsupported questions.

Should I use streaming responses?
Streaming can make long answers feel faster, but it adds transport and UI complexity. Start with a normal request-response endpoint, then add streaming when the basic retrieval flow is reliable.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.