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What the service should do
Keep upload handling and AI orchestration separate. Spring MVC owns the HTTP request, multipart data, and response. A document-processing service validates the upload and delegates text extraction. LangChain4j owns the model-facing summarization call. This separation makes it easier to reject unsuitable files before they reach the model and to change parsers or model integrations without changing the API contract.
A useful first API is POST /api/summaries, accepting one multipart field named file and optional preferences such as audience or desired length. Return a response DTO with the summary and metadata such as the detected media type. If processing may take too long for a single request, use a job endpoint that returns an ID and exposes status and failure details instead of holding the connection open.
Choose the formats and limits before accepting uploads
Do not treat a filename extension or client-declared content type as proof of what the file contains. Define an allowlist of formats, verify the content as far as the chosen parser permits, and reject empty, unsupported, encrypted, or malformed inputs with a clear client error. The parser’s existence does not guarantee accurate extraction from every file.
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| Input type | LangChain4j parser option | Practical consideration |
|---|---|---|
ApachePdfBoxDocumentParser |
Text extraction quality depends on the PDF; the cited documentation does not establish OCR for image-only pages or reliable layout preservation. | |
| Office formats | ApachePoiDocumentParser |
Specify which Office formats the API supports and test representative files, especially documents with tables. |
| Many formats with automatic detection | ApacheTikaDocumentParser |
Broader detection is not a promise of complete or faithful extraction from every detected format. |
| Plain text or Markdown | LangChain4j text or Markdown parser options | These are useful low-complexity formats for a first implementation. |
Spring Boot’s current MVC how-to documents defaults of 1 MB per file and 10 MB of file data per request (accessed 2026-10-03). These are configurable defaults, not production recommendations; verify behavior for the exact Spring Boot release you deploy. Configure both bounds deliberately, along with any reverse-proxy limits. Spring’s how-to recommends using the servlet container’s built-in multipart support rather than adding Apache Commons File Upload just for multipart handling.
spring.servlet.multipart.max-file-size=10MB
spring.servlet.multipart.max-request-size=12MBThe values above are an example configuration, not universal safe limits. A request limit must accommodate the file plus multipart framing and any additional fields. Set it according to your deployment’s memory, temporary-storage, and processing budgets.
Pin compatible dependencies and configure the model
LangChain4j’s Spring Boot integration documentation describes Java 17 support requirements and separate starter naming for Boot 3 and Boot 4: the documented patterns are -spring-boot-starter and -spring-boot4-starter, respectively. The integration documentation currently describes Spring Boot 3.5+ and 4.0+ support. Treat these as release-sensitive compatibility details: check the current integration guide, select the starter for your Boot major line and model provider, and pin a mutually compatible set of versions rather than copying an old dependency declaration.
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Configure the model integration using its documented settings and supply credentials through deployment configuration or a secret manager, not source control. The LangChain4j integration guide uses OpenAI as an example of model configuration. That establishes an available integration, not current pricing, retention terms, or suitability for sensitive documents. Review the provider’s current data-use and security terms for your use case.
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OPENAI_API_KEY=${OPENAI_API_KEY}Do not log API keys or full uploaded document contents. Log operational metadata such as request ID, parser outcome, processing duration, and a sanitized failure category instead.
Implement the upload and extraction boundary
Spring MVC binds a multipart file to MultipartFile. Keep the controller thin: check basic request conditions, invoke a service, and map a successful result to a DTO. For clarity, this sketch leaves parser construction inside an application-specific adapter because parser dependencies and constructors should match the LangChain4j release you pin.
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public record SummaryResponse(String summary, String mediaType) {}
@RestController
@RequestMapping("/api/summaries")
class SummaryController {
private final DocumentSummaryService service;
SummaryController(DocumentSummaryService service) {
this.service = service;
}
@PostMapping(consumes = MediaType.MULTIPART_FORM_DATA_VALUE)
SummaryResponse summarize(@RequestPart("file") MultipartFile file,
@RequestParam(required = false) String audience,
@RequestParam(required = false) String length) {
if (file.isEmpty()) {
throw new ResponseStatusException(HttpStatus.BAD_REQUEST, "File is empty");
}
return service.summarize(file, audience, length);
}
}In the service, check the configured size bound and supported content before parsing. Pass a stream or bounded temporary file to the parser rather than reading an unbounded upload into memory. Spring Boot multipart storage settings can be configured; decide whether uploads are held in memory or written to temporary storage and ensure temporary files are removed when processing ends, including on exceptions.
Have the extraction adapter return both extracted text and metadata, or a typed failure. Empty extracted text should not be sent to the model as if it were a valid document. Map unsupported format or invalid input to a client error; map parser or provider outages to an appropriate server or service-unavailable response. Do not return raw exception messages or extracted text to clients.
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Text that fits the model context
For extracted text that fits comfortably within the selected model’s context, make one clearly scoped summarization request. Include the intended audience, target length, and desired structure. Tell the model whether to retain names, figures, caveats, and uncertainty, and instruct it not to invent details missing from the source. These prompt choices improve clarity but do not guarantee faithful summaries.
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Text that exceeds the context budget
Do not send an arbitrarily large document in a single prompt. Split it at meaningful boundaries where possible, summarize each section, then synthesize those intermediate summaries into a final response. Preserve headings or section identifiers so the synthesis has context for how the parts relate. Consider passing source excerpts or references alongside intermediate summaries when omissions would be consequential.
LangChain4j’s RAG tutorial shows an ingestion splitter example using segments of at most 300 tokens with a 30-token overlap. Those are tutorial example settings for retrieval ingestion—not benchmark results or a validated summarization configuration. Choose chunk size and overlap for the model’s context capacity, parser output, document structure, and evaluation on representative documents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use an AI Service as the summarization boundary
LangChain4j AI Services provide an interface-oriented abstraction backed by generated implementations. For a one-request operation, a method such as summarize(text, preferences) is a natural boundary. The Spring Boot integration can create an AI Service bean for injection. A conceptual interface might look like this; adapt annotation and configuration details to the pinned LangChain4j version.
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interface SummaryAssistant {
String summarize(String documentText, String instructions);
}Wire the service to the configured chat model using the AI Services API or the Spring integration’s supported bean configuration. AI Services handle input formatting and output parsing, and can optionally use memory, tools, or retrieval. A one-shot document summary generally does not need chat memory; add memory only if the product supports a continuing conversation about the document.
A direct ChatModel call is also reasonable in a minimal teaching implementation when exposing the mechanics matters more than the interface abstraction. Neither choice is established as faster or more accurate; select the approach that best fits the application’s service boundary.
Account for privacy, safety, and operational failure
A document summarizer handles untrusted, potentially sensitive input. Before deployment, decide how authorization, retention and deletion, encryption, malware scanning, provider data use, and access to the original document will work. The framework documentation cited here does not establish these controls, so verify them in the relevant infrastructure and provider documentation rather than assuming the starter supplies them.
- Set upload and extracted-text limits; a small compressed file can still expand into a large amount of text.
- Apply authentication and authorization where documents or summaries are private, and avoid exposing one user’s results to another.
- Treat uploaded text as untrusted data. A document can contain instructions aimed at the model; frame the task so document content is treated as source material rather than authority to override the summarization request.
- Give generated summaries a clear label and, where appropriate, let users consult the original. Summaries can omit or distort source details.
- Handle parser failures, model timeouts, rate limits, and provider errors explicitly. For longer tasks, a queued job can report processing, completion, and failure without keeping an HTTP request open.
Test with representative supported files and check extraction separately from summary quality. Include text-heavy PDFs, files with tables, malformed uploads, empty extraction, and documents near configured limits. The cited documentation lists parser components but does not establish OCR behavior, extraction fidelity, a best chunking strategy, provider costs, or a comparative performance benchmark.
Quick Recap
End-to-end request flow
- Receive: Spring MVC accepts
multipart/form-dataas aMultipartFileand enforces deliberate request and per-file limits. - Validate: Reject empty, oversized, unsupported, or invalid files; do not trust the filename or declared media type alone.
- Extract: Route the file to a parser selected for the formats the API promises, then reject empty or unusable extracted text.
- Summarize: Use one model request for text within context; otherwise split, summarize sections, and synthesize with structure preserved.
- Return: Respond with the summary and useful metadata, and report validation, extraction, or provider failures with appropriate status and safe error details.
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