Build a document summarizer as a Spring Boot upload service: accept and validate a file, extract its text, send manageable content to a LangChain4j summarization service, then return a summary with useful source metadata. For long documents, summarize coherent chunks and synthesize their results; a vector database or RAG is not required for a one-document summary.
Choose a LangChain4j integration that matches your Spring Boot version
LangChain4j documents Spring Boot starters for Java 17, with Spring Boot 3.5+ and 4.0+ listed as supported lines. Use the starter family that matches your application: the documented naming pattern is langchain4j-{integration-name}-spring-boot-starter for Boot 3 and langchain4j-{integration-name}-spring-boot4-starter for Boot 4. Check the release notes and dependency compatibility for the specific versions you select; support can change. See the LangChain4j Spring Boot integration documentation.
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Use an AI Service interface for a concise service layer
An @AiService interface expresses the summarization operation declaratively. The starter can scan the interface, create its implementation, and register it as a Spring bean using components in the application context. This avoids some request-and-response plumbing and is a natural fit for a focused summarizer. LangChain4j describes AI Services as handling common prompt input formatting and output parsing, with optional support for memory, tools, and RAG; a one-shot summary generally does not need memory or tools. See LangChain4j AI Services.
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Injecting a ChatModel lets you construct requests and handle responses more directly, including prompt templates and request options. The starter documentation shows model configuration in application properties and model injection into a controller. Choose this route if that control is more useful than the reduced boilerplate of an AI Service.
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Design the upload-to-summary flow
Keep file handling, text extraction, model summarization, and response formatting as distinct responsibilities. This makes errors easier to classify and lets the model operate on extracted text rather than on assumptions about file formats.
- Accept a multipart upload. Expose a
POSTendpoint and define any optional preferences, such as target length or bullet format. - Validate before processing. Check authorization, file size, and allowed media type before reading the file. Reject empty uploads and unsupported formats with clear client-facing errors.
- Extract document text. Use a parser appropriate to the formats the service actually supports. Preserve the original filename and page or section locations where available, and do not log full document bodies.
- Choose a size strategy. Send a small enough document in one summarization request. For longer inputs, split text into coherent, ordered chunks, summarize each chunk, then synthesize those intermediate summaries into a final result.
- Return a response DTO. Include the summary and, where useful, key points, filename, and processing status. Preserve source references so readers can check important claims against the original.
Document extraction and splitting are separate from the LangChain4j model call. Spring AI’s ETL documentation describes a pipeline using a DocumentReader, DocumentTransformer, and DocumentWriter, with readers for PDF and text inputs and a TokenTextSplitter. Those are Spring AI APIs, not LangChain4j classes, but the separation of reading, transforming, and writing is a useful architectural model for a LangChain4j service. See Spring AI ETL.
Write prompts that preserve what the document says
Specify the intended reader, desired length, and format, such as headings or bullets. Tell the model to use source-faithful wording, avoid outside facts, retain names, dates, quantities, and qualifications that affect meaning, and mark ambiguity. Ask it to say when the document does not contain an answer rather than filling a gap. Treat the result as a model-generated summary, not verified ground truth.
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Document text is untrusted input: it may contain instructions that conflict with the task. Keep the application’s summarization instruction separate from the document, explicitly treat embedded instructions as source material rather than commands, and test with adversarial documents.
Handle long documents with hierarchical summarization
A model request has finite context capacity, so a large document may not fit in a single call. Split it into semantically coherent chunks, summarize each, and combine those summaries in a final synthesis step. Preserve chunk ordering and page or section references where possible. Otherwise, a final summary can lose relationships between sections or make claims that are harder to verify against the source.
Chunk size and overlap depend on the model and the extraction output; the cited documentation does not establish a universal value. Set limits appropriate to the model and test them with the formats and document lengths your application accepts.
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Decide whether structured output is worth the added validation
Free-form text is simplest when the endpoint only needs to display a summary. If downstream code needs stable fields, define a typed response such as SummaryResponse(summary, keyPoints, caveats) and use the output-parsing or structured-output feature supported by the LangChain4j version you chose. Validate the parsed fields before returning them, and handle malformed output as a distinct failure.
A schema instruction does not guarantee that a model will return valid data. Spring AI’s structured-output documentation illustrates the general issue: mapping a model response into a Java type can fail when output is malformed or includes unexpected fields. Its ChatClient.entity(...) API belongs to Spring AI; it is not a LangChain4j API. See Spring AI structured output.
Use RAG for retrieval products, not as a requirement for summarization
A whole-document summary needs the content of the document, either in one request or represented through chunk summaries. Retrieval-augmented generation (RAG) instead selects relevant passages to answer a question. For summarizing one uploaded document, hierarchical summarization usually matches the task more directly than retrieving only semantically similar passages, and a vector store is not needed solely because the input is a document.
RAG becomes relevant when the product needs recurring search or question answering over a persistent collection, especially when users need metadata filters or answers grounded in selected passages. Keep its ingestion path separate from query handling: extract, chunk, attach metadata, and embed/store documents during ingestion; retrieve supporting passages at query time. Define what the system should do when retrieval returns no context, apply metadata filters where needed, and identify the passages that informed an answer. These patterns are covered in Spring AI RAG; the referenced retriever and advisor features are Spring AI concepts, not LangChain4j components.
Choose an implementation for the actual product need
| Decision | Option A | Option B | Choose based on |
|---|---|---|---|
| LangChain4j calling style | @AiService interface |
Direct ChatModel |
Less boilerplate versus more explicit control over prompts, options, and response handling. See LangChain4j Spring Boot and AI Services. |
| Input size | One model request | Chunk, summarize, then synthesize | Context capacity, fidelity across sections, latency, cost, and whether ordering or source references matter. See Spring AI ETL. |
| Product architecture | Direct summarization | Persistent collection plus RAG | One-off whole-document summaries versus recurring search and question answering across many documents. See Spring AI RAG. |
| Response format | Free-form text | Typed response validated by the application | Flexible display versus predictable downstream fields and parse-failure handling. See Spring AI structured output. |
Set operational and privacy safeguards
- Bound resource use. Set upload-size limits, extraction and model-call timeouts, and request-concurrency limits. Large files can exhaust memory or exceed a model’s context capacity.
- Protect uploaded content. Restrict document access, define retention and deletion behavior, and avoid logging document text, credentials, or provider responses by default. Review the selected model provider’s data-handling terms for your deployment.
- Report failures safely. Distinguish unsupported formats, extraction errors, timeouts, provider failures, and response-validation failures. Give clients actionable errors without exposing secrets or internal stack traces.
- Make results checkable. Keep page or section references where feasible so users can compare important summary claims with the original document.
- Monitor without oversharing. Track latency, failure counts, file sizes, and token usage when available, while excluding sensitive document contents.
These controls are application responsibilities; using a framework starter does not itself establish an upload-security, privacy, or retention policy.
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