To put this paper itself "in action", the accompanying GitHub repo would be:
@RestController public class ChatController private final ChatClient chatClient; private final VectorStore vectorStore; @GetMapping("/ask") public String askAboutGitHubPdfs(@RequestParam String question) // Retrieve relevant PDF chunks List<Document> relevantDocs = vectorStore.similaritySearch(question); // Create system prompt with context String context = relevantDocs.stream() .map(Document::getText) .collect(Collectors.joining("\n---\n")); return chatClient.call(new Prompt( List.of(new SystemMessage("Answer based only on: " + context), new UserMessage(question)) )).getResult().getOutput().getText(); spring ai in action pdf github
spring-ai-pdf-github-demo/ ├── src/main/java/com/example/ │ ├── config/VectorStoreConfig.java │ ├── service/GitHubPdfFetcher.java │ ├── service/PdfDocumentService.java │ ├── pipeline/IngestionPipeline.java │ └── controller/ChatController.java ├── src/main/resources/application.yml ├── docker-compose.yml (for PGVector) ├── README.md └── sample-pdfs/ (for testing) spring: ai: openai: api-key: $OPENAI_API_KEY embedding: options: model: text-embedding-ada-002 vectorstore: pgvector: index-type: HNSW distance-type: COSINE_DISTANCE datasource: url: jdbc:postgresql://localhost:5432/vectordb github: token: $GITHUB_TOKEN 5. Best Practices & Troubleshooting | Challenge | Solution | |-----------|----------| | Large PDFs > 10MB | Use GitHub's blob API with range requests. | | Rate limiting (GitHub API) | Implement RetryTemplate with exponential backoff. | | PDFs with scanned images | Use TikaDocumentReader with OCR plugin (Tesseract). | | Token limit exceeded | Use TokenTextSplitter with overlap=100 tokens. | | Metadata tracking | Add Document metadata: put("source", pdfUrl) for provenance. | 6. Conclusion The combination of Spring AI (abstractions for LLM workflows), GitHub as a document source , and PDF parsing creates a powerful enterprise knowledge retrieval system. By following the ingestion and query patterns shown here, developers can build secure, context-aware AI applications that leverage existing documentation stored in GitHub repositories. To put this paper itself "in action", the
This is an excellent topic, as it sits at the intersection of a popular framework (Spring AI), a specific resource format (PDF), and a vital developer platform (GitHub). | | PDFs with scanned images | Use
@Service public class GitHubPdfFetcher private final GitHub github = new GitHubBuilder().withOAuthToken(System.getenv("GITHUB_TOKEN")).build(); public List<byte[]> fetchPdfsFromRepo(String repoName, String path) throws IOException GHRepository repo = github.getRepository(repoName); List<GHContent> pdfs = repo.getDirectoryContent(path).stream() .filter(c -> c.getName().endsWith(".pdf")) .toList(); return pdfs.stream().map(content -> try (InputStream is = content.read()) return is.readAllBytes(); catch (IOException e) throw new RuntimeException(e); ).collect(Collectors.toList());
public void indexPdfsFromGitHub(String repo, String pdfPath) List<byte[]> pdfs = gitHubPdfFetcher.fetchPdfsFromRepo(repo, pdfPath); List<Document> rawDocs = pdfDocumentService.parsePdfs(pdfs); List<Document> chunkedDocs = splitter.apply(rawDocs); // Store in vector DB vectorStore.add(chunkedDocs);