Spring Ai In Action Pdf Github ((top)) -

Spring AI in Action is a practical guide by Craig Walls (published by Manning ) that teaches Java developers how to integrate generative AI into Spring Boot applications. Official GitHub Repositories

RAG is a technique that connects your AI model to your own data, allowing it to answer questions about your documents, knowledge bases, or APIs. The Spring AI in Action book has a dedicated section explaining how to set up this pattern. A great GitHub example is the spring-ai-cli-chatbot project. This repository demonstrates a command-line chatbot that loads a PDF document, processes it, stores it in a vector database like Chroma, and then answers user questions based on that document's content. The core code for this is quite expressive:

// The AI will automatically call this Java function when needed. spring ai in action pdf github

Prompts are the inputs passed to the AI model. Spring AI provides a structured Prompt class that encapsulates a collection of Message objects (System, User, Assistant). PromptTemplate allows for dynamic string manipulation using placeholders, ensuring clean separation between prompt logic and application data. 3. Structured Outputs

Read private documents (PDFs, Markdown, Word docs) using Spring AI's DocumentReader . Spring AI in Action is a practical guide

4. Practical Implementation: Building a Smart REST Controller

If you know Spring Data JPA (which lets you swap Hibernate for EclipseLink with minimal changes), Spring AI lets you swap OpenAI for Anthropic Claude by changing one line in application.properties . A great GitHub example is the spring-ai-cli-chatbot project

Formats raw LLM responses directly into Java POJOs or Records. PromptTemplate

One of the hardest parts of LLM integration is forcing an unstructured model to return structured data like JSON. Spring AI solves this using StructuredOutputConverter .

spring-ai-action-template/ ├── .github/workflows/ # CI/CD pipelines (Docker builds, Test suites) ├── config/ # Shared infrastructure configuration (Docker Compose) │ └── docker-compose.yml # Local Postgres with pgvector, Redis, and Ollama ├── spring-ai-core/ # Core business domain definitions, entities, interfaces ├── spring-ai-ingestion/ # Batch or streaming jobs to parse PDFs, S3 files, DB records └── spring-ai-api/ # REST Controllers, Security configurations, WebFlux streaming ├── src/main/java/ └── src/main/resources/ └── application.yml Essential Repository Checklists

Introduction Enterprise Java development is undergoing a massive shift. Integrating Artificial Intelligence (AI) into enterprise applications used to require complex Python microservices, heavy data pipelines, and brittle REST connections. The emergence of changes this landscape entirely.