This Spring Boot application demonstrates how to use Spring AI with a vector store to create an intelligent FAQ system. The application reads FAQ data from a text file, stores it in a SimpleVectorStore for semantic search, and provides AI-powered responses through REST endpoints using Retrieval-Augmented Generation (RAG).
- FAQ Data Management: Reads FAQ data from a text file with structured format
- Vector Store Integration: Uses Spring AI's SimpleVectorStore for semantic search with persistent storage
- AI-Powered Responses: Integrates with OpenAI to provide intelligent answers based on FAQ context using RAG
- RESTful API: Provides endpoints for searching FAQs and asking questions
- Semantic Search: Finds relevant FAQ items using vector similarity with configurable thresholds
- Prompt Templates: Uses customizable prompt templates for AI responses
- Persistent Vector Store: Saves and loads vector store data to avoid rebuilding on each restart
- Java 17 or later
- OpenAI API key
- Gradle (for building the project)
-
Clone or download the project
-
Set your OpenAI API key:
- Set the
OPENAI_API_KEYenvironment variable, or - Update the
spring.ai.openai.api-keyproperty inapplication.properties
- Set the
-
Build the project:
./gradlew build
-
Run the application:
./gradlew bootRun
The application will start on http://localhost:8080
GET /api/faq/search?query={your-query}&topK={number}
Searches for similar FAQ items using semantic similarity.
Parameters:
query(required): The search querytopK(optional): Maximum number of results (default: 5)
Example:
GET /api/faq/search?query=How to create REST API&topK=3
Response: Returns a list of FAQ items with question, answer, and category.
GET /api/faq/ask?query={your-question}&topK={number}
Returns an AI-generated answer based on relevant FAQ context using RAG (Retrieval-Augmented Generation).
Parameters:
query(required): The question to asktopK(optional): Number of FAQ items to use as context (default: 3)
Example:
GET /api/faq/ask?query=How do I create a REST API in Spring Boot?&topK=3
Response:
{
"answer": "AI-generated answer based on FAQ context...",
"relevantFaqs": [
{
"question": "How do I create a REST API in Spring Boot?",
"answer": "You can create a REST API by using...",
"category": "Spring Boot"
}
],
"query": "How do I create a REST API in Spring Boot?"
}The FAQ data is stored in src/main/resources/faq.txt with the following format:
Q: Question text | A: Answer text | Category: Category name
Example:
Q: What is Spring Boot? | A: Spring Boot is a framework that simplifies Spring application development. | Category: Spring Boot
Key configuration properties in application.properties:
spring.ai.openai.api-key: Your OpenAI API key (can be set via environment variableOPENAI_API_KEY)spring.ai.openai.chat.options.model: OpenAI model to use (default: gpt-5-nano)app.faq.file-path: Path to the FAQ text file (default: classpath:/docs/faq.txt)app.vectorstore.similarity-threshold: Similarity threshold for vector search (default: 0.7)app.vectorstore.file-name: Vector store file name for persistence (default: vectorstore.json)
OPENAI_API_KEY: Your OpenAI API key (alternative to setting in application.properties)
logging.level.com.example: Logging level for application classes (default: INFO)logging.level.org.springframework.ai: Logging level for Spring AI (default: INFO)
- Initialization: On startup, the application checks for existing vector store data
- Vector Store Creation: If no vector store exists, FAQ items are read from the text file and converted to documents
- Embedding: Each FAQ item is embedded using OpenAI's embedding model and stored in SimpleVectorStore
- Persistence: Vector store data is saved to disk for faster subsequent startups
- Search: When a query is made, the system finds similar FAQ items using vector similarity with configurable thresholds
- RAG Response: Relevant FAQ context is combined with a prompt template and sent to OpenAI to generate intelligent responses
- Token Splitting: Large documents are automatically split using TokenTextSplitter to handle token limits
curl "http://localhost:8080/api/faq/search?query=authentication&topK=3"curl "http://localhost:8080/api/faq/ask?query=How%20do%20I%20handle%20security%20in%20Spring%20Boot?&topK=3"src/main/java/com/example/vectorstore/
├── VectorStoreApplication.java # Main Spring Boot application class
├── controller/
│ └── FaqController.java # REST controller for FAQ operations
├── model/
│ └── FaqItem.java # FAQ data model with parsing logic
└── service/
├── FaqService.java # FAQ file reading and parsing service
└── VectorStoreService.java # Vector store management and search
src/main/resources/
├── application.properties # Application configuration
├── docs/
│ ├── faq.txt # Main FAQ data file
│ └── olympic-faq.txt # Additional FAQ data (optional)
├── prompts/
│ └── faq.st # Prompt template for AI responses
└── data/
├── vectorstore.json # Persistent vector store data
└── olympic-vectorstore.json # Olympic FAQ vector store data
- Spring Boot 3.5.5
- Spring AI 1.0.1
- Spring AI OpenAI Starter
- Spring AI Vector Store
- Lombok (for reducing boilerplate code)
- Gradle (build tool)
- JUnit 5 (for testing)
- The application uses Spring AI version 1.0.1 (stable release)
- Vector store data is persisted to disk and loaded on startup for faster initialization
- The application supports multiple FAQ data sources (main FAQ and Olympic FAQ)
- Prompt templates are customizable and located in
src/main/resources/prompts/ - For production use, consider using external vector databases like Pinecone or Weaviate
- Make sure to set your OpenAI API key before running the application
- The application uses RAG (Retrieval-Augmented Generation) for intelligent responses
- Token splitting is automatically handled for large documents