Demonstrate the concepts of building a Spring AI chatbot with both general knowledge RAG and an FAQ section into a single comprehensive article.
Building a Powerful Spring AI Chatbot with RAG and FAQ
Large Language Models (LLMs) offer incredible potential for building intelligent chatbots. However, to create truly useful and context-aware chatbots, especially for specific domains, we often need to ground their responses in relevant knowledge. This is where Retrieval-Augmented Generation (RAG) comes into play. Furthermore, for common inquiries, a direct Frequently Asked Questions (FAQ) mechanism can provide faster and more accurate answers. This article will guide you through building a Spring AI chatbot that leverages both RAG for general knowledge and a dedicated FAQ section.
Core Concepts:

• Large Language Models (LLMs): The AI brains behind the chatbot, capable of generating human-like text. Spring AI provides abstractions to interact with various LLM providers.
• Retrieval-Augmented Generation (RAG): A process of augmenting the LLM’s knowledge by retrieving relevant documents from a knowledge base and including them in the prompt. This allows the chatbot to answer questions based on specific information.
• Document Loading: The process of ingesting your knowledge base (e.g., PDFs, text files, web pages) into a format Spring AI can process.
• Text Embedding: Converting text into numerical vector representations that capture its semantic meaning. This enables efficient similarity searching.
• Vector Store: A database optimized for storing and querying vector embeddings.
• Retrieval: The process of searching the vector store for embeddings similar to the user’s query.
• Prompt Engineering: Crafting effective prompts that guide the LLM to generate accurate and relevant responses, often including retrieved context.
• Frequently Asked Questions (FAQ): A predefined set of common questions and their answers, allowing for direct retrieval for common inquiries.
  Setting Up Your Spring AI Project:
• Create a Spring Boot Project: Start with a new Spring Boot project using Spring Initializr (https://start.spring.io/). Include the necessary Spring AI dependencies for your chosen LLM provider (e.g., spring-ai-openai, spring-ai-anthropic) and a vector store implementation (e.g., spring-ai-chromadb).
  org.springframework.ai spring-ai-openai runtime org.springframework.ai spring-ai-chromadb org.springframework.boot spring-boot-starter-web com.fasterxml.jackson.core jackson-databind org.springframework.boot spring-boot-starter-test test
• Configure API Keys and Vector Store: Configure your LLM provider’s API key and the settings for your chosen vector store in your application.properties or application.yml file.
  spring.ai.openai.api-key=YOUR_OPENAI_API_KEY
  spring.ai.openai.embedding.options.model=text-embedding-3-small

spring.ai.vectorstore.chroma.host=localhost
spring.ai.vectorstore.chroma.port=8000

Implementing RAG for General Knowledge:

• Document Loading and Indexing Service: Create a service to load your knowledge base documents, embed their content, and store them in the vector store.
  @Service
  public class DocumentService { private final PdfLoader pdfLoader;
  private final EmbeddingClient embeddingClient;
  private final VectorStore vectorStore; public DocumentService(PdfLoader pdfLoader, EmbeddingClient embeddingClient, VectorStore vectorStore) {
  this.pdfLoader = pdfLoader;
  this.embeddingClient = embeddingClient;
  this.vectorStore = vectorStore;
  } @PostConstruct
  public void loadAndIndexDocuments() throws IOException {
  List documents = pdfLoader.load(new FileSystemResource(“path/to/your/documents.pdf”));
  List embeddings = embeddingClient.embed(documents.stream().map(Document::getContent).toList());
  vectorStore.add(embeddings, documents);
  System.out.println(“General knowledge documents loaded and indexed.”);
  }
  }
• Chat Endpoint with RAG: Implement your chat endpoint to retrieve relevant documents based on the user’s query and include them in the prompt sent to the LLM.
  @RestController
  public class ChatController { private final ChatClient chatClient;
  private final VectorStore vectorStore;
  private final EmbeddingClient embeddingClient; public ChatController(ChatClient chatClient, VectorStore vectorStore, EmbeddingClient embeddingClient) {
  this.chatClient = chatClient;
  this.vectorStore = vectorStore;
  this.embeddingClient = embeddingClient;
  } @GetMapping(“/chat”)
  public String chat(@RequestParam(“message”) String message) {
  Embedding queryEmbedding = embeddingClient.embed(message);
  List searchResults = vectorStore.similaritySearch(queryEmbedding.getVector(), 3); String context = searchResults.stream() .map(SearchResult::getContent) .collect(Collectors.joining("\n\n")); Prompt prompt = new PromptTemplate(""" Answer the question based on the context provided. Context: {context} Question: {question} """) .create(Map.of("context", context, "question", message));ChatResponse response = chatClient.call(prompt); return response.getResult().getOutput().getContent(); }
  }

Integrating an FAQ Section:

• Create FAQ Data: Define your frequently asked questions and answers (e.g., in faq.json in your resources folder).
  [
  {
  “question”: “What are your hours of operation?”,
  “answer”: “Our business hours are Monday to Friday, 9 AM to 5 PM.”
  },
  {
  “question”: “Where are you located?”,
  “answer”: “We are located at 123 Main Street, Bentonville, AR.”
  },
  {
  “question”: “How do I contact customer support?”,
  “answer”: “You can contact our customer support team by emailing support@example.com or calling us at (555) 123-4567.”
  }
  ]
• FAQ Loading and Indexing Service: Create a service to load and index your FAQ data in the vector store.
  @Service
  public class FAQService { private final EmbeddingClient embeddingClient;
  private final VectorStore vectorStore;
  private final ObjectMapper objectMapper; public FAQService(EmbeddingClient embeddingClient, VectorStore vectorStore, ObjectMapper objectMapper) {
  this.embeddingClient = embeddingClient;
  this.vectorStore = vectorStore;
  this.objectMapper = objectMapper;
  } @PostConstruct
  public void loadAndIndexFAQs() throws IOException {
  Resource faqResource = new ClassPathResource(“faq.json”);
  List faqEntries = objectMapper.readValue(faqResource.getInputStream(), new TypeReference>() {}); List<Document> faqDocuments = faqEntries.stream() .map(faq -> new Document(faq.getQuestion(), Map.of("answer", faq.getAnswer()))) .toList(); List<Embedding> faqEmbeddings = embeddingClient.embed(faqDocuments.stream().map(Document::getContent).toList()); vectorStore.add(faqEmbeddings, faqDocuments); System.out.println("FAQ data loaded and indexed."); } public record FAQEntry(String question, String answer) {}
  }
• Prioritize FAQ in Chat Endpoint: Modify your chat endpoint to first check if the user’s query closely matches an FAQ before resorting to general knowledge RAG.
  @RestController
  public class ChatController { private final ChatClient chatClient;
  private final VectorStore vectorStore;
  private final EmbeddingClient embeddingClient; public ChatController(ChatClient chatClient, VectorStore vectorStore, EmbeddingClient embeddingClient) {
  this.chatClient = chatClient;
  this.vectorStore = vectorStore;
  this.embeddingClient = embeddingClient;
  } @GetMapping(“/chat”)
  public String chat(@RequestParam(“message”) String message) {
  Embedding queryEmbedding = embeddingClient.embed(message); // Search FAQ first List<SearchResult> faqSearchResults = vectorStore.similaritySearch(queryEmbedding.getVector(), 1); if (!faqSearchResults.isEmpty() && faqSearchResults.get(0).getScore() > 0.85) { return (String) faqSearchResults.get(0).getMetadata().get("answer"); } // If no good FAQ match, proceed with general knowledge RAG List<SearchResult> knowledgeBaseResults = vectorStore.similaritySearch(queryEmbedding.getVector(), 3); String context = knowledgeBaseResults.stream() .map(SearchResult::getContent) .collect(Collectors.joining("\n\n")); Prompt prompt = new PromptTemplate(""" Answer the question based on the context provided. Context: {context} Question: {question} """) .create(Map.of("context", context, "question", message));ChatResponse response = chatClient.call(prompt); return response.getResult().getOutput().getContent(); }
  }

Conclusion:
By combining the power of RAG with a dedicated FAQ section, you can build a Spring AI chatbot that is both knowledgeable about a broad range of topics (through RAG) and efficient in answering common questions directly. This approach leads to a more robust, accurate, and user-friendly chatbot experience. Remember to adapt the code and configurations to your specific data sources and requirements, and experiment with similarity thresholds to optimize the performance of your FAQ retrieval.