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.