Rename csv-chatbot to sql-chatbot

docs/modules/ROOT/pages/csv.adoc

@@ -10,7 +10,7 @@ When loading a CSV file, the process involves:
 2. Ingesting the set of documents using an appropriate *document splitter*.
 3. Storing the documents in the database.
 
-You can find a complete example in the https://github.com/quarkiverse/quarkus-langchain4j/tree/main/samples/csv-chatbot[GitHub Repository].
+You can find a complete example in the https://github.com/quarkiverse/quarkus-langchain4j/tree/main/samples/sql-chatbot[GitHub Repository].
 
 == From CSV to Documents
 
@@ -92,38 +92,44 @@ var ingestor = EmbeddingStoreIngestor.builder()
 ingestor.ingest(documents);
 ----
 
-== Implementing the Retriever
+== Implementing the Retrieval augmentor
 
-With the documents ingested, you can now implement the retriever:
+With the documents ingested, you can now implement the retrieval augmentor:
 
 [source, java]
 ----
 package io.quarkiverse.langchain4j.sample.chatbot;
 
-import java.util.List;
+import java.util.function.Supplier;
 
 import jakarta.enterprise.context.ApplicationScoped;
 
-import dev.langchain4j.data.segment.TextSegment;
 import dev.langchain4j.model.embedding.EmbeddingModel;
-import dev.langchain4j.retriever.EmbeddingStoreRetriever;
-import dev.langchain4j.retriever.Retriever;
-import io.quarkiverse.langchain4j.redis.RedisEmbeddingStore;
+import dev.langchain4j.rag.DefaultRetrievalAugmentor;
+import dev.langchain4j.rag.RetrievalAugmentor;
+import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
+import dev.langchain4j.store.embedding.EmbeddingStore;
 
 @ApplicationScoped
-public class RetrieverExample implements Retriever<TextSegment> {
+public class AugmentorExample implements Supplier<RetrievalAugmentor> {
 
-    private final EmbeddingStoreRetriever retriever;
+    private final EmbeddingStoreContentRetriever retriever;
 
-    RetrieverExample(RedisEmbeddingStore store, EmbeddingModel model) {
-        // Limit the number of documents to avoid exceeding the context size.
-        retriever = EmbeddingStoreRetriever.from(store, model, 10);
+    AugmentorExample(EmbeddingStore store, EmbeddingModel model) {
+        retriever = EmbeddingStoreContentRetriever.builder()
+                .embeddingModel(model)
+                .embeddingStore(store)
+                .maxResults(10)
+                .build();
     }
 
     @Override
-    public List<TextSegment> findRelevant(String s) {
-        return retriever.findRelevant(s);
+    public RetrievalAugmentor get() {
+        return DefaultRetrievalAugmentor.builder()
+                .contentRetriever(retriever)
+                .build();
     }
 }
+
 ----
 

pom.xml

@@ -194,7 +194,7 @@
         <module>samples/fraud-detection</module>
         <module>samples/chatbot</module>
         <module>samples/chatbot-easy-rag</module>
-        <module>samples/csv-chatbot</module>
+        <module>samples/sql-chatbot</module>
       </modules>
     </profile>
   </profiles>

samples/csv-chatbot/pom.xml → samples/sql-chatbot/pom.xml

@@ -3,8 +3,8 @@
     <modelVersion>4.0.0</modelVersion>
 
     <groupId>io.quarkiverse.langchain4j</groupId>
-    <artifactId>quarkus-langchain4j-sample-csv-chatbot</artifactId>
-    <name>Quarkus LangChain4j - Sample - Chatbot &amp; RAG loading a CSV file</name>
+    <artifactId>quarkus-langchain4j-sample-sql-chatbot</artifactId>
+    <name>Quarkus LangChain4j - Sample - Chatbot &amp; RAG from a SQL database</name>
     <version>1.0-SNAPSHOT</version>
 
     <properties>