Hi Jason -- Google Cloud has a couple different offerings for building a RAG app. Based on your description above, Vertex AI Search & Conversation (VASC) might be a good pick. (This product went by a few other names, previously - Discovery Engine, Gen AI App Builder, Enterprise Search)
- First, you can create a VASC Data Store. You give VASC a Cloud Storage bucket with your text files. No need to pre-chunk yourself. VASC will take care of processing + creating the underlying vector embeddings in your Data Store. (Docs)
- Then you can write code to query your VASC Data Store with regular text queries. This is the prompt augmentation step for RAG.
- Once you get back search results and augment your prompt, you can then use the Gemini API on Vertex, for the multi turn chat. (Docs)
Here's a code example in Java Spring, from some recent experimentation I did.
import com.google.cloud.discoveryengine.v1.SearchRequest;
import com.google.cloud.discoveryengine.v1.SearchResponse;
import com.google.cloud.discoveryengine.v1.SearchServiceClient;
import com.google.cloud.discoveryengine.v1.SearchServiceSettings;
import com.google.cloud.discoveryengine.v1.ServingConfigName;
import com.google.cloud.vertexai.VertexAI;
import com.google.cloud.vertexai.api.GenerateContentResponse;
import com.google.cloud.vertexai.generativeai.preview.ChatSession;
import com.google.cloud.vertexai.generativeai.preview.GenerativeModel;
import com.google.cloud.vertexai.generativeai.preview.ResponseHandler;
...
@PostMapping(value = "/chat", consumes = "application/json", produces = "application/json")
public ChatMessage message(@RequestBody ChatMessage message) {
String userPrompt = message.getPrompt();
logger.info("💬 POST /chat, prompt: " + userPrompt);
// 1 - Query Vertex AI Search (VASC aka discoveryengine API) for matching
// documents
String projectId = "YOUR_PROJECT_ID";
String location = "global";
String collectionId = "default_collection";
String dataStoreId = "YOUR_VASC_DATASTORE";
String servingConfigId = "default_search";
String searchQuery = userPrompt;
String endpoint = String.format("discoveryengine.googleapis.com:443", location);
String augment = "";
try {
SearchServiceSettings settings = SearchServiceSettings.newBuilder().setEndpoint(endpoint).build();
SearchServiceClient searchServiceClient = SearchServiceClient.create(settings);
SearchRequest request = SearchRequest.newBuilder()
.setServingConfig(
ServingConfigName.formatProjectLocationCollectionDataStoreServingConfigName(
projectId, location, collectionId, dataStoreId, servingConfigId))
.setQuery(searchQuery)
.setPageSize(10)
.build();
SearchResponse response = searchServiceClient.search(request).getPage().getResponse();
for (SearchResponse.SearchResult element : response.getResultsList()) {
Struct derivedStructData = element.getDocument().getDerivedStructData();
Map<String, Value> fields = derivedStructData.getFieldsMap();
Value extractiveAnswersValue = fields.get("extractive_answers");
ListValue listValue = extractiveAnswersValue.getListValue();
Value firstValue = listValue.getValues(0);
Struct structValue = firstValue.getStructValue();
Map<String, Value> innerFields = structValue.getFieldsMap();
Value contentValue = innerFields.get("content");
String stringValue = contentValue.getStringValue();
augment += stringValue;
}
} catch (Exception e) {
logger.error("⚠️ Vertex AI ERROR: " + e);
}
// 2 - Use augmented prompt to query Gemini (Vertex AI API)
String geminiPrompt = "You are a helpful car manual chatbot. Answer the car owner's question about their car. Human prompt: "
+ userPrompt
+ ",\n Use the following grounding data as context. This came from the relevant vehicle owner's manual: "
+ augment;
logger.info("🔮 GEMINI PROMPT: " + geminiPrompt);
String geminiLocation = "us-central1";
String modelName = "gemini-pro";
try {
VertexAI vertexAI = new VertexAI(projectId, geminiLocation);
GenerateContentResponse response;
GenerativeModel model = new GenerativeModel(modelName, vertexAI);
ChatSession chatSession = new ChatSession(model);
response = chatSession.sendMessage(geminiPrompt);
String strResp = ResponseHandler.getText(response);
logger.info("🔮 GEMINI RESPONSE: " + strResp);
message.setResponse(strResp);
} catch (Exception e) {
logger.error("⚠️ GEMINI ERROR: " + e);
}
return message;
}
}
LinkedIn
Twitter
