Add RAG and update readme

README.md

@@ -22,6 +22,10 @@ The **GenAIApp** library is a Google Apps Script library designed for creating,
     - [Creating a Function](#creating-a-function)
     - [Configuring Parameters](#configuring-parameters)
   - [VectorStoreObject Class](#vectorstoreobject-class)
+    - [Create a Vector Store](#create-a-vector-store)
+    - [Upload Documents](#upload-documents)
+    - [Use a Vector Store in a Chat](#use-a-vector-store-in-a-chat)
+    - [Optional Controls](#optional-controls)
   - [Retrieving Knowledge from an OpenAI Vector Store](#retrieving-knowledge-from-an-openai-vector-store)
 - [Examples](#examples)
   - [Example 1: Send a Prompt and Get Completion](#example-1--send-a-prompt-and-get-completion)
@@ -256,16 +260,83 @@ functionObject.addParameter("rating", "number", "The minimum rating of movies to
 
 ## VectorStoreObject Class
 
-### Retrieving Knowledge from an OpenAI Vector Store
+The `VectorStoreObject` class lets you create and use vector stores for Retrieval Augmented Generation (RAG).
+Vector stores allow the model to retrieve relevant information from your own documents during a chat.
 
-Retrieve contextual information from a specific OpenAI vector search :
+GenAIApp supports:
 
-```js
-const vectorStoreObject = GenAIApp.newVectorStore()
-  .initializeFromId("your-vector-store-id");
-chat.addVectorStore(vectorStoreObject);
+- OpenAI vector stores
+
+- Google Vertex AI RAG
+
+### Create a Vector Store
+
+By default, GenAIApp creates an OpenAI vector store.
+
+```javascript
+GenAIApp.setOpenAIAPIKey(OPEN_AI_API_KEY);
+
+const vectorStore = GenAIApp.newVectorStore()
+  .setName("Support knowledge base")
+  .createVectorStore();
+
+const vectorStoreId = vectorStore.getId();
+```
+
+To use Google Vertex AI RAG instead, specify the provider and a GCS bucket:
+
+```javascript
+GenAIApp.setGeminiAuth("my-gcp-project-id", "europe-west4");
+
+// RAG endpoints use a dedicated region (may differ from Gemini auth region)
+GenAIApp.setRagRegion("europe-west4");
+
+const vectorStore = GenAIApp.newVectorStore("google")
+  .setName("Product documentation")
+  .setBucketName("gs://my-rag-bucket")
+  .createVectorStore();
+```
+
+> ⚠️  **Warning:** `setGeminiAuth()` configures authentication and region for Gemini
+> model calls, while `setRagRegion()` controls the region used for Vertex AI RAG
+> operations. Since RAG is not available in all regions, these two values may
+> differ and should both be set explicitly when using Google Vertex AI RAG.
+
+
+### Upload Documents
+
+Documents are provided as `Blob` objects and are automatically chunked and embedded.
+
+```javascript
+const blob = /* any Blob (PDF, text, image, etc.) */;
+vectorStore.uploadAndAttachFile(blob);
+```
+
+You can also upload multiple documents at once:
+
+```javascript
+vectorStore.uploadAndAttachFiles([blob1, blob2]);
+```
+
+### Use a Vector Store in a Chat
+
+```javascript
+const chat = GenAIApp.newChat();
+
+chat
+  .addMessage("How do I reset my password?")
+  .addVectorStores(vectorStoreId);
+
+const answer = chat.run();
+Logger.log(answer);
+```
+
+### Optional Controls
+
+```javascript
+chat.setMaxChunks(5);        // Limit retrieved chunks
+chat.onlyReturnChunks(true); // Return raw chunks instead of a text answer
 ```
-To find out more : [https://platform.openai.com/docs/api-reference/vector_stores/search](https://platform.openai.com/docs/api-reference/vector_stores/search)
 
 ## Examples
 
@@ -460,15 +531,18 @@ A `FunctionObject` represents a function that can be called by the chat.
 
 ### Vector Store Object
 
-A `VectorStoreObject` represents an OpenAI vector store.
+A `VectorStoreObject` represents a vector store (OpenAI or Google Vertex AI RAG).
 
 - `setName(newName)`: Set the vector store name.
 - `setDescription(newDesc)`: Set the description.
 - `setChunkingStrategy(maxChunkSize, chunkOverlap)`: Configure chunking before uploads.
+- `setBucketName(bucketAddress)`: Set the Google Cloud Storage bucket (Google RAG only).
 - `createVectorStore()`: Create the vector store.
 - `initializeFromId(vectorStoreId)`: Initialize from an existing vector store ID.
 - `getId()`: Get the vector store ID.
+- `getProvider()`: Get the underlying provider (`"openai"` or `"google"`).
 - `uploadAndAttachFile(blob, attributes)`: Upload a file and attach it to the store.
+- `uploadAndAttachFiles(blobs, attributesList)`: Upload and attach multiple files.
 - `listFiles()`: List files attached to the store.
 - `deleteFile(fileId)`: Delete a file from the store.
 - `deleteVectorStore()`: Delete the vector store.