feat: ensure retriever returns an image and send it to the LLM base64…

… encoded

.changeset/large-plums-drum.md

@@ -0,0 +1,6 @@
+---
+"llamaindex": patch
+---
+
+Added support for multi-modal RAG (retriever and query engine) incl. an example
+Fixed persisting and loading image vector stores 

examples/multimodal/rag.ts

@@ -1,5 +1,9 @@
 import {
+  CallbackManager,
+  ImageDocument,
+  ImageType,
   MultiModalResponseSynthesizer,
+  NodeWithScore,
   OpenAI,
   ServiceContext,
   VectorStoreIndex,
@@ -21,23 +25,34 @@ export async function createIndex(serviceContext: ServiceContext) {
 }
 
 async function main() {
+  let images: ImageType[] = [];
+  const callbackManager = new CallbackManager({
+    onRetrieve: ({ query, nodes }) => {
+      images = nodes
+        .filter(({ node }: NodeWithScore) => node instanceof ImageDocument)
+        .map(({ node }: NodeWithScore) => (node as ImageDocument).image);
+    },
+  });
   const llm = new OpenAI({ model: "gpt-4-vision-preview", maxTokens: 512 });
   const serviceContext = serviceContextFromDefaults({
     llm,
     chunkSize: 512,
     chunkOverlap: 20,
+    callbackManager,
   });
   const index = await createIndex(serviceContext);
 
   const queryEngine = index.asQueryEngine({
     responseSynthesizer: new MultiModalResponseSynthesizer({ serviceContext }),
-    // TODO: set imageSimilarityTopK: 1,
-    retriever: index.asRetriever({ similarityTopK: 2 }),
+    retriever: index.asRetriever({ similarityTopK: 3, imageSimilarityTopK: 1 }),
   });
   const result = await queryEngine.query(
-    "what are Vincent van Gogh's famous paintings",
+    "Tell me more about Vincent van Gogh's famous paintings",
+  );
+  console.log(result.response, "\n");
+  images.forEach((image) =>
+    console.log(`Image retrieved and used in inference: ${image.toString()}`),
   );
-  console.log(result.response);
 }
 
 main().catch(console.error);

examples/multimodal/retrieve.ts

@@ -5,7 +5,6 @@ import {
   TextNode,
   VectorStoreIndex,
 } from "llamaindex";
-import * as path from "path";
 
 export async function createIndex() {
   // set up vector store index with two vector stores, one for text, the other for images
@@ -37,7 +36,7 @@ async function main() {
       continue;
     }
     if (node instanceof ImageNode) {
-      console.log(`Image: ${path.join(__dirname, node.id_)}`);
+      console.log(`Image: ${node.getUrl()}`);
     } else if (node instanceof TextNode) {
       console.log("Text:", (node as TextNode).text.substring(0, 128));
     }

packages/core/package.json

@@ -10,6 +10,7 @@
     "@xenova/transformers": "^2.10.0",
     "assemblyai": "^4.0.0",
     "crypto-js": "^4.2.0",
+    "file-type": "^18.7.0",
     "js-tiktoken": "^1.0.8",
     "lodash": "^4.17.21",
     "mammoth": "^1.6.0",

packages/core/src/Node.ts

@@ -1,4 +1,5 @@
 import CryptoJS from "crypto-js";
+import path from "path";
 import { v4 as uuidv4 } from "uuid";
 
 export enum NodeRelationship {
@@ -304,6 +305,12 @@ export class ImageNode<T extends Metadata = Metadata> extends TextNode<T> {
   getType(): ObjectType {
     return ObjectType.IMAGE;
   }
+
+  getUrl(): URL {
+    // id_ stores the relative path, convert it to the URL of the file
+    const absPath = path.resolve(this.id_);
+    return new URL(`file://${absPath}`);
+  }
 }
 
 export class ImageDocument<T extends Metadata = Metadata> extends ImageNode<T> {

packages/core/src/embeddings/utils.ts

@@ -1,7 +1,7 @@
 import _ from "lodash";
 import { ImageType } from "../Node";
 import { DEFAULT_SIMILARITY_TOP_K } from "../constants";
-import { VectorStoreQueryMode } from "../storage";
+import { DEFAULT_FS, VectorStoreQueryMode } from "../storage";
 import { SimilarityType } from "./types";
 
 /**
@@ -185,6 +185,16 @@ export function getTopKMMREmbeddings(
   return [resultSimilarities, resultIds];
 }
 
+async function blobToDataUrl(input: Blob) {
+  const { fileTypeFromBuffer } = await import("file-type");
+  const buffer = Buffer.from(await input.arrayBuffer());
+  const type = await fileTypeFromBuffer(buffer);
+  if (!type) {
+    throw new Error("Unsupported image type");
+  }
+  return "data:" + type.mime + ";base64," + buffer.toString("base64");
+}
+
 export async function readImage(input: ImageType) {
   const { RawImage } = await import("@xenova/transformers");
   if (input instanceof Blob) {
@@ -195,3 +205,53 @@ export async function readImage(input: ImageType) {
     throw new Error(`Unsupported input type: ${typeof input}`);
   }
 }
+
+export async function imageToString(input: ImageType): Promise<string> {
+  if (input instanceof Blob) {
+    // if the image is a Blob, convert it to a base64 data URL
+    return await blobToDataUrl(input);
+  } else if (_.isString(input)) {
+    return input;
+  } else if (input instanceof URL) {
+    return input.toString();
+  } else {
+    throw new Error(`Unsupported input type: ${typeof input}`);
+  }
+}
+
+export function stringToImage(input: string): ImageType {
+  if (input.startsWith("data:")) {
+    // if the input is a base64 data URL, convert it back to a Blob
+    const base64Data = input.split(",")[1];
+    const byteArray = Buffer.from(base64Data, "base64");
+    return new Blob([byteArray]);
+  } else if (input.startsWith("http://") || input.startsWith("https://")) {
+    return new URL(input);
+  } else if (_.isString(input)) {
+    return input;
+  } else {
+    throw new Error(`Unsupported input type: ${typeof input}`);
+  }
+}
+
+export async function imageToDataUrl(input: ImageType): Promise<string> {
+  // first ensure, that the input is a Blob
+  if (
+    (input instanceof URL && input.protocol === "file:") ||
+    _.isString(input)
+  ) {
+    // string or file URL
+    const fs = DEFAULT_FS;
+    const dataBuffer = await fs.readFile(
+      input instanceof URL ? input.pathname : input,
+    );
+    input = new Blob([dataBuffer]);
+  } else if (!(input instanceof Blob)) {
+    if (input instanceof URL) {
+      throw new Error(`Unsupported URL with protocol: ${input.protocol}`);
+    } else {
+      throw new Error(`Unsupported input type: ${typeof input}`);
+    }
+  }
+  return await blobToDataUrl(input);
+}

packages/core/src/indices/vectorStore/VectorIndexRetriever.ts

@@ -1,10 +1,10 @@
-import { Event } from "../../callbacks/CallbackManager";
-import { DEFAULT_SIMILARITY_TOP_K } from "../../constants";
-import { BaseEmbedding } from "../../embeddings";
 import { globalsHelper } from "../../GlobalsHelper";
-import { Metadata, NodeWithScore } from "../../Node";
+import { ImageNode, Metadata, NodeWithScore } from "../../Node";
 import { BaseRetriever } from "../../Retriever";
 import { ServiceContext } from "../../ServiceContext";
+import { Event } from "../../callbacks/CallbackManager";
+import { DEFAULT_SIMILARITY_TOP_K } from "../../constants";
+import { BaseEmbedding } from "../../embeddings";
 import {
   VectorStoreQuery,
   VectorStoreQueryMode,
@@ -18,20 +18,23 @@ import { VectorStoreIndex } from "./VectorStoreIndex";
 
 export class VectorIndexRetriever implements BaseRetriever {
   index: VectorStoreIndex;
-  similarityTopK;
+  similarityTopK: number;
+  imageSimilarityTopK: number;
   private serviceContext: ServiceContext;
 
   constructor({
     index,
     similarityTopK,
+    imageSimilarityTopK,
   }: {
     index: VectorStoreIndex;
     similarityTopK?: number;
+    imageSimilarityTopK?: number;
   }) {
     this.index = index;
     this.serviceContext = this.index.serviceContext;
-
     this.similarityTopK = similarityTopK ?? DEFAULT_SIMILARITY_TOP_K;
+    this.imageSimilarityTopK = imageSimilarityTopK ?? DEFAULT_SIMILARITY_TOP_K;
   }
 
   async retrieve(
@@ -51,7 +54,11 @@ export class VectorIndexRetriever implements BaseRetriever {
     query: string,
     preFilters?: unknown,
   ): Promise<NodeWithScore[]> {
-    const q = await this.buildVectorStoreQuery(this.index.embedModel, query);
+    const q = await this.buildVectorStoreQuery(
+      this.index.embedModel,
+      query,
+      this.similarityTopK,
+    );
     const result = await this.index.vectorStore.query(q, preFilters);
     return this.buildNodeListFromQueryResult(result);
   }
@@ -64,6 +71,7 @@ export class VectorIndexRetriever implements BaseRetriever {
     const q = await this.buildVectorStoreQuery(
       this.index.imageEmbedModel,
       query,
+      this.imageSimilarityTopK,
     );
     const result = await this.index.imageVectorStore.query(q, preFilters);
     return this.buildNodeListFromQueryResult(result);
@@ -89,13 +97,14 @@ export class VectorIndexRetriever implements BaseRetriever {
   protected async buildVectorStoreQuery(
     embedModel: BaseEmbedding,
     query: string,
+    similarityTopK: number,
   ): Promise<VectorStoreQuery> {
     const queryEmbedding = await embedModel.getQueryEmbedding(query);
 
     return {
       queryEmbedding: queryEmbedding,
       mode: VectorStoreQueryMode.DEFAULT,
-      similarityTopK: this.similarityTopK,
+      similarityTopK: similarityTopK,
     };
   }
 
@@ -108,6 +117,12 @@ export class VectorIndexRetriever implements BaseRetriever {
       }
 
       const node = this.index.indexStruct.nodesDict[result.ids[i]];
+      // XXX: Hack, if it's an image node, we reconstruct the image from the URL
+      // Alternative: Store image in doc store and retrieve it here
+      if (node instanceof ImageNode) {
+        node.image = node.getUrl();
+      }
+
       nodesWithScores.push({
         node: node,
         score: result.similarities[i],

packages/core/src/synthesizers/MultiModalResponseSynthesizer.ts

@@ -1,8 +1,14 @@
 import { MessageContentDetail } from "../ChatEngine";
-import { MetadataMode, NodeWithScore, splitNodesByType } from "../Node";
+import {
+  ImageNode,
+  MetadataMode,
+  NodeWithScore,
+  splitNodesByType,
+} from "../Node";
 import { Response } from "../Response";
 import { ServiceContext, serviceContextFromDefaults } from "../ServiceContext";
 import { Event } from "../callbacks/CallbackManager";
+import { imageToDataUrl } from "../embeddings";
 import { TextQaPrompt, defaultTextQaPrompt } from "./../Prompt";
 import { BaseSynthesizer } from "./types";
 
@@ -34,15 +40,19 @@ export class MultiModalResponseSynthesizer implements BaseSynthesizer {
     // TODO: use builders to generate context
     const context = textChunks.join("\n\n");
     const textPrompt = this.textQATemplate({ context, query });
-    // TODO: get images from imageNodes
+    const images = await Promise.all(
+      imageNodes.map(async (node: ImageNode) => {
+        return {
+          type: "image_url",
+          image_url: {
+            url: await imageToDataUrl(node.image),
+          },
+        } as MessageContentDetail;
+      }),
+    );
     const prompt: MessageContentDetail[] = [
       { type: "text", text: textPrompt },
-      {
-        type: "image_url",
-        image_url: {
-          url: "https://upload.wikimedia.org/wikipedia/commons/b/b0/Vincent_van_Gogh_%281853-1890%29_Caf%C3%A9terras_bij_nacht_%28place_du_Forum%29_Kr%C3%B6ller-M%C3%BCller_Museum_Otterlo_23-8-2016_13-35-40.JPG",
-        },
-      },
+      ...images,
     ];
     let response = await this.serviceContext.llm.complete(prompt, parentEvent);
     return new Response(response.message.content, nodes);