Release 0.5.5 (#1046)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

apps/docs/CHANGELOG.md

@@ -1,5 +1,27 @@
 # docs
 
+## 0.0.46
+
+### Patch Changes
+
+- Updated dependencies [b974eea]
+  - llamaindex@0.5.5
+
+## 0.0.45
+
+### Patch Changes
+
+- Updated dependencies [1a65ead]
+  - llamaindex@0.5.4
+
+## 0.0.44
+
+### Patch Changes
+
+- Updated dependencies [9bbbc67]
+- Updated dependencies [b3681bf]
+  - llamaindex@0.5.3
+
 ## 0.0.43
 
 ### Patch Changes

apps/docs/docs/modules/data_loaders/discord.mdx

@@ -0,0 +1,34 @@
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../../examples/readers/src/discord";
+
+# DiscordReader
+
+DiscordReader is a simple data loader that reads all messages in a given Discord channel and returns them as Document objects.
+It uses the [@discordjs/rest](https://github.com/discordjs/discord.js/tree/main/packages/rest) library to fetch the messages.
+
+## Usage
+
+First step is to create a Discord Application and generating a bot token [here](https://discord.com/developers/applications).
+In your Discord Application, go to the `OAuth2` tab and generate an invite URL by selecting `bot` and click `Read Messages/View Channels` as wells as `Read Message History`.
+This will invite the bot with the necessary permissions to read messages.
+Copy the URL in your browser and select the server you want your bot to join.
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>
+
+### Params
+
+#### DiscordReader()
+
+- `discordToken?`: The Discord bot token.
+- `makeRequest?`: Optionally provide a custom request function for edge environments, e.g. `fetch`. See discord.js for more info.
+
+#### DiscordReader.loadData
+
+- `channelIDs`: The ID(s) of discord channels as an array of strings.
+- `limit?`: Optionally limit the number of messages to read
+- `additionalInfo?`: An optional flag to include embedded messages and attachment urls in the document.
+- `oldestFirst?`: An optional flag to return the oldest messages first.
+
+## API Reference
+
+- [DiscordReader](../../api/classes/DiscordReader.md)

apps/docs/docs/modules/data_loaders/llama_parse/index.mdx

@@ -42,10 +42,13 @@ They can be divided into two groups.
 - `fastMode?` Optional. Set to true to use the fast mode. This mode will skip OCR of images, and table/heading reconstruction. Note: Non-compatible with `gpt4oMode`.
 - `doNotUnrollColumns?` Optional. Set to true to keep the text according to document layout. Reduce reconstruction accuracy, and LLMs/embeddings performances in most cases.
 - `pageSeparator?` Optional. The page separator to use. Defaults is `\\n---\\n`.
-- `gpt4oMode` set to true to use GPT-4o to extract content. Default is `false`.
-- `gpt4oApiKey?` Optional. Set the GPT-4o API key. Lowers the cost of parsing by using your own API key. Your OpenAI account will be charged. Can also be set in the environment variable `LLAMA_CLOUD_GPT4O_API_KEY`.
+- `gpt4oMode` Deprecated. Use vendorMultimodal params. Set to true to use GPT-4o to extract content. Default is `false`.
+- `gpt4oApiKey?` Deprecated. Use vendorMultimodal params. Optional. Set the GPT-4o API key. Lowers the cost of parsing by using your own API key. Your OpenAI account will be charged. Can also be set in the environment variable `LLAMA_CLOUD_GPT4O_API_KEY`.
 - `boundingBox?` Optional. Specify an area of the document to parse. Expects the bounding box margins as a string in clockwise order, e.g. `boundingBox = "0.1,0,0,0"` to not parse the top 10% of the document.
 - `targetPages?` Optional. Specify which pages to parse by specifying them as a comma-separated list. First page is `0`.
+- `useVendorMultimodalModel` set to true to use a multimodal model. Default is `false`.
+- `vendorMultimodalModel?` Optional. Specify which multimodal model to use. Default is GPT4o. See [here](https://docs.cloud.llamaindex.ai/llamaparse/features/multimodal) for a list of available models and cost.
+- `vendorMultimodalApiKey?` Optional. Set the multimodal model API key. Can also be set in the environment variable `LLAMA_CLOUD_VENDOR_MULTIMODAL_API_KEY`.
 - `numWorkers` as in the python version, is set in `SimpleDirectoryReader`. Default is 1.
 
 ### LlamaParse with SimpleDirectoryReader

apps/docs/docs/modules/query_engines/metadata_filtering.md

@@ -75,7 +75,7 @@ const queryEngine = index.asQueryEngine({
       {
         key: "dogId",
         value: "2",
-        filterType: "ExactMatch",
+        operator: "==",
       },
     ],
   },
@@ -135,7 +135,7 @@ async function main() {
           {
             key: "dogId",
             value: "2",
-            filterType: "ExactMatch",
+            operator: "==",
           },
         ],
       },

apps/docs/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docs",
-  "version": "0.0.43",
+  "version": "0.0.46",
   "private": true,
   "scripts": {
     "docusaurus": "docusaurus",
@@ -34,10 +34,10 @@
     "@docusaurus/types": "3.4.0",
     "@tsconfig/docusaurus": "2.0.3",
     "@types/node": "^20.12.11",
-    "docusaurus-plugin-typedoc": "1.0.2",
-    "typedoc": "0.26.3",
+    "docusaurus-plugin-typedoc": "1.0.3",
+    "typedoc": "0.26.4",
     "typedoc-plugin-markdown": "4.1.2",
-    "typescript": "^5.5.2"
+    "typescript": "^5.5.3"
   },
   "browserslist": {
     "production": [

examples/chromadb/preFilters.ts

@@ -40,7 +40,7 @@ async function main() {
           {
             key: "dogId",
             value: "2",
-            filterType: "ExactMatch",
+            operator: "==",
           },
         ],
       },

examples/metadata-filter/milvus.ts

@@ -0,0 +1,40 @@
+import { MilvusVectorStore, VectorStoreIndex } from "llamaindex";
+
+const collectionName = "movie_reviews";
+
+async function main() {
+  try {
+    const milvus = new MilvusVectorStore({ collection: collectionName });
+    const index = await VectorStoreIndex.fromVectorStore(milvus);
+    const retriever = index.asRetriever({ similarityTopK: 20 });
+
+    console.log("\n=====\nQuerying the index with filters");
+    const queryEngineWithFilters = index.asQueryEngine({
+      retriever,
+      preFilters: {
+        filters: [
+          {
+            key: "document_id",
+            value: "./data/movie_reviews.csv_37",
+            operator: "==",
+          },
+          {
+            key: "document_id",
+            value: "./data/movie_reviews.csv_37",
+            operator: "!=",
+          },
+        ],
+        condition: "or",
+      },
+    });
+    const resultAfterFilter = await queryEngineWithFilters.query({
+      query: "Get all movie titles.",
+    });
+    console.log(`Query from ${resultAfterFilter.sourceNodes?.length} nodes`);
+    console.log(resultAfterFilter.response);
+  } catch (e) {
+    console.error(e);
+  }
+}
+
+void main();

examples/metadata-filter/simple.ts

@@ -0,0 +1,143 @@
+import {
+  Document,
+  Settings,
+  SimpleDocumentStore,
+  VectorStoreIndex,
+  storageContextFromDefaults,
+} from "llamaindex";
+
+Settings.callbackManager.on("retrieve-end", (event) => {
+  const { nodes } = event.detail;
+  console.log("Number of retrieved nodes:", nodes.length);
+});
+
+async function getDataSource() {
+  const docs = [
+    new Document({
+      text: "The dog is brown",
+      metadata: {
+        dogId: "1",
+        private: true,
+      },
+    }),
+    new Document({
+      text: "The dog is yellow",
+      metadata: {
+        dogId: "2",
+        private: false,
+      },
+    }),
+    new Document({
+      text: "The dog is red",
+      metadata: {
+        dogId: "3",
+        private: false,
+      },
+    }),
+  ];
+  const storageContext = await storageContextFromDefaults({
+    persistDir: "./cache",
+  });
+  const numberOfDocs = Object.keys(
+    (storageContext.docStore as SimpleDocumentStore).toDict(),
+  ).length;
+  if (numberOfDocs === 0) {
+    // Generate the data source if it's empty
+    return await VectorStoreIndex.fromDocuments(docs, {
+      storageContext,
+    });
+  }
+  return await VectorStoreIndex.init({
+    storageContext,
+  });
+}
+
+async function main() {
+  const index = await getDataSource();
+  console.log(
+    "=============\nQuerying index with no filters. The output should be any color.",
+  );
+  const queryEngineNoFilters = index.asQueryEngine({
+    similarityTopK: 3,
+  });
+  const noFilterResponse = await queryEngineNoFilters.query({
+    query: "What is the color of the dog?",
+  });
+  console.log("No filter response:", noFilterResponse.toString());
+
+  console.log(
+    "\n=============\nQuerying index with dogId 2 and private false. The output always should be red.",
+  );
+  const queryEngineEQ = index.asQueryEngine({
+    preFilters: {
+      filters: [
+        {
+          key: "private",
+          value: "false",
+          operator: "==",
+        },
+        {
+          key: "dogId",
+          value: "3",
+          operator: "==",
+        },
+      ],
+    },
+    similarityTopK: 3,
+  });
+  const responseEQ = await queryEngineEQ.query({
+    query: "What is the color of the dog?",
+  });
+  console.log("Filter with dogId 2 response:", responseEQ.toString());
+
+  console.log(
+    "\n=============\nQuerying index with dogId IN (1, 3). The output should be brown and red.",
+  );
+  const queryEngineIN = index.asQueryEngine({
+    preFilters: {
+      filters: [
+        {
+          key: "dogId",
+          value: ["1", "3"],
+          operator: "in",
+        },
+      ],
+    },
+    similarityTopK: 3,
+  });
+  const responseIN = await queryEngineIN.query({
+    query: "What is the color of the dog?",
+  });
+  console.log("Filter with dogId IN (1, 3) response:", responseIN.toString());
+
+  console.log(
+    "\n=============\nQuerying index with dogId IN (1, 3). The output should be any.",
+  );
+  const queryEngineOR = index.asQueryEngine({
+    preFilters: {
+      filters: [
+        {
+          key: "private",
+          value: "false",
+          operator: "==",
+        },
+        {
+          key: "dogId",
+          value: ["1", "3"],
+          operator: "in",
+        },
+      ],
+      condition: "or",
+    },
+    similarityTopK: 3,
+  });
+  const responseOR = await queryEngineOR.query({
+    query: "What is the color of the dog?",
+  });
+  console.log(
+    "Filter with dogId with OR operator response:",
+    responseOR.toString(),
+  );
+}
+
+void main();

examples/package.json

@@ -9,7 +9,7 @@
     "@llamaindex/core": "^0.1.0",
     "@notionhq/client": "^2.2.15",
     "@pinecone-database/pinecone": "^2.2.2",
-    "@zilliz/milvus2-sdk-node": "^2.4.2",
+    "@zilliz/milvus2-sdk-node": "^2.4.4",
     "chromadb": "^1.8.1",
     "commander": "^12.1.0",
     "dotenv": "^16.4.5",
@@ -21,7 +21,7 @@
   "devDependencies": {
     "@types/node": "^20.14.1",
     "tsx": "^4.15.6",
-    "typescript": "^5.5.2"
+    "typescript": "^5.5.3"
   },
   "scripts": {
     "lint": "eslint ."

examples/qdrantdb/preFilters.ts

@@ -64,7 +64,7 @@ async function main() {
           {
             key: "dogId",
             value: "2",
-            filterType: "ExactMatch",
+            operator: "==",
           },
         ],
       },

examples/readers/package.json

@@ -12,7 +12,8 @@
     "start:llamaparse": "node --import tsx ./src/llamaparse.ts",
     "start:notion": "node --import tsx ./src/notion.ts",
     "start:llamaparse-dir": "node --import tsx ./src/simple-directory-reader-with-llamaparse.ts",
-    "start:llamaparse-json": "node --import tsx ./src/llamaparse-json.ts"
+    "start:llamaparse-json": "node --import tsx ./src/llamaparse-json.ts",
+    "start:discord": "node --import tsx ./src/discord.ts"
   },
   "dependencies": {
     "llamaindex": "*"
@@ -20,6 +21,6 @@
   "devDependencies": {
     "@types/node": "^20.12.11",
     "tsx": "^4.15.6",
-    "typescript": "^5.5.2"
+    "typescript": "^5.5.3"
   }
 }

examples/readers/src/discord.ts

@@ -0,0 +1,20 @@
+import { DiscordReader } from "llamaindex";
+
+async function main() {
+  // Create an instance of the DiscordReader. Set token here or DISCORD_TOKEN environment variable
+  const discordReader = new DiscordReader();
+
+  // Specify the channel IDs you want to read messages from as an arry of strings
+  const channelIds = ["721374320794009630", "719596376261918720"];
+
+  // Specify the number of messages to fetch per channel
+  const limit = 10;
+
+  // Load messages from the specified channel
+  const messages = await discordReader.loadData(channelIds, limit, true);
+
+  // Print out the messages
+  console.log(messages);
+}
+
+main().catch(console.error);

package.json

@@ -21,19 +21,19 @@
     "@changesets/cli": "^2.27.5",
     "@typescript-eslint/eslint-plugin": "^7.13.1",
     "eslint": "^8.57.0",
-    "eslint-config-next": "^14.2.4",
+    "eslint-config-next": "^14.2.5",
     "eslint-config-prettier": "^9.1.0",
     "eslint-config-turbo": "^2.0.5",
-    "eslint-plugin-react": "7.34.1",
+    "eslint-plugin-react": "7.34.3",
     "husky": "^9.0.11",
     "lint-staged": "^15.2.7",
     "madge": "^7.0.0",
     "prettier": "^3.3.2",
-    "prettier-plugin-organize-imports": "^3.2.4",
+    "prettier-plugin-organize-imports": "^4.0.0",
     "turbo": "^2.0.5",
-    "typescript": "^5.5.2"
+    "typescript": "^5.5.3"
   },
-  "packageManager": "pnpm@9.4.0",
+  "packageManager": "pnpm@9.5.0",
   "pnpm": {
     "overrides": {
       "trim": "1.0.1",

packages/autotool/examples/01_node/package.json

@@ -5,7 +5,7 @@
   "dependencies": {
     "@llamaindex/autotool": "workspace:*",
     "llamaindex": "workspace:*",
-    "openai": "^4.52.0"
+    "openai": "^4.52.5"
   },
   "devDependencies": {
     "tsx": "^4.15.6"

packages/autotool/examples/02_nextjs/CHANGELOG.md

@@ -1,5 +1,30 @@
 # @llamaindex/autotool-02-next-example
 
+## 0.1.30
+
+### Patch Changes
+
+- Updated dependencies [b974eea]
+  - llamaindex@0.5.5
+  - @llamaindex/autotool@2.0.0
+
+## 0.1.29
+
+### Patch Changes
+
+- Updated dependencies [1a65ead]
+  - llamaindex@0.5.4
+  - @llamaindex/autotool@2.0.0
+
+## 0.1.28
+
+### Patch Changes
+
+- Updated dependencies [9bbbc67]
+- Updated dependencies [b3681bf]
+  - llamaindex@0.5.3
+  - @llamaindex/autotool@2.0.0
+
 ## 0.1.27
 
 ### Patch Changes

packages/autotool/examples/02_nextjs/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/autotool-02-next-example",
   "private": true,
-  "version": "0.1.27",
+  "version": "0.1.30",
   "scripts": {
     "dev": "next dev",
     "build": "next build",
@@ -14,7 +14,7 @@
     "class-variance-authority": "^0.7.0",
     "dotenv": "^16.3.1",
     "llamaindex": "workspace:*",
-    "lucide-react": "^0.378.0",
+    "lucide-react": "^0.407.0",
     "next": "14.3.0-canary.51",
     "react": "^18.3.1",
     "react-dom": "^18.3.1",
@@ -32,6 +32,6 @@
     "cross-env": "^7.0.3",
     "postcss": "^8.4.32",
     "tailwindcss": "^3.4.4",
-    "typescript": "^5.5.2"
+    "typescript": "^5.5.3"
   }
 }

packages/autotool/package.json

@@ -47,11 +47,11 @@
   "dependencies": {
     "@swc/core": "^1.6.3",
     "jotai": "^2.8.3",
-    "typedoc": "^0.25.13",
+    "typedoc": "^0.26.4",
     "unplugin": "^1.10.1"
   },
   "peerDependencies": {
-    "llamaindex": "^0.5.2",
+    "llamaindex": "^0.5.5",
     "openai": "^4",
     "typescript": "^4"
   },
@@ -72,11 +72,11 @@
     "@types/node": "^20.12.11",
     "bunchee": "5.3.0-beta.0",
     "llamaindex": "workspace:*",
-    "next": "14.2.3",
+    "next": "14.2.5",
     "rollup": "^4.18.0",
     "tsx": "^4.15.6",
-    "typescript": "^5.5.2",
-    "vitest": "^1.6.0",
+    "typescript": "^5.5.3",
+    "vitest": "^2.0.2",
     "webpack": "^5.92.1"
   }
 }

packages/community/CHANGELOG.md

@@ -1,5 +1,19 @@
 # @llamaindex/community
 
+## 0.0.22
+
+### Patch Changes
+
+- Updated dependencies [b974eea]
+  - @llamaindex/core@0.1.2
+
+## 0.0.21
+
+### Patch Changes
+
+- Updated dependencies [b3681bf]
+  - @llamaindex/core@0.1.1
+
 ## 0.0.20
 
 ### Patch Changes

packages/community/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/community",
   "description": "Community package for LlamaIndexTS",
-  "version": "0.0.20",
+  "version": "0.0.22",
   "type": "module",
   "types": "dist/type/index.d.ts",
   "main": "dist/cjs/index.js",
@@ -46,7 +46,7 @@
     "bunchee": "5.3.0-beta.0"
   },
   "dependencies": {
-    "@aws-sdk/client-bedrock-runtime": "^3.600.0",
+    "@aws-sdk/client-bedrock-runtime": "^3.613.0",
     "@llamaindex/core": "workspace:*"
   }
 }

packages/core/CHANGELOG.md

@@ -1,5 +1,17 @@
 # @llamaindex/core
 
+## 0.1.2
+
+### Patch Changes
+
+- b974eea: Add support for Metadata filters
+
+## 0.1.1
+
+### Patch Changes
+
+- b3681bf: fix: DataCloneError when using FunctionTool
+
 ## 0.1.0
 
 ### Minor Changes

packages/core/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/core",
   "type": "module",
-  "version": "0.1.0",
+  "version": "0.1.2",
   "description": "LlamaIndex Core Module",
   "exports": {
     "./llms": {
@@ -32,6 +32,20 @@
         "default": "./dist/decorator/index.js"
       }
     },
+    "./embeddings": {
+      "require": {
+        "types": "./dist/embeddings/index.d.cts",
+        "default": "./dist/embeddings/index.cjs"
+      },
+      "import": {
+        "types": "./dist/embeddings/index.d.ts",
+        "default": "./dist/embeddings/index.js"
+      },
+      "default": {
+        "types": "./dist/embeddings/index.d.ts",
+        "default": "./dist/embeddings/index.js"
+      }
+    },
     "./global": {
       "require": {
         "types": "./dist/global/index.d.cts",

packages/core/src/embeddings/base.ts

@@ -1,9 +1,8 @@
-import type { MessageContentDetail } from "@llamaindex/core/llms";
-import type { BaseNode } from "@llamaindex/core/schema";
-import { MetadataMode } from "@llamaindex/core/schema";
-import { extractSingleText } from "@llamaindex/core/utils";
 import { type Tokenizers } from "@llamaindex/env";
-import type { TransformComponent } from "../ingestion/types.js";
+import type { MessageContentDetail } from "../llms";
+import type { TransformComponent } from "../schema";
+import { BaseNode, MetadataMode } from "../schema";
+import { extractSingleText } from "../utils";
 import { truncateMaxTokens } from "./tokenizer.js";
 import { SimilarityType, similarity } from "./utils.js";
 
@@ -17,7 +16,13 @@ export type EmbeddingInfo = {
   tokenizer?: Tokenizers;
 };
 
-export abstract class BaseEmbedding implements TransformComponent {
+export type BaseEmbeddingOptions = {
+  logProgress?: boolean;
+};
+
+export abstract class BaseEmbedding
+  implements TransformComponent<BaseEmbeddingOptions>
+{
   embedBatchSize = DEFAULT_EMBED_BATCH_SIZE;
   embedInfo?: EmbeddingInfo;
 
@@ -45,7 +50,7 @@ export abstract class BaseEmbedding implements TransformComponent {
    * Optionally override this method to retrieve multiple embeddings in a single request
    * @param texts
    */
-  async getTextEmbeddings(texts: string[]): Promise<Array<number[]>> {
+  getTextEmbeddings = async (texts: string[]): Promise<Array<number[]>> => {
     const embeddings: number[][] = [];
 
     for (const text of texts) {
@@ -54,7 +59,7 @@ export abstract class BaseEmbedding implements TransformComponent {
     }
 
     return embeddings;
-  }
+  };
 
   /**
    * Get embeddings for a batch of texts
@@ -63,22 +68,23 @@ export abstract class BaseEmbedding implements TransformComponent {
    */
   async getTextEmbeddingsBatch(
     texts: string[],
-    options?: {
-      logProgress?: boolean;
-    },
+    options?: BaseEmbeddingOptions,
   ): Promise<Array<number[]>> {
     return await batchEmbeddings(
       texts,
-      this.getTextEmbeddings.bind(this),
+      this.getTextEmbeddings,
       this.embedBatchSize,
       options,
     );
   }
 
-  async transform(nodes: BaseNode[], _options?: any): Promise<BaseNode[]> {
+  async transform(
+    nodes: BaseNode[],
+    options?: BaseEmbeddingOptions,
+  ): Promise<BaseNode[]> {
     const texts = nodes.map((node) => node.getContent(MetadataMode.EMBED));
 
-    const embeddings = await this.getTextEmbeddingsBatch(texts, _options);
+    const embeddings = await this.getTextEmbeddingsBatch(texts, options);
 
     for (let i = 0; i < nodes.length; i++) {
       nodes[i].embedding = embeddings[i];
@@ -104,9 +110,7 @@ export async function batchEmbeddings<T>(
   values: T[],
   embedFunc: EmbedFunc<T>,
   chunkSize: number,
-  options?: {
-    logProgress?: boolean;
-  },
+  options?: BaseEmbeddingOptions,
 ): Promise<Array<number[]>> {
   const resultEmbeddings: Array<number[]> = [];
 

packages/core/src/embeddings/index.ts

@@ -0,0 +1,4 @@
+export { BaseEmbedding, batchEmbeddings } from "./base";
+export type { BaseEmbeddingOptions, EmbeddingInfo } from "./base";
+export { truncateMaxTokens } from "./tokenizer";
+export { DEFAULT_SIMILARITY_TOP_K, SimilarityType, similarity } from "./utils";

packages/core/src/embeddings/utils.ts

@@ -0,0 +1,64 @@
+export const DEFAULT_SIMILARITY_TOP_K = 2;
+
+/**
+ * Similarity type
+ * Default is cosine similarity. Dot product and negative Euclidean distance are also supported.
+ */
+export enum SimilarityType {
+  DEFAULT = "cosine",
+  DOT_PRODUCT = "dot_product",
+  EUCLIDEAN = "euclidean",
+}
+
+/**
+ * The similarity between two embeddings.
+ * @param embedding1
+ * @param embedding2
+ * @param mode
+ * @returns similarity score with higher numbers meaning the two embeddings are more similar
+ */
+
+export function similarity(
+  embedding1: number[],
+  embedding2: number[],
+  mode: SimilarityType = SimilarityType.DEFAULT,
+): number {
+  if (embedding1.length !== embedding2.length) {
+    throw new Error("Embedding length mismatch");
+  }
+
+  // NOTE I've taken enough Kahan to know that we should probably leave the
+  // numeric programming to numeric programmers. The naive approach here
+  // will probably cause some avoidable loss of floating point precision
+  // ml-distance is worth watching although they currently also use the naive
+  // formulas
+  function norm(x: number[]): number {
+    let result = 0;
+    for (let i = 0; i < x.length; i++) {
+      result += x[i] * x[i];
+    }
+    return Math.sqrt(result);
+  }
+
+  switch (mode) {
+    case SimilarityType.EUCLIDEAN: {
+      const difference = embedding1.map((x, i) => x - embedding2[i]);
+      return -norm(difference);
+    }
+    case SimilarityType.DOT_PRODUCT: {
+      let result = 0;
+      for (let i = 0; i < embedding1.length; i++) {
+        result += embedding1[i] * embedding2[i];
+      }
+      return result;
+    }
+    case SimilarityType.DEFAULT: {
+      return (
+        similarity(embedding1, embedding2, SimilarityType.DOT_PRODUCT) /
+        (norm(embedding1) * norm(embedding2))
+      );
+    }
+    default:
+      throw new Error("Not implemented yet");
+  }
+}

packages/core/src/global/settings/callback-manager.ts

@@ -105,9 +105,7 @@ export class CallbackManager {
     }
     queueMicrotask(() => {
       cbs.forEach((handler) =>
-        handler(
-          LlamaIndexCustomEvent.fromEvent(event, structuredClone(detail)),
-        ),
+        handler(LlamaIndexCustomEvent.fromEvent(event, { ...detail })),
       );
     });
   }

packages/core/src/schema/index.ts

@@ -1,2 +1,3 @@
 export * from "./node";
+export type { TransformComponent } from "./type";
 export * from "./zod";

packages/core/src/schema/type.ts

@@ -0,0 +1,5 @@
+import type { BaseNode } from "./node";
+
+export interface TransformComponent<Options extends Record<string, unknown>> {
+  transform(nodes: BaseNode[], options?: Options): Promise<BaseNode[]>;
+}

packages/core/tests/embeddings.test.ts

@@ -1,6 +1,6 @@
+import { truncateMaxTokens } from "@llamaindex/core/embeddings";
 import { Tokenizers, tokenizers } from "@llamaindex/env";
 import { describe, expect, test } from "vitest";
-import { truncateMaxTokens } from "../../src/embeddings/tokenizer.js";
 
 describe("truncateMaxTokens", () => {
   const tokenizer = tokenizers.tokenizer(Tokenizers.CL100K_BASE);

packages/core/tests/event-system.test.ts

@@ -6,6 +6,9 @@ declare module "@llamaindex/core/global" {
     test: {
       value: number;
     };
+    functionTest: {
+      fn: ({ x }: { x: number }) => string;
+    };
   }
 }
 
@@ -42,6 +45,25 @@ describe("event system", () => {
     expect(callback).toHaveBeenCalledTimes(1);
   });
 
+  test("dispatch function tool event", async () => {
+    const testFunction = ({ x }: { x: number }) => `${x * 2}`;
+    let callback;
+    Settings.callbackManager.on(
+      "functionTest",
+      (callback = vi.fn((event) => {
+        const data = event.detail;
+        expect(data.fn).toBe(testFunction);
+      })),
+    );
+
+    Settings.callbackManager.dispatchEvent("functionTest", {
+      fn: testFunction,
+    });
+    expect(callback).toHaveBeenCalledTimes(0);
+    await new Promise((resolve) => process.nextTick(resolve));
+    expect(callback).toHaveBeenCalledTimes(1);
+  });
+
   // rollup doesn't support decorators for now
   // test('wrap event caller', async () => {
   //   class A {

packages/core/tests/package.json

@@ -7,6 +7,6 @@
   },
   "devDependencies": {
     "@llamaindex/core": "workspace:*",
-    "vitest": "^1.6.0"
+    "vitest": "^2.0.2"
   }
 }

packages/env/package.json

@@ -68,11 +68,11 @@
   },
   "devDependencies": {
     "@aws-crypto/sha256-js": "^5.2.0",
-    "@swc/cli": "^0.3.12",
+    "@swc/cli": "^0.4.0",
     "@swc/core": "^1.6.3",
     "concurrently": "^8.2.2",
     "pathe": "^1.1.2",
-    "vitest": "^1.6.0"
+    "vitest": "^2.0.2"
   },
   "dependencies": {
     "@types/lodash": "^4.17.5",

packages/experimental/CHANGELOG.md

@@ -1,5 +1,27 @@
 # @llamaindex/experimental
 
+## 0.0.55
+
+### Patch Changes
+
+- Updated dependencies [b974eea]
+  - llamaindex@0.5.5
+
+## 0.0.54
+
+### Patch Changes
+
+- Updated dependencies [1a65ead]
+  - llamaindex@0.5.4
+
+## 0.0.53
+
+### Patch Changes
+
+- Updated dependencies [9bbbc67]
+- Updated dependencies [b3681bf]
+  - llamaindex@0.5.3
+
 ## 0.0.52
 
 ### Patch Changes

packages/experimental/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/experimental",
   "description": "Experimental package for LlamaIndexTS",
-  "version": "0.0.52",
+  "version": "0.0.55",
   "type": "module",
   "types": "dist/type/index.d.ts",
   "main": "dist/cjs/index.js",
@@ -56,7 +56,7 @@
   },
   "devDependencies": {
     "@aws-crypto/sha256-js": "^5.2.0",
-    "@swc/cli": "^0.3.12",
+    "@swc/cli": "^0.4.0",
     "@swc/core": "^1.6.3",
     "@types/jsonpath": "^0.2.4",
     "concurrently": "^8.2.2",

packages/llamaindex/CHANGELOG.md

@@ -1,5 +1,28 @@
 # llamaindex
 
+## 0.5.5
+
+### Patch Changes
+
+- b974eea: Add support for Metadata filters
+- Updated dependencies [b974eea]
+  - @llamaindex/core@0.1.2
+
+## 0.5.4
+
+### Patch Changes
+
+- 1a65ead: feat: add vendorMultimodal params to LlamaParseReader
+
+## 0.5.3
+
+### Patch Changes
+
+- 9bbbc67: feat: add a reader for Discord messages
+- b3681bf: fix: DataCloneError when using FunctionTool
+- Updated dependencies [b3681bf]
+  - @llamaindex/core@0.1.1
+
 ## 0.5.2
 
 ### Patch Changes

packages/llamaindex/e2e/examples/cloudflare-worker-agent/CHANGELOG.md

@@ -1,5 +1,27 @@
 # @llamaindex/cloudflare-worker-agent-test
 
+## 0.0.39
+
+### Patch Changes
+
+- Updated dependencies [b974eea]
+  - llamaindex@0.5.5
+
+## 0.0.38
+
+### Patch Changes
+
+- Updated dependencies [1a65ead]
+  - llamaindex@0.5.4
+
+## 0.0.37
+
+### Patch Changes
+
+- Updated dependencies [9bbbc67]
+- Updated dependencies [b3681bf]
+  - llamaindex@0.5.3
+
 ## 0.0.36
 
 ### Patch Changes

packages/llamaindex/e2e/examples/cloudflare-worker-agent/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/cloudflare-worker-agent-test",
-  "version": "0.0.36",
+  "version": "0.0.39",
   "type": "module",
   "private": true,
   "scripts": {
@@ -12,13 +12,13 @@
     "cf-typegen": "wrangler types"
   },
   "devDependencies": {
-    "@cloudflare/vitest-pool-workers": "^0.4.3",
-    "@cloudflare/workers-types": "^4.20240605.0",
-    "@vitest/runner": "1.3.0",
-    "@vitest/snapshot": "1.3.0",
-    "typescript": "^5.5.2",
-    "vitest": "1.3.0",
-    "wrangler": "^3.60.1"
+    "@cloudflare/vitest-pool-workers": "^0.4.10",
+    "@cloudflare/workers-types": "^4.20240620.0",
+    "@vitest/runner": "1.5.3",
+    "@vitest/snapshot": "1.5.3",
+    "typescript": "^5.5.3",
+    "vitest": "1.5.3",
+    "wrangler": "^3.63.2"
   },
   "dependencies": {
     "llamaindex": "workspace:*"

packages/llamaindex/e2e/examples/nextjs-agent/CHANGELOG.md

@@ -1,5 +1,27 @@
 # @llamaindex/next-agent-test
 
+## 0.1.39
+
+### Patch Changes
+
+- Updated dependencies [b974eea]
+  - llamaindex@0.5.5
+
+## 0.1.38
+
+### Patch Changes
+
+- Updated dependencies [1a65ead]
+  - llamaindex@0.5.4
+
+## 0.1.37
+
+### Patch Changes
+
+- Updated dependencies [9bbbc67]
+- Updated dependencies [b3681bf]
+  - llamaindex@0.5.3
+
 ## 0.1.36
 
 ### Patch Changes

packages/llamaindex/e2e/examples/nextjs-agent/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/next-agent-test",
-  "version": "0.1.36",
+  "version": "0.1.39",
   "private": true,
   "scripts": {
     "dev": "next dev",
@@ -11,7 +11,7 @@
   "dependencies": {
     "ai": "^3.2.1",
     "llamaindex": "workspace:*",
-    "next": "14.2.4",
+    "next": "14.2.5",
     "react": "18.3.1",
     "react-dom": "18.3.1"
   },
@@ -20,9 +20,9 @@
     "@types/react": "^18.3.3",
     "@types/react-dom": "^18.3.0",
     "eslint": "^8.57.0",
-    "eslint-config-next": "14.2.3",
+    "eslint-config-next": "14.2.5",
     "postcss": "^8",
     "tailwindcss": "^3.4.4",
-    "typescript": "^5.5.2"
+    "typescript": "^5.5.3"
   }
 }

packages/llamaindex/e2e/examples/nextjs-edge-runtime/CHANGELOG.md

@@ -1,5 +1,27 @@
 # test-edge-runtime
 
+## 0.1.38
+
+### Patch Changes
+
+- Updated dependencies [b974eea]
+  - llamaindex@0.5.5
+
+## 0.1.37
+
+### Patch Changes
+
+- Updated dependencies [1a65ead]
+  - llamaindex@0.5.4
+
+## 0.1.36
+
+### Patch Changes
+
+- Updated dependencies [9bbbc67]
+- Updated dependencies [b3681bf]
+  - llamaindex@0.5.3
+
 ## 0.1.35
 
 ### Patch Changes

packages/llamaindex/e2e/examples/nextjs-edge-runtime/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/nextjs-edge-runtime-test",
-  "version": "0.1.35",
+  "version": "0.1.38",
   "private": true,
   "scripts": {
     "dev": "next dev",
@@ -9,7 +9,7 @@
   },
   "dependencies": {
     "llamaindex": "workspace:*",
-    "next": "14.2.4",
+    "next": "14.2.5",
     "react": "^18.3.1",
     "react-dom": "^18.3.1"
   },
@@ -17,6 +17,6 @@
     "@types/node": "^20.12.11",
     "@types/react": "^18.3.3",
     "@types/react-dom": "^18.3.0",
-    "typescript": "^5.5.2"
+    "typescript": "^5.5.3"
   }
 }

packages/llamaindex/e2e/examples/nextjs-node-runtime/CHANGELOG.md

@@ -1,5 +1,27 @@
 # @llamaindex/next-node-runtime
 
+## 0.0.20
+
+### Patch Changes
+
+- Updated dependencies [b974eea]
+  - llamaindex@0.5.5
+
+## 0.0.19
+
+### Patch Changes
+
+- Updated dependencies [1a65ead]
+  - llamaindex@0.5.4
+
+## 0.0.18
+
+### Patch Changes
+
+- Updated dependencies [9bbbc67]
+- Updated dependencies [b3681bf]
+  - llamaindex@0.5.3
+
 ## 0.0.17
 
 ### Patch Changes

packages/llamaindex/e2e/examples/nextjs-node-runtime/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/next-node-runtime-test",
-  "version": "0.0.17",
+  "version": "0.0.20",
   "private": true,
   "scripts": {
     "dev": "next dev",
@@ -10,7 +10,7 @@
   },
   "dependencies": {
     "llamaindex": "workspace:*",
-    "next": "14.2.4",
+    "next": "14.2.5",
     "react": "18.3.1",
     "react-dom": "18.3.1"
   },
@@ -19,9 +19,9 @@
     "@types/react": "^18.3.3",
     "@types/react-dom": "^18.3.0",
     "eslint": "^8.57.0",
-    "eslint-config-next": "14.2.3",
+    "eslint-config-next": "14.2.5",
     "postcss": "^8",
     "tailwindcss": "^3.4.4",
-    "typescript": "^5.5.2"
+    "typescript": "^5.5.3"
   }
 }

packages/llamaindex/e2e/examples/waku-query-engine/CHANGELOG.md

@@ -1,5 +1,27 @@
 # @llamaindex/waku-query-engine-test
 
+## 0.0.39
+
+### Patch Changes
+
+- Updated dependencies [b974eea]
+  - llamaindex@0.5.5
+
+## 0.0.38
+
+### Patch Changes
+
+- Updated dependencies [1a65ead]
+  - llamaindex@0.5.4
+
+## 0.0.37
+
+### Patch Changes
+
+- Updated dependencies [9bbbc67]
+- Updated dependencies [b3681bf]
+  - llamaindex@0.5.3
+
 ## 0.0.36
 
 ### Patch Changes

packages/llamaindex/e2e/examples/waku-query-engine/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/waku-query-engine-test",
-  "version": "0.0.36",
+  "version": "0.0.39",
   "type": "module",
   "private": true,
   "scripts": {
@@ -10,16 +10,16 @@
   },
   "dependencies": {
     "llamaindex": "workspace:*",
-    "react": "19.0.0-canary-e3ebcd54b-20240405",
-    "react-dom": "19.0.0-canary-e3ebcd54b-20240405",
-    "react-server-dom-webpack": "19.0.0-canary-e3ebcd54b-20240405",
-    "waku": "0.20.1"
+    "react": "19.0.0-beta-e7d213dfb0-20240507",
+    "react-dom": "19.0.0-beta-e7d213dfb0-20240507",
+    "react-server-dom-webpack": "19.0.0-beta-e7d213dfb0-20240507",
+    "waku": "0.20.2"
   },
   "devDependencies": {
-    "@types/react": "18.3.1",
+    "@types/react": "18.3.3",
     "@types/react-dom": "18.3.0",
     "autoprefixer": "10.4.19",
-    "tailwindcss": "3.4.3",
-    "typescript": "5.4.5"
+    "tailwindcss": "3.4.4",
+    "typescript": "5.5.3"
   }
 }

packages/llamaindex/package.json

@@ -1,6 +1,6 @@
 {
   "name": "llamaindex",
-  "version": "0.5.2",
+  "version": "0.5.5",
   "license": "MIT",
   "type": "module",
   "keywords": [
@@ -20,18 +20,20 @@
     "llamaindex"
   ],
   "dependencies": {
-    "@anthropic-ai/sdk": "^0.21.1",
+    "@anthropic-ai/sdk": "0.21.1",
     "@aws-crypto/sha256-js": "^5.2.0",
     "@azure/identity": "^4.2.1",
     "@datastax/astra-db-ts": "^1.2.1",
+    "@discordjs/rest": "^2.3.0",
     "@google-cloud/vertexai": "^1.2.0",
-    "@google/generative-ai": "^0.12.0",
-    "@grpc/grpc-js": "^1.10.8",
+    "@google/generative-ai": "0.12.0",
+    "@grpc/grpc-js": "^1.10.11",
     "@huggingface/inference": "^2.7.0",
     "@llamaindex/cloud": "workspace:*",
     "@llamaindex/core": "workspace:*",
     "@llamaindex/env": "workspace:*",
-    "@mistralai/mistralai": "^0.4.0",
+    "@mistralai/mistralai": "^0.5.0",
+    "@mixedbread-ai/sdk": "^2.2.11",
     "@pinecone-database/pinecone": "^2.2.2",
     "@qdrant/js-client-rest": "^1.9.0",
     "@types/lodash": "^4.17.4",
@@ -39,13 +41,13 @@
     "@types/papaparse": "^5.3.14",
     "@types/pg": "^8.11.6",
     "@xenova/transformers": "^2.17.2",
-    "@zilliz/milvus2-sdk-node": "^2.4.2",
+    "@zilliz/milvus2-sdk-node": "^2.4.4",
     "ajv": "^8.16.0",
-    "assemblyai": "^4.4.5",
+    "assemblyai": "^4.6.0",
     "chromadb": "1.8.1",
-    "cohere-ai": "7.9.5",
+    "cohere-ai": "7.10.6",
+    "discord-api-types": "^0.37.92",
     "groq-sdk": "^0.5.0",
-    "@mixedbread-ai/sdk": "^2.2.11",
     "js-tiktoken": "^1.0.12",
     "lodash": "^4.17.21",
     "magic-bytes.js": "^1.10.0",
@@ -53,16 +55,16 @@
     "md-utils-ts": "^2.0.0",
     "mongodb": "^6.7.0",
     "notion-md-crawler": "^1.0.0",
-    "openai": "^4.52.0",
+    "openai": "^4.52.5",
     "papaparse": "^5.4.1",
     "pathe": "^1.1.2",
     "pg": "^8.12.0",
-    "pgvector": "^0.1.8",
-    "portkey-ai": "^0.1.16",
+    "pgvector": "^0.2.0",
+    "portkey-ai": "0.1.16",
     "rake-modified": "^1.0.8",
     "string-strip-html": "^13.4.8",
     "tiktoken": "^1.0.15",
-    "unpdf": "^0.10.1",
+    "unpdf": "^0.11.0",
     "wikipedia": "^2.1.2",
     "wink-nlp": "^2.3.0",
     "zod": "^3.23.8"
@@ -77,11 +79,11 @@
   },
   "devDependencies": {
     "@notionhq/client": "^2.2.15",
-    "@swc/cli": "^0.3.12",
+    "@swc/cli": "^0.4.0",
     "@swc/core": "^1.6.3",
     "concurrently": "^8.2.2",
-    "glob": "^10.4.2",
-    "typescript": "^5.5.2"
+    "glob": "^11.0.0",
+    "typescript": "^5.5.3"
   },
   "engines": {
     "node": ">=18.0.0"

packages/llamaindex/src/ServiceContext.ts

@@ -1,7 +1,7 @@
+import type { BaseEmbedding } from "@llamaindex/core/embeddings";
 import type { LLM } from "@llamaindex/core/llms";
 import { PromptHelper } from "./PromptHelper.js";
 import { OpenAIEmbedding } from "./embeddings/OpenAIEmbedding.js";
-import type { BaseEmbedding } from "./embeddings/types.js";
 import { OpenAI } from "./llm/openai.js";
 import { SimpleNodeParser } from "./nodeParsers/SimpleNodeParser.js";
 import type { NodeParser } from "./nodeParsers/types.js";

packages/llamaindex/src/Settings.ts

@@ -7,10 +7,10 @@ import { OpenAI } from "./llm/openai.js";
 import { PromptHelper } from "./PromptHelper.js";
 import { SimpleNodeParser } from "./nodeParsers/SimpleNodeParser.js";
 
+import type { BaseEmbedding } from "@llamaindex/core/embeddings";
 import type { LLM } from "@llamaindex/core/llms";
 import { AsyncLocalStorage, getEnv } from "@llamaindex/env";
 import type { ServiceContext } from "./ServiceContext.js";
-import type { BaseEmbedding } from "./embeddings/types.js";
 import {
   getEmbeddedModel,
   setEmbeddedModel,

packages/llamaindex/src/cloud/LlamaCloudIndex.ts

@@ -1,7 +1,6 @@
-import type { Document } from "@llamaindex/core/schema";
+import type { Document, TransformComponent } from "@llamaindex/core/schema";
 import type { BaseRetriever } from "../Retriever.js";
 import { RetrieverQueryEngine } from "../engines/query/RetrieverQueryEngine.js";
-import type { TransformComponent } from "../ingestion/types.js";
 import type { BaseNodePostprocessor } from "../postprocessors/types.js";
 import type { BaseSynthesizer } from "../synthesizers/types.js";
 import type { QueryEngine } from "../types.js";
@@ -148,11 +147,11 @@ export class LlamaCloudIndex {
   static async fromDocuments(
     params: {
       documents: Document[];
-      transformations?: TransformComponent[];
+      transformations?: TransformComponent<any>[];
       verbose?: boolean;
     } & CloudConstructorParams,
   ): Promise<LlamaCloudIndex> {
-    const defaultTransformations: TransformComponent[] = [
+    const defaultTransformations: TransformComponent<any>[] = [
       new SimpleNodeParser(),
       new OpenAIEmbedding({
         apiKey: getEnv("OPENAI_API_KEY"),

packages/llamaindex/src/cloud/config.ts

@@ -3,20 +3,19 @@ import type {
   PipelineCreate,
   PipelineType,
 } from "@llamaindex/cloud/api";
-import { BaseNode } from "@llamaindex/core/schema";
+import { BaseNode, type TransformComponent } from "@llamaindex/core/schema";
 import { OpenAIEmbedding } from "../embeddings/OpenAIEmbedding.js";
-import type { TransformComponent } from "../ingestion/types.js";
 import { SimpleNodeParser } from "../nodeParsers/SimpleNodeParser.js";
 
 export type GetPipelineCreateParams = {
   pipelineName: string;
   pipelineType: PipelineType;
-  transformations?: TransformComponent[];
+  transformations?: TransformComponent<any>[];
   inputNodes?: BaseNode[];
 };
 
 function getTransformationConfig(
-  transformation: TransformComponent,
+  transformation: TransformComponent<any>,
 ): ConfiguredTransformationItem {
   if (transformation instanceof SimpleNodeParser) {
     return {

packages/llamaindex/src/constants.ts

@@ -4,6 +4,5 @@ export const DEFAULT_NUM_OUTPUTS = 256;
 export const DEFAULT_CHUNK_SIZE = 1024;
 export const DEFAULT_CHUNK_OVERLAP = 20;
 export const DEFAULT_CHUNK_OVERLAP_RATIO = 0.1;
-export const DEFAULT_SIMILARITY_TOP_K = 2;
 
 export const DEFAULT_PADDING = 5;

packages/llamaindex/src/embeddings/DeepInfraEmbedding.ts

@@ -1,7 +1,7 @@
+import { BaseEmbedding } from "@llamaindex/core/embeddings";
 import type { MessageContentDetail } from "@llamaindex/core/llms";
 import { extractSingleText } from "@llamaindex/core/utils";
 import { getEnv } from "@llamaindex/env";
-import { BaseEmbedding } from "./types.js";
 
 const DEFAULT_MODEL = "sentence-transformers/clip-ViT-B-32";
 
@@ -103,10 +103,10 @@ export class DeepInfraEmbedding extends BaseEmbedding {
     }
   }
 
-  async getTextEmbeddings(texts: string[]): Promise<number[][]> {
+  getTextEmbeddings = async (texts: string[]): Promise<number[][]> => {
     const textsWithPrefix = mapPrefixWithInputs(this.textPrefix, texts);
-    return await this.getDeepInfraEmbedding(textsWithPrefix);
-  }
+    return this.getDeepInfraEmbedding(textsWithPrefix);
+  };
 
   async getQueryEmbeddings(queries: string[]): Promise<number[][]> {
     const queriesWithPrefix = mapPrefixWithInputs(this.queryPrefix, queries);

packages/llamaindex/src/embeddings/GeminiEmbedding.ts

@@ -1,6 +1,6 @@
+import { BaseEmbedding } from "@llamaindex/core/embeddings";
 import { GeminiSession, GeminiSessionStore } from "../llm/gemini/base.js";
 import { GEMINI_BACKENDS } from "../llm/gemini/types.js";
-import { BaseEmbedding } from "./types.js";
 
 export enum GEMINI_EMBEDDING_MODEL {
   EMBEDDING_001 = "embedding-001",

packages/llamaindex/src/embeddings/HuggingFaceEmbedding.ts

@@ -1,6 +1,6 @@
 import { HfInference } from "@huggingface/inference";
+import { BaseEmbedding } from "@llamaindex/core/embeddings";
 import { lazyLoadTransformers } from "../internal/deps/transformers.js";
-import { BaseEmbedding } from "./types.js";
 
 export enum HuggingFaceEmbeddingModelType {
   XENOVA_ALL_MINILM_L6_V2 = "Xenova/all-MiniLM-L6-v2",
@@ -91,11 +91,11 @@ export class HuggingFaceInferenceAPIEmbedding extends BaseEmbedding {
     return res as number[];
   }
 
-  async getTextEmbeddings(texts: string[]): Promise<Array<number[]>> {
+  getTextEmbeddings = async (texts: string[]): Promise<Array<number[]>> => {
     const res = await this.hf.featureExtraction({
       model: this.model,
       inputs: texts,
     });
     return res as number[][];
-  }
+  };
 }

packages/llamaindex/src/embeddings/MistralAIEmbedding.ts

@@ -1,5 +1,5 @@
+import { BaseEmbedding } from "@llamaindex/core/embeddings";
 import { MistralAISession } from "../llm/mistral.js";
-import { BaseEmbedding } from "./types.js";
 
 export enum MistralAIEmbeddingModelType {
   MISTRAL_EMBED = "mistral-embed",

packages/llamaindex/src/embeddings/MixedbreadAIEmbeddings.ts

@@ -1,6 +1,6 @@
+import { BaseEmbedding, type EmbeddingInfo } from "@llamaindex/core/embeddings";
 import { getEnv } from "@llamaindex/env";
 import { MixedbreadAI, MixedbreadAIClient } from "@mixedbread-ai/sdk";
-import { BaseEmbedding, type EmbeddingInfo } from "./types.js";
 
 type EmbeddingsRequestWithoutInput = Omit<
   MixedbreadAI.EmbeddingsRequest,
@@ -153,7 +153,7 @@ export class MixedbreadAIEmbeddings extends BaseEmbedding {
    * const result = await mxbai.getTextEmbeddings(texts);
    * console.log(result);
    */
-  async getTextEmbeddings(texts: string[]): Promise<Array<number[]>> {
+  getTextEmbeddings = async (texts: string[]): Promise<Array<number[]>> => {
     if (texts.length === 0) {
       return [];
     }
@@ -166,5 +166,5 @@ export class MixedbreadAIEmbeddings extends BaseEmbedding {
       this.requestOptions,
     );
     return response.data.map((d) => d.embedding as number[]);
-  }
+  };
 }

packages/llamaindex/src/embeddings/MultiModalEmbedding.ts

@@ -1,3 +1,4 @@
+import { BaseEmbedding, batchEmbeddings } from "@llamaindex/core/embeddings";
 import type { MessageContentDetail } from "@llamaindex/core/llms";
 import {
   ImageNode,
@@ -8,7 +9,6 @@ import {
   type ImageType,
 } from "@llamaindex/core/schema";
 import { extractImage, extractSingleText } from "@llamaindex/core/utils";
-import { BaseEmbedding, batchEmbeddings } from "./types.js";
 
 /*
  * Base class for Multi Modal embeddings.

packages/llamaindex/src/embeddings/OllamaEmbedding.ts

@@ -1,5 +1,5 @@
+import type { BaseEmbedding } from "@llamaindex/core/embeddings";
 import { Ollama } from "../llm/ollama.js";
-import type { BaseEmbedding } from "./types.js";
 
 /**
  * OllamaEmbedding is an alias for Ollama that implements the BaseEmbedding interface.

packages/llamaindex/src/embeddings/OpenAIEmbedding.ts

@@ -1,3 +1,4 @@
+import { BaseEmbedding } from "@llamaindex/core/embeddings";
 import { Tokenizers } from "@llamaindex/env";
 import type { ClientOptions as OpenAIClientOptions } from "openai";
 import type { AzureOpenAIConfig } from "../llm/azure.js";
@@ -8,7 +9,6 @@ import {
 } from "../llm/azure.js";
 import type { OpenAISession } from "../llm/openai.js";
 import { getOpenAISession } from "../llm/openai.js";
-import { BaseEmbedding } from "./types.js";
 
 export const ALL_OPENAI_EMBEDDING_MODELS = {
   "text-embedding-ada-002": {
@@ -132,9 +132,9 @@ export class OpenAIEmbedding extends BaseEmbedding {
    * Get embeddings for a batch of texts
    * @param texts
    */
-  async getTextEmbeddings(texts: string[]): Promise<number[][]> {
-    return await this.getOpenAIEmbedding(texts);
-  }
+  getTextEmbeddings = async (texts: string[]): Promise<number[][]> => {
+    return this.getOpenAIEmbedding(texts);
+  };
 
   /**
    * Get embeddings for a single text

packages/llamaindex/src/embeddings/index.ts

@@ -1,3 +1,4 @@
+export * from "@llamaindex/core/embeddings";
 export { DeepInfraEmbedding } from "./DeepInfraEmbedding.js";
 export { FireworksEmbedding } from "./fireworks.js";
 export * from "./GeminiEmbedding.js";
@@ -9,5 +10,3 @@ export * from "./MultiModalEmbedding.js";
 export { OllamaEmbedding } from "./OllamaEmbedding.js";
 export * from "./OpenAIEmbedding.js";
 export { TogetherEmbedding } from "./together.js";
-export * from "./types.js";
-export * from "./utils.js";

packages/llamaindex/src/embeddings/utils.ts

@@ -1,256 +0,0 @@
-import type { ImageType } from "@llamaindex/core/schema";
-import { fs } from "@llamaindex/env";
-import _ from "lodash";
-import { filetypemime } from "magic-bytes.js";
-import { DEFAULT_SIMILARITY_TOP_K } from "../constants.js";
-import type { VectorStoreQueryMode } from "../storage/vectorStore/types.js";
-
-/**
- * Similarity type
- * Default is cosine similarity. Dot product and negative Euclidean distance are also supported.
- */
-export enum SimilarityType {
-  DEFAULT = "cosine",
-  DOT_PRODUCT = "dot_product",
-  EUCLIDEAN = "euclidean",
-}
-
-/**
- * The similarity between two embeddings.
- * @param embedding1
- * @param embedding2
- * @param mode
- * @returns similarity score with higher numbers meaning the two embeddings are more similar
- */
-
-export function similarity(
-  embedding1: number[],
-  embedding2: number[],
-  mode: SimilarityType = SimilarityType.DEFAULT,
-): number {
-  if (embedding1.length !== embedding2.length) {
-    throw new Error("Embedding length mismatch");
-  }
-
-  // NOTE I've taken enough Kahan to know that we should probably leave the
-  // numeric programming to numeric programmers. The naive approach here
-  // will probably cause some avoidable loss of floating point precision
-  // ml-distance is worth watching although they currently also use the naive
-  // formulas
-  function norm(x: number[]): number {
-    let result = 0;
-    for (let i = 0; i < x.length; i++) {
-      result += x[i] * x[i];
-    }
-    return Math.sqrt(result);
-  }
-
-  switch (mode) {
-    case SimilarityType.EUCLIDEAN: {
-      const difference = embedding1.map((x, i) => x - embedding2[i]);
-      return -norm(difference);
-    }
-    case SimilarityType.DOT_PRODUCT: {
-      let result = 0;
-      for (let i = 0; i < embedding1.length; i++) {
-        result += embedding1[i] * embedding2[i];
-      }
-      return result;
-    }
-    case SimilarityType.DEFAULT: {
-      return (
-        similarity(embedding1, embedding2, SimilarityType.DOT_PRODUCT) /
-        (norm(embedding1) * norm(embedding2))
-      );
-    }
-    default:
-      throw new Error("Not implemented yet");
-  }
-}
-
-/**
- * Get the top K embeddings from a list of embeddings ordered by similarity to the query.
- * @param queryEmbedding
- * @param embeddings list of embeddings to consider
- * @param similarityTopK max number of embeddings to return, default 2
- * @param embeddingIds ids of embeddings in the embeddings list
- * @param similarityCutoff minimum similarity score
- * @returns
- */
-// eslint-disable-next-line max-params
-export function getTopKEmbeddings(
-  queryEmbedding: number[],
-  embeddings: number[][],
-  similarityTopK: number = DEFAULT_SIMILARITY_TOP_K,
-  embeddingIds: any[] | null = null,
-  similarityCutoff: number | null = null,
-): [number[], any[]] {
-  if (embeddingIds == null) {
-    embeddingIds = Array(embeddings.length).map((_, i) => i);
-  }
-
-  if (embeddingIds.length !== embeddings.length) {
-    throw new Error(
-      "getTopKEmbeddings: embeddings and embeddingIds length mismatch",
-    );
-  }
-
-  const similarities: { similarity: number; id: number }[] = [];
-
-  for (let i = 0; i < embeddings.length; i++) {
-    const sim = similarity(queryEmbedding, embeddings[i]);
-    if (similarityCutoff == null || sim > similarityCutoff) {
-      similarities.push({ similarity: sim, id: embeddingIds[i] });
-    }
-  }
-
-  similarities.sort((a, b) => b.similarity - a.similarity); // Reverse sort
-
-  const resultSimilarities: number[] = [];
-  const resultIds: any[] = [];
-
-  for (let i = 0; i < similarityTopK; i++) {
-    if (i >= similarities.length) {
-      break;
-    }
-    resultSimilarities.push(similarities[i].similarity);
-    resultIds.push(similarities[i].id);
-  }
-
-  return [resultSimilarities, resultIds];
-}
-
-// eslint-disable-next-line max-params
-export function getTopKEmbeddingsLearner(
-  queryEmbedding: number[],
-  embeddings: number[][],
-  similarityTopK?: number,
-  embeddingsIds?: any[],
-  queryMode?: VectorStoreQueryMode,
-): [number[], any[]] {
-  throw new Error("Not implemented yet");
-}
-
-// eslint-disable-next-line max-params
-export function getTopKMMREmbeddings(
-  queryEmbedding: number[],
-  embeddings: number[][],
-  similarityFn: ((...args: any[]) => number) | null = null,
-  similarityTopK: number | null = null,
-  embeddingIds: any[] | null = null,
-  _similarityCutoff: number | null = null,
-  mmrThreshold: number | null = null,
-): [number[], any[]] {
-  const threshold = mmrThreshold || 0.5;
-  similarityFn = similarityFn || similarity;
-
-  if (embeddingIds === null || embeddingIds.length === 0) {
-    embeddingIds = Array.from({ length: embeddings.length }, (_, i) => i);
-  }
-  const fullEmbedMap = new Map(embeddingIds.map((value, i) => [value, i]));
-  const embedMap = new Map(fullEmbedMap);
-  const embedSimilarity: Map<any, number> = new Map();
-  let score: number = Number.NEGATIVE_INFINITY;
-  let highScoreId: any | null = null;
-
-  for (let i = 0; i < embeddings.length; i++) {
-    const emb = embeddings[i];
-    const similarity = similarityFn(queryEmbedding, emb);
-    embedSimilarity.set(embeddingIds[i], similarity);
-    if (similarity * threshold > score) {
-      highScoreId = embeddingIds[i];
-      score = similarity * threshold;
-    }
-  }
-
-  const results: [number, any][] = [];
-
-  const embeddingLength = embeddings.length;
-  const similarityTopKCount = similarityTopK || embeddingLength;
-
-  while (results.length < Math.min(similarityTopKCount, embeddingLength)) {
-    results.push([score, highScoreId]);
-    embedMap.delete(highScoreId);
-    const recentEmbeddingId = highScoreId;
-    score = Number.NEGATIVE_INFINITY;
-    for (const embedId of Array.from(embedMap.keys())) {
-      const overlapWithRecent = similarityFn(
-        embeddings[embedMap.get(embedId)!],
-        embeddings[fullEmbedMap.get(recentEmbeddingId)!],
-      );
-      if (
-        threshold * embedSimilarity.get(embedId)! -
-          (1 - threshold) * overlapWithRecent >
-        score
-      ) {
-        score =
-          threshold * embedSimilarity.get(embedId)! -
-          (1 - threshold) * overlapWithRecent;
-        highScoreId = embedId;
-      }
-    }
-  }
-
-  const resultSimilarities = results.map(([s, _]) => s);
-  const resultIds = results.map(([_, n]) => n);
-
-  return [resultSimilarities, resultIds];
-}
-
-async function blobToDataUrl(input: Blob) {
-  const buffer = Buffer.from(await input.arrayBuffer());
-  const mimes = filetypemime(buffer);
-  if (mimes.length < 1) {
-    throw new Error("Unsupported image type");
-  }
-  return "data:" + mimes[0] + ";base64," + buffer.toString("base64");
-}
-
-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 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/llamaindex/src/extractors/types.ts

@@ -1,12 +1,11 @@
-import type { BaseNode } from "@llamaindex/core/schema";
+import type { BaseNode, TransformComponent } from "@llamaindex/core/schema";
 import { MetadataMode, TextNode } from "@llamaindex/core/schema";
-import type { TransformComponent } from "../ingestion/types.js";
 import { defaultNodeTextTemplate } from "./prompts.js";
 
 /*
  * Abstract class for all extractors.
  */
-export abstract class BaseExtractor implements TransformComponent {
+export abstract class BaseExtractor implements TransformComponent<any> {
   isTextNodeOnly: boolean = true;
   showProgress: boolean = true;
   metadataMode: MetadataMode = MetadataMode.ALL;

packages/llamaindex/src/indices/vectorStore/index.ts

@@ -1,3 +1,7 @@
+import {
+  DEFAULT_SIMILARITY_TOP_K,
+  type BaseEmbedding,
+} from "@llamaindex/core/embeddings";
 import { Settings } from "@llamaindex/core/global";
 import type { MessageContent } from "@llamaindex/core/llms";
 import {
@@ -13,8 +17,6 @@ import { wrapEventCaller } from "@llamaindex/core/utils";
 import type { BaseRetriever, RetrieveParams } from "../../Retriever.js";
 import type { ServiceContext } from "../../ServiceContext.js";
 import { nodeParserFromSettingsOrContext } from "../../Settings.js";
-import { DEFAULT_SIMILARITY_TOP_K } from "../../constants.js";
-import type { BaseEmbedding } from "../../embeddings/index.js";
 import { RetrieverQueryEngine } from "../../engines/query/RetrieverQueryEngine.js";
 import {
   addNodesToVectorStores,

packages/llamaindex/src/ingestion/IngestionCache.ts

@@ -1,12 +1,11 @@
-import type { BaseNode } from "@llamaindex/core/schema";
+import type { BaseNode, TransformComponent } from "@llamaindex/core/schema";
 import { MetadataMode } from "@llamaindex/core/schema";
 import { createSHA256 } from "@llamaindex/env";
 import { docToJson, jsonToDoc } from "../storage/docStore/utils.js";
 import { SimpleKVStore } from "../storage/kvStore/SimpleKVStore.js";
 import type { BaseKVStore } from "../storage/kvStore/types.js";
-import type { TransformComponent } from "./types.js";
 
-const transformToJSON = (obj: TransformComponent) => {
+const transformToJSON = (obj: TransformComponent<any>) => {
   const seen: any[] = [];
 
   const replacer = (key: string, value: any) => {
@@ -27,7 +26,7 @@ const transformToJSON = (obj: TransformComponent) => {
 
 export function getTransformationHash(
   nodes: BaseNode[],
-  transform: TransformComponent,
+  transform: TransformComponent<any>,
 ) {
   const nodesStr: string = nodes
     .map((node) => node.getContent(MetadataMode.ALL))

packages/llamaindex/src/ingestion/IngestionPipeline.ts

@@ -1,3 +1,4 @@
+import type { TransformComponent } from "@llamaindex/core/schema";
 import {
   ModalityType,
   splitNodesByType,
@@ -16,7 +17,6 @@ import {
   DocStoreStrategy,
   createDocStoreStrategy,
 } from "./strategies/index.js";
-import type { TransformComponent } from "./types.js";
 
 type IngestionRunArgs = {
   documents?: Document[];
@@ -26,12 +26,12 @@ type IngestionRunArgs = {
 type TransformRunArgs = {
   inPlace?: boolean;
   cache?: IngestionCache;
-  docStoreStrategy?: TransformComponent;
+  docStoreStrategy?: TransformComponent<any>;
 };
 
 export async function runTransformations(
   nodesToRun: BaseNode[],
-  transformations: TransformComponent[],
+  transformations: TransformComponent<any>[],
   transformOptions: any = {},
   { inPlace = true, cache, docStoreStrategy }: TransformRunArgs = {},
 ): Promise<BaseNode[]> {
@@ -60,7 +60,7 @@ export async function runTransformations(
 }
 
 export class IngestionPipeline {
-  transformations: TransformComponent[] = [];
+  transformations: TransformComponent<any>[] = [];
   documents?: Document[];
   reader?: BaseReader;
   vectorStore?: VectorStore;
@@ -70,7 +70,7 @@ export class IngestionPipeline {
   cache?: IngestionCache;
   disableCache: boolean = false;
 
-  private _docStoreStrategy?: TransformComponent;
+  private _docStoreStrategy?: TransformComponent<any>;
 
   constructor(init?: Partial<IngestionPipeline>) {
     Object.assign(this, init);
@@ -112,10 +112,7 @@ export class IngestionPipeline {
     return inputNodes.flat();
   }
 
-  async run(
-    args: IngestionRunArgs & TransformRunArgs = {},
-    transformOptions?: any,
-  ): Promise<BaseNode[]> {
+  async run(args: any = {}, transformOptions?: any): Promise<BaseNode[]> {
     args.cache = args.cache ?? this.cache;
     args.docStoreStrategy = args.docStoreStrategy ?? this._docStoreStrategy;
     const inputNodes = await this.prepareInput(args.documents, args.nodes);

packages/llamaindex/src/ingestion/index.ts

@@ -1,2 +1 @@
 export * from "./IngestionPipeline.js";
-export * from "./types.js";

packages/llamaindex/src/ingestion/strategies/DuplicatesStrategy.ts

@@ -1,11 +1,10 @@
-import type { BaseNode } from "@llamaindex/core/schema";
+import type { BaseNode, TransformComponent } from "@llamaindex/core/schema";
 import type { BaseDocumentStore } from "../../storage/docStore/types.js";
-import type { TransformComponent } from "../types.js";
 
 /**
  * Handle doc store duplicates by checking all hashes.
  */
-export class DuplicatesStrategy implements TransformComponent {
+export class DuplicatesStrategy implements TransformComponent<any> {
   private docStore: BaseDocumentStore;
 
   constructor(docStore: BaseDocumentStore) {

packages/llamaindex/src/ingestion/strategies/UpsertsAndDeleteStrategy.ts

@@ -1,14 +1,13 @@
-import type { BaseNode } from "@llamaindex/core/schema";
+import type { BaseNode, TransformComponent } from "@llamaindex/core/schema";
 import type { BaseDocumentStore } from "../../storage/docStore/types.js";
 import type { VectorStore } from "../../storage/vectorStore/types.js";
-import type { TransformComponent } from "../types.js";
 import { classify } from "./classify.js";
 
 /**
  * Handle docstore upserts by checking hashes and ids.
  * Identify missing docs and delete them from docstore and vector store
  */
-export class UpsertsAndDeleteStrategy implements TransformComponent {
+export class UpsertsAndDeleteStrategy implements TransformComponent<any> {
   protected docStore: BaseDocumentStore;
   protected vectorStores?: VectorStore[];
 

packages/llamaindex/src/ingestion/strategies/UpsertsStrategy.ts

@@ -1,13 +1,12 @@
-import type { BaseNode } from "@llamaindex/core/schema";
+import type { BaseNode, TransformComponent } from "@llamaindex/core/schema";
 import type { BaseDocumentStore } from "../../storage/docStore/types.js";
 import type { VectorStore } from "../../storage/vectorStore/types.js";
-import type { TransformComponent } from "../types.js";
 import { classify } from "./classify.js";
 
 /**
  * Handles doc store upserts by checking hashes and ids.
  */
-export class UpsertsStrategy implements TransformComponent {
+export class UpsertsStrategy implements TransformComponent<any> {
   protected docStore: BaseDocumentStore;
   protected vectorStores?: VectorStore[];
 

packages/llamaindex/src/ingestion/strategies/index.ts

@@ -1,6 +1,6 @@
+import type { TransformComponent } from "@llamaindex/core/schema";
 import type { BaseDocumentStore } from "../../storage/docStore/types.js";
 import type { VectorStore } from "../../storage/vectorStore/types.js";
-import type { TransformComponent } from "../types.js";
 import { DuplicatesStrategy } from "./DuplicatesStrategy.js";
 import { UpsertsAndDeleteStrategy } from "./UpsertsAndDeleteStrategy.js";
 import { UpsertsStrategy } from "./UpsertsStrategy.js";
@@ -19,7 +19,7 @@ export enum DocStoreStrategy {
   NONE = "none", // no-op strategy
 }
 
-class NoOpStrategy implements TransformComponent {
+class NoOpStrategy implements TransformComponent<any> {
   async transform(nodes: any[]): Promise<any[]> {
     return nodes;
   }
@@ -29,7 +29,7 @@ export function createDocStoreStrategy(
   docStoreStrategy: DocStoreStrategy,
   docStore?: BaseDocumentStore,
   vectorStores: VectorStore[] = [],
-): TransformComponent {
+): TransformComponent<any> {
   if (docStoreStrategy === DocStoreStrategy.NONE) {
     return new NoOpStrategy();
   }

packages/llamaindex/src/ingestion/types.ts

@@ -1,5 +0,0 @@
-import type { BaseNode } from "@llamaindex/core/schema";
-
-export interface TransformComponent {
-  transform(nodes: BaseNode[], options?: any): Promise<BaseNode[]>;
-}

packages/llamaindex/src/internal/settings/EmbedModel.ts

@@ -1,6 +1,6 @@
+import type { BaseEmbedding } from "@llamaindex/core/embeddings";
 import { AsyncLocalStorage } from "@llamaindex/env";
 import { OpenAIEmbedding } from "../../embeddings/OpenAIEmbedding.js";
-import type { BaseEmbedding } from "../../embeddings/index.js";
 
 const embeddedModelAsyncLocalStorage = new AsyncLocalStorage<BaseEmbedding>();
 let globalEmbeddedModel: BaseEmbedding | null = null;

packages/llamaindex/src/internal/utils.ts

@@ -1,4 +1,8 @@
+import { similarity } from "@llamaindex/core/embeddings";
 import type { JSONValue } from "@llamaindex/core/global";
+import type { ImageType } from "@llamaindex/core/schema";
+import { fs } from "@llamaindex/env";
+import { filetypemime } from "magic-bytes.js";
 
 export const isAsyncIterable = (
   obj: unknown,
@@ -24,3 +28,177 @@ export function prettifyError(error: unknown): string {
 export function stringifyJSONToMessageContent(value: JSONValue): string {
   return JSON.stringify(value, null, 2).replace(/"([^"]*)"/g, "$1");
 }
+
+/**
+ * Get the top K embeddings from a list of embeddings ordered by similarity to the query.
+ * @param queryEmbedding
+ * @param embeddings list of embeddings to consider
+ * @param similarityTopK max number of embeddings to return, default 2
+ * @param embeddingIds ids of embeddings in the embeddings list
+ * @param similarityCutoff minimum similarity score
+ * @returns
+ */
+// eslint-disable-next-line max-params
+export function getTopKEmbeddings(
+  queryEmbedding: number[],
+  embeddings: number[][],
+  similarityTopK: number = 2,
+  embeddingIds: any[] | null = null,
+  similarityCutoff: number | null = null,
+): [number[], any[]] {
+  if (embeddingIds == null) {
+    embeddingIds = Array(embeddings.length).map((_, i) => i);
+  }
+
+  if (embeddingIds.length !== embeddings.length) {
+    throw new Error(
+      "getTopKEmbeddings: embeddings and embeddingIds length mismatch",
+    );
+  }
+
+  const similarities: { similarity: number; id: number }[] = [];
+
+  for (let i = 0; i < embeddings.length; i++) {
+    const sim = similarity(queryEmbedding, embeddings[i]);
+    if (similarityCutoff == null || sim > similarityCutoff) {
+      similarities.push({ similarity: sim, id: embeddingIds[i] });
+    }
+  }
+
+  similarities.sort((a, b) => b.similarity - a.similarity); // Reverse sort
+
+  const resultSimilarities: number[] = [];
+  const resultIds: any[] = [];
+
+  for (let i = 0; i < similarityTopK; i++) {
+    if (i >= similarities.length) {
+      break;
+    }
+    resultSimilarities.push(similarities[i].similarity);
+    resultIds.push(similarities[i].id);
+  }
+
+  return [resultSimilarities, resultIds];
+}
+
+// eslint-disable-next-line max-params
+export function getTopKMMREmbeddings(
+  queryEmbedding: number[],
+  embeddings: number[][],
+  similarityFn: ((...args: any[]) => number) | null = null,
+  similarityTopK: number | null = null,
+  embeddingIds: any[] | null = null,
+  _similarityCutoff: number | null = null,
+  mmrThreshold: number | null = null,
+): [number[], any[]] {
+  const threshold = mmrThreshold || 0.5;
+  similarityFn = similarityFn || similarity;
+
+  if (embeddingIds === null || embeddingIds.length === 0) {
+    embeddingIds = Array.from({ length: embeddings.length }, (_, i) => i);
+  }
+  const fullEmbedMap = new Map(embeddingIds.map((value, i) => [value, i]));
+  const embedMap = new Map(fullEmbedMap);
+  const embedSimilarity: Map<any, number> = new Map();
+  let score: number = Number.NEGATIVE_INFINITY;
+  let highScoreId: any | null = null;
+
+  for (let i = 0; i < embeddings.length; i++) {
+    const emb = embeddings[i];
+    const similarity = similarityFn(queryEmbedding, emb);
+    embedSimilarity.set(embeddingIds[i], similarity);
+    if (similarity * threshold > score) {
+      highScoreId = embeddingIds[i];
+      score = similarity * threshold;
+    }
+  }
+
+  const results: [number, any][] = [];
+
+  const embeddingLength = embeddings.length;
+  const similarityTopKCount = similarityTopK || embeddingLength;
+
+  while (results.length < Math.min(similarityTopKCount, embeddingLength)) {
+    results.push([score, highScoreId]);
+    embedMap.delete(highScoreId);
+    const recentEmbeddingId = highScoreId;
+    score = Number.NEGATIVE_INFINITY;
+    for (const embedId of Array.from(embedMap.keys())) {
+      const overlapWithRecent = similarityFn(
+        embeddings[embedMap.get(embedId)!],
+        embeddings[fullEmbedMap.get(recentEmbeddingId)!],
+      );
+      if (
+        threshold * embedSimilarity.get(embedId)! -
+          (1 - threshold) * overlapWithRecent >
+        score
+      ) {
+        score =
+          threshold * embedSimilarity.get(embedId)! -
+          (1 - threshold) * overlapWithRecent;
+        highScoreId = embedId;
+      }
+    }
+  }
+
+  const resultSimilarities = results.map(([s, _]) => s);
+  const resultIds = results.map(([_, n]) => n);
+
+  return [resultSimilarities, resultIds];
+}
+
+async function blobToDataUrl(input: Blob) {
+  const buffer = Buffer.from(await input.arrayBuffer());
+  const mimes = filetypemime(buffer);
+  if (mimes.length < 1) {
+    throw new Error("Unsupported image type");
+  }
+  return "data:" + mimes[0] + ";base64," + buffer.toString("base64");
+}
+
+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 (typeof input === "string") {
+    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 {
+    return input;
+  }
+}
+
+export async function imageToDataUrl(input: ImageType): Promise<string> {
+  // first ensure, that the input is a Blob
+  if (
+    (input instanceof URL && input.protocol === "file:") ||
+    typeof input === "string"
+  ) {
+    // string or file URL
+    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/llamaindex/src/llm/ollama.ts

@@ -1,3 +1,4 @@
+import { BaseEmbedding } from "@llamaindex/core/embeddings";
 import type {
   ChatResponse,
   ChatResponseChunk,
@@ -10,7 +11,6 @@ import type {
   LLMMetadata,
 } from "@llamaindex/core/llms";
 import { extractText, streamConverter } from "@llamaindex/core/utils";
-import { BaseEmbedding } from "../embeddings/types.js";
 import {
   Ollama as OllamaBase,
   type Config,

packages/llamaindex/src/nodeParsers/types.ts

@@ -1,10 +1,9 @@
-import type { BaseNode } from "@llamaindex/core/schema";
-import type { TransformComponent } from "../ingestion/types.js";
+import type { BaseNode, TransformComponent } from "@llamaindex/core/schema";
 
 /**
  * A NodeParser generates Nodes from Documents
  */
-export interface NodeParser extends TransformComponent {
+export interface NodeParser extends TransformComponent<any> {
   /**
    * Generates an array of nodes from an array of documents.
    * @param documents - The documents to generate nodes from.

packages/llamaindex/src/readers/DiscordReader.ts

@@ -0,0 +1,137 @@
+import { REST, type RESTOptions } from "@discordjs/rest";
+import { Document } from "@llamaindex/core/schema";
+import { getEnv } from "@llamaindex/env";
+import { Routes, type APIEmbed, type APIMessage } from "discord-api-types/v10";
+
+/**
+ * Represents a reader for Discord messages using @discordjs/rest
+ * See https://github.com/discordjs/discord.js/tree/main/packages/rest
+ */
+export class DiscordReader {
+  private client: REST;
+
+  constructor(
+    discordToken?: string,
+    requestHandler?: RESTOptions["makeRequest"],
+  ) {
+    const token = discordToken ?? getEnv("DISCORD_TOKEN");
+    if (!token) {
+      throw new Error(
+        "Must specify `discordToken` or set environment variable `DISCORD_TOKEN`.",
+      );
+    }
+
+    const restOptions: Partial<RESTOptions> = { version: "10" };
+
+    // Use the provided request handler if specified
+    if (requestHandler) {
+      restOptions.makeRequest = requestHandler;
+    }
+
+    this.client = new REST(restOptions).setToken(token);
+  }
+
+  // Read all messages in a channel given a channel ID
+  private async readChannel(
+    channelId: string,
+    limit?: number,
+    additionalInfo?: boolean,
+    oldestFirst?: boolean,
+  ): Promise<Document[]> {
+    const params = new URLSearchParams();
+    if (limit) params.append("limit", limit.toString());
+    if (oldestFirst) params.append("after", "0");
+
+    try {
+      const endpoint =
+        `${Routes.channelMessages(channelId)}?${params}` as `/channels/${string}/messages`;
+      const messages = (await this.client.get(endpoint)) as APIMessage[];
+      return messages.map((msg) =>
+        this.createDocumentFromMessage(msg, additionalInfo),
+      );
+    } catch (err) {
+      console.error(err);
+      return [];
+    }
+  }
+
+  private createDocumentFromMessage(
+    msg: APIMessage,
+    additionalInfo?: boolean,
+  ): Document {
+    let content = msg.content || "";
+
+    // Include information from embedded messages
+    if (additionalInfo && msg.embeds.length > 0) {
+      content +=
+        "\n" + msg.embeds.map((embed) => this.embedToString(embed)).join("\n");
+    }
+
+    // Include URL from attachments
+    if (additionalInfo && msg.attachments.length > 0) {
+      content +=
+        "\n" +
+        msg.attachments
+          .map((attachment) => `Attachment: ${attachment.url}`)
+          .join("\n");
+    }
+
+    return new Document({
+      text: content,
+      id_: msg.id,
+      metadata: {
+        messageId: msg.id,
+        username: msg.author.username,
+        createdAt: new Date(msg.timestamp).toISOString(),
+        editedAt: msg.edited_timestamp
+          ? new Date(msg.edited_timestamp).toISOString()
+          : undefined,
+      },
+    });
+  }
+
+  // Create a string representation of an embedded message
+  private embedToString(embed: APIEmbed): string {
+    let result = "***Embedded Message***\n";
+    if (embed.title) result += `**${embed.title}**\n`;
+    if (embed.description) result += `${embed.description}\n`;
+    if (embed.url) result += `${embed.url}\n`;
+    if (embed.fields) {
+      result += embed.fields
+        .map((field) => `**${field.name}**: ${field.value}`)
+        .join("\n");
+    }
+    return result.trim();
+  }
+
+  /**
+   * Loads messages from multiple discord channels and returns an array of Document Objects.
+   *
+   * @param {string[]} channelIds - An array of channel IDs from which to load data.
+   * @param {number} [limit] - An optional limit on the number of messages to load per channel.
+   * @param {boolean} [additionalInfo] - An optional flag to include content from embedded messages and attachments urls as text.
+   * @param {boolean} [oldestFirst] - An optional flag to load oldest messages first.
+   * @return {Promise<Document[]>} A promise that resolves to an array of loaded documents.
+   */
+  async loadData(
+    channelIds: string[],
+    limit?: number,
+    additionalInfo?: boolean,
+    oldestFirst?: boolean,
+  ): Promise<Document[]> {
+    let results: Document[] = [];
+    for (const channelId of channelIds) {
+      if (typeof channelId !== "string") {
+        throw new Error(`Channel id ${channelId} must be a string.`);
+      }
+      const channelDocuments = await this.readChannel(
+        channelId,
+        limit,
+        additionalInfo,
+        oldestFirst,
+      );
+      results = results.concat(channelDocuments);
+    }
+    return results;
+  }
+}

packages/llamaindex/src/readers/LlamaParseReader.ts

@@ -129,9 +129,9 @@ export class LlamaParseReader extends FileReader {
   doNotUnrollColumns?: boolean;
   // The page separator to use to split the text. Default is None, which means the parser will use the default separator '\\n---\\n'.
   pageSeparator?: string;
-  // Whether to use gpt-4o to extract text from documents.
+  // Deprecated. Use vendorMultimodal params. Whether to use gpt-4o to extract text from documents.
   gpt4oMode: boolean = false;
-  // The API key for the GPT-4o API. Optional, lowers the cost of parsing. Can be set as an env variable: LLAMA_CLOUD_GPT4O_API_KEY.
+  // Deprecated. Use vendorMultimodal params. The API key for the GPT-4o API. Optional, lowers the cost of parsing. Can be set as an env variable: LLAMA_CLOUD_GPT4O_API_KEY.
   gpt4oApiKey?: string;
   // The bounding box to use to extract text from documents. Describe as a string containing the bounding box margins.
   boundingBox?: string;
@@ -139,6 +139,12 @@ export class LlamaParseReader extends FileReader {
   targetPages?: string;
   // Whether or not to ignore and skip errors raised during parsing.
   ignoreErrors: boolean = true;
+  // Whether to use the vendor multimodal API.
+  useVendorMultimodalModel: boolean = false;
+  // The model name for the vendor multimodal API
+  vendorMultimodalModelName?: string;
+  // The API key for the multimodal API. Can also be set as an env variable: LLAMA_CLOUD_VENDOR_MULTIMODAL_API_KEY
+  vendorMultimodalApiKey?: string;
   // numWorkers is implemented in SimpleDirectoryReader
 
   constructor(params: Partial<LlamaParseReader> = {}) {
@@ -158,6 +164,13 @@ export class LlamaParseReader extends FileReader {
 
       this.gpt4oApiKey = params.gpt4oApiKey;
     }
+    if (params.useVendorMultimodalModel) {
+      params.vendorMultimodalApiKey =
+        params.vendorMultimodalApiKey ??
+        getEnv("LLAMA_CLOUD_VENDOR_MULTIMODAL_API_KEY");
+
+      this.vendorMultimodalApiKey = params.vendorMultimodalApiKey;
+    }
   }
 
   // Create a job for the LlamaParse API
@@ -189,6 +202,9 @@ export class LlamaParseReader extends FileReader {
       gpt4o_api_key: this.gpt4oApiKey,
       bounding_box: this.boundingBox,
       target_pages: this.targetPages,
+      use_vendor_multimodal_model: this.useVendorMultimodalModel?.toString(),
+      vendor_multimodal_model_name: this.vendorMultimodalModelName,
+      vendor_multimodal_api_key: this.vendorMultimodalApiKey,
     };
 
     // Appends body with any defined LlamaParseBodyParams

packages/llamaindex/src/readers/index.ts

@@ -1,5 +1,6 @@
 export * from "./AssemblyAIReader.js";
 export * from "./CSVReader.js";
+export * from "./DiscordReader.js";
 export * from "./DocxReader.js";
 export * from "./HTMLReader.js";
 export * from "./ImageReader.js";

packages/llamaindex/src/storage/vectorStore/MilvusVectorStore.ts

@@ -11,11 +11,66 @@ import {
 import {
   VectorStoreBase,
   type IEmbedModel,
+  type MetadataFilters,
   type VectorStoreNoEmbedModel,
   type VectorStoreQuery,
   type VectorStoreQueryResult,
 } from "./types.js";
-import { metadataDictToNode, nodeToMetadata } from "./utils.js";
+import {
+  metadataDictToNode,
+  nodeToMetadata,
+  parseArrayValue,
+  parseNumberValue,
+  parsePrimitiveValue,
+} from "./utils.js";
+
+function parseScalarFilters(scalarFilters: MetadataFilters): string {
+  const condition = scalarFilters.condition ?? "and";
+  const filters: string[] = [];
+
+  for (const filter of scalarFilters.filters) {
+    switch (filter.operator) {
+      case "==":
+      case "!=": {
+        filters.push(
+          `metadata["${filter.key}"] ${filter.operator} "${parsePrimitiveValue(filter.value)}"`,
+        );
+        break;
+      }
+      case "in": {
+        const filterValue = parseArrayValue(filter.value)
+          .map((v) => `"${v}"`)
+          .join(", ");
+        filters.push(
+          `metadata["${filter.key}"] ${filter.operator} [${filterValue}]`,
+        );
+        break;
+      }
+      case "nin": {
+        // Milvus does not support `nin` operator, so we need to manually check every value
+        // Expected: not metadata["key"] != "value1" and not metadata["key"] != "value2"
+        const filterStr = parseArrayValue(filter.value)
+          .map((v) => `metadata["${filter.key}"] != "${v}"`)
+          .join(" && ");
+        filters.push(filterStr);
+        break;
+      }
+      case "<":
+      case "<=":
+      case ">":
+      case ">=": {
+        filters.push(
+          `metadata["${filter.key}"] ${filter.operator} ${parseNumberValue(filter.value)}`,
+        );
+        break;
+      }
+      default:
+        throw new Error(`Operator ${filter.operator} is not supported.`);
+    }
+  }
+
+  return filters.join(` ${condition} `);
+}
 
 export class MilvusVectorStore
   extends VectorStoreBase
@@ -183,6 +238,12 @@ export class MilvusVectorStore
     });
   }
 
+  public toMilvusFilter(filters?: MetadataFilters): string | undefined {
+    if (!filters) return undefined;
+    // TODO: Milvus also support standard filters, we can add it later
+    return parseScalarFilters(filters);
+  }
+
   public async query(
     query: VectorStoreQuery,
     _options?: any,
@@ -193,6 +254,7 @@ export class MilvusVectorStore
       collection_name: this.collectionName,
       limit: query.similarityTopK,
       vector: query.queryEmbedding,
+      filter: this.toMilvusFilter(query.filters),
     });
 
     const nodes: BaseNode<Metadata>[] = [];

packages/llamaindex/src/storage/vectorStore/MongoDBAtlasVectorStore.ts

@@ -1,9 +1,9 @@
+import type { BaseEmbedding } from "@llamaindex/core/embeddings";
 import type { BaseNode } from "@llamaindex/core/schema";
 import { MetadataMode } from "@llamaindex/core/schema";
 import { getEnv } from "@llamaindex/env";
 import type { BulkWriteOptions, Collection } from "mongodb";
 import { MongoClient } from "mongodb";
-import { BaseEmbedding } from "../../embeddings/types.js";
 import {
   VectorStoreBase,
   type MetadataFilters,

packages/llamaindex/src/storage/vectorStore/PGVectorStore.ts

@@ -272,7 +272,10 @@ export class PGVectorStore
     query.filters?.filters.forEach((filter, index) => {
       const paramIndex = params.length + 1;
       whereClauses.push(`metadata->>'${filter.key}' = $${paramIndex}`);
-      params.push(filter.value);
+      // TODO: support filter with other operators
+      if (!Array.isArray(filter.value)) {
+        params.push(filter.value);
+      }
     });
 
     const where =

packages/llamaindex/src/storage/vectorStore/PineconeVectorStore.ts

@@ -1,7 +1,7 @@
 import {
   VectorStoreBase,
-  type ExactMatchFilter,
   type IEmbedModel,
+  type MetadataFilter,
   type MetadataFilters,
   type VectorStoreNoEmbedModel,
   type VectorStoreQuery,
@@ -199,8 +199,12 @@ export class PineconeVectorStore
   }
 
   toPineconeFilter(stdFilters?: MetadataFilters) {
-    return stdFilters?.filters?.reduce((carry: any, item: ExactMatchFilter) => {
-      carry[item.key] = item.value;
+    return stdFilters?.filters?.reduce((carry: any, item: MetadataFilter) => {
+      // Use MetadataFilter with EQ operator to replace ExactMatchFilter
+      // TODO: support filter with other operators
+      if (item.operator === "==") {
+        carry[item.key] = item.value;
+      }
       return carry;
     }, {});
   }

packages/llamaindex/src/storage/vectorStore/SimpleVectorStore.ts

@@ -1,21 +1,29 @@
+import type { BaseEmbedding } from "@llamaindex/core/embeddings";
 import type { BaseNode } from "@llamaindex/core/schema";
 import { fs, path } from "@llamaindex/env";
-import { BaseEmbedding } from "../../embeddings/index.js";
 import {
   getTopKEmbeddings,
-  getTopKEmbeddingsLearner,
   getTopKMMREmbeddings,
-} from "../../embeddings/utils.js";
+} from "../../internal/utils.js";
 import { exists } from "../FileSystem.js";
 import { DEFAULT_PERSIST_DIR } from "../constants.js";
 import {
+  FilterOperator,
   VectorStoreBase,
   VectorStoreQueryMode,
   type IEmbedModel,
+  type MetadataFilter,
+  type MetadataFilters,
   type VectorStoreNoEmbedModel,
   type VectorStoreQuery,
   type VectorStoreQueryResult,
 } from "./types.js";
+import {
+  nodeToMetadata,
+  parseArrayValue,
+  parseNumberValue,
+  parsePrimitiveValue,
+} from "./utils.js";
 
 const LEARNER_MODES = new Set<VectorStoreQueryMode>([
   VectorStoreQueryMode.SVM,
@@ -25,9 +33,85 @@ const LEARNER_MODES = new Set<VectorStoreQueryMode>([
 
 const MMR_MODE = VectorStoreQueryMode.MMR;
 
+type MetadataValue = Record<string, any>;
+
+// Mapping of filter operators to metadata filter functions
+const OPERATOR_TO_FILTER: {
+  [key in FilterOperator]: (
+    { key, value }: MetadataFilter,
+    metadata: MetadataValue,
+  ) => boolean;
+} = {
+  [FilterOperator.EQ]: ({ key, value }, metadata) => {
+    return parsePrimitiveValue(metadata[key]) === parsePrimitiveValue(value);
+  },
+  [FilterOperator.NE]: ({ key, value }, metadata) => {
+    return parsePrimitiveValue(metadata[key]) !== parsePrimitiveValue(value);
+  },
+  [FilterOperator.IN]: ({ key, value }, metadata) => {
+    return parseArrayValue(value).includes(parsePrimitiveValue(metadata[key]));
+  },
+  [FilterOperator.NIN]: ({ key, value }, metadata) => {
+    return !parseArrayValue(value).includes(parsePrimitiveValue(metadata[key]));
+  },
+  [FilterOperator.ANY]: ({ key, value }, metadata) => {
+    return parseArrayValue(value).some((v) =>
+      parseArrayValue(metadata[key]).includes(v),
+    );
+  },
+  [FilterOperator.ALL]: ({ key, value }, metadata) => {
+    return parseArrayValue(value).every((v) =>
+      parseArrayValue(metadata[key]).includes(v),
+    );
+  },
+  [FilterOperator.TEXT_MATCH]: ({ key, value }, metadata) => {
+    return parsePrimitiveValue(metadata[key]).includes(
+      parsePrimitiveValue(value),
+    );
+  },
+  [FilterOperator.CONTAINS]: ({ key, value }, metadata) => {
+    return parseArrayValue(metadata[key]).includes(parsePrimitiveValue(value));
+  },
+  [FilterOperator.GT]: ({ key, value }, metadata) => {
+    return parseNumberValue(metadata[key]) > parseNumberValue(value);
+  },
+  [FilterOperator.LT]: ({ key, value }, metadata) => {
+    return parseNumberValue(metadata[key]) < parseNumberValue(value);
+  },
+  [FilterOperator.GTE]: ({ key, value }, metadata) => {
+    return parseNumberValue(metadata[key]) >= parseNumberValue(value);
+  },
+  [FilterOperator.LTE]: ({ key, value }, metadata) => {
+    return parseNumberValue(metadata[key]) <= parseNumberValue(value);
+  },
+};
+
+// Build a filter function based on the metadata and the preFilters
+const buildFilterFn = (
+  metadata: MetadataValue | undefined,
+  preFilters: MetadataFilters | undefined,
+) => {
+  if (!preFilters) return true;
+  if (!metadata) return false;
+
+  const { filters, condition } = preFilters;
+  const queryCondition = condition || "and"; // default to and
+
+  const itemFilterFn = (filter: MetadataFilter) => {
+    const metadataLookupFn = OPERATOR_TO_FILTER[filter.operator];
+    if (!metadataLookupFn)
+      throw new Error(`Unsupported operator: ${filter.operator}`);
+    return metadataLookupFn(filter, metadata);
+  };
+
+  if (queryCondition === "and") return filters.every(itemFilterFn);
+  return filters.some(itemFilterFn);
+};
+
 class SimpleVectorStoreData {
   embeddingDict: Record<string, number[]> = {};
   textIdToRefDocId: Record<string, string> = {};
+  metadataDict: Record<string, MetadataValue> = {};
 }
 
 export class SimpleVectorStore
@@ -68,6 +152,11 @@ export class SimpleVectorStore
       }
 
       this.data.textIdToRefDocId[node.id_] = node.sourceNode?.nodeId;
+
+      // Add metadata to the metadataDict
+      const metadata = nodeToMetadata(node, true, undefined, false);
+      delete metadata["_node_content"];
+      this.data.metadataDict[node.id_] = metadata;
     }
 
     if (this.persistPath) {
@@ -84,6 +173,7 @@ export class SimpleVectorStore
     for (const textId of textIdsToDelete) {
       delete this.data.embeddingDict[textId];
       delete this.data.textIdToRefDocId[textId];
+      if (this.data.metadataDict) delete this.data.metadataDict[textId];
     }
     if (this.persistPath) {
       await this.persist(this.persistPath);
@@ -91,36 +181,40 @@ export class SimpleVectorStore
     return Promise.resolve();
   }
 
-  async query(query: VectorStoreQuery): Promise<VectorStoreQueryResult> {
-    if (!(query.filters == null)) {
-      throw new Error(
-        "Metadata filters not implemented for SimpleVectorStore yet.",
-      );
-    }
-
+  private async filterNodes(query: VectorStoreQuery): Promise<{
+    nodeIds: string[];
+    embeddings: number[][];
+  }> {
     const items = Object.entries(this.data.embeddingDict);
+    const queryFilterFn = (nodeId: string) => {
+      const metadata = this.data.metadataDict[nodeId];
+      return buildFilterFn(metadata, query.filters);
+    };
 
-    let nodeIds: string[], embeddings: number[][];
-    if (query.docIds) {
+    const nodeFilterFn = (nodeId: string) => {
+      if (!query.docIds) return true;
       const availableIds = new Set(query.docIds);
-      const queriedItems = items.filter((item) => availableIds.has(item[0]));
-      nodeIds = queriedItems.map((item) => item[0]);
-      embeddings = queriedItems.map((item) => item[1]);
-    } else {
-      // No docIds specified, so use all available items
-      nodeIds = items.map((item) => item[0]);
-      embeddings = items.map((item) => item[1]);
-    }
+      return availableIds.has(nodeId);
+    };
 
+    const queriedItems = items.filter(
+      (item) => nodeFilterFn(item[0]) && queryFilterFn(item[0]),
+    );
+    const nodeIds = queriedItems.map((item) => item[0]);
+    const embeddings = queriedItems.map((item) => item[1]);
+
+    return { nodeIds, embeddings };
+  }
+
+  async query(query: VectorStoreQuery): Promise<VectorStoreQueryResult> {
+    const { nodeIds, embeddings } = await this.filterNodes(query);
     const queryEmbedding = query.queryEmbedding!;
 
     let topSimilarities: number[], topIds: string[];
     if (LEARNER_MODES.has(query.mode)) {
-      [topSimilarities, topIds] = getTopKEmbeddingsLearner(
-        queryEmbedding,
-        embeddings,
-        query.similarityTopK,
-        nodeIds,
+      // fixme: unfinished
+      throw new Error(
+        "Learner modes not implemented for SimpleVectorStore yet.",
       );
     } else if (query.mode === MMR_MODE) {
       const mmrThreshold = query.mmrThreshold;
@@ -194,6 +288,7 @@ export class SimpleVectorStore
     const data = new SimpleVectorStoreData();
     data.embeddingDict = dataDict.embeddingDict ?? {};
     data.textIdToRefDocId = dataDict.textIdToRefDocId ?? {};
+    data.metadataDict = dataDict.metadataDict ?? {};
     const store = new SimpleVectorStore({ data, embedModel });
     store.persistPath = persistPath;
     return store;
@@ -206,6 +301,7 @@ export class SimpleVectorStore
     const data = new SimpleVectorStoreData();
     data.embeddingDict = saveDict.embeddingDict;
     data.textIdToRefDocId = saveDict.textIdToRefDocId;
+    data.metadataDict = saveDict.metadataDict;
     return new SimpleVectorStore({ data, embedModel });
   }
 
@@ -213,6 +309,7 @@ export class SimpleVectorStore
     return {
       embeddingDict: this.data.embeddingDict,
       textIdToRefDocId: this.data.textIdToRefDocId,
+      metadataDict: this.data.metadataDict,
     };
   }
 }

packages/llamaindex/src/storage/vectorStore/types.ts

@@ -1,5 +1,5 @@
+import type { BaseEmbedding } from "@llamaindex/core/embeddings";
 import type { BaseNode, ModalityType } from "@llamaindex/core/schema";
-import type { BaseEmbedding } from "../../embeddings/types.js";
 import { getEmbeddedModel } from "../../internal/settings/EmbedModel.js";
 
 export interface VectorStoreQueryResult {
@@ -20,20 +20,37 @@ export enum VectorStoreQueryMode {
   MMR = "mmr",
 }
 
-export interface ExactMatchFilter {
-  filterType: "ExactMatch";
+export enum FilterOperator {
+  EQ = "==", // default operator (string, number)
+  IN = "in", // In array (string or number)
+  GT = ">", // greater than (number)
+  LT = "<", // less than (number)
+  NE = "!=", // not equal to (string, number)
+  GTE = ">=", // greater than or equal to (number)
+  LTE = "<=", // less than or equal to (number)
+  NIN = "nin", // Not in array (string or number)
+  ANY = "any", // Contains any (array of strings)
+  ALL = "all", // Contains all (array of strings)
+  TEXT_MATCH = "text_match", // full text match (allows you to search for a specific substring, token or phrase within the text field)
+  CONTAINS = "contains", // metadata array contains value (string or number)
+}
+
+export enum FilterCondition {
+  AND = "and",
+  OR = "or",
+}
+
+export type MetadataFilterValue = string | number | string[] | number[];
+
+export interface MetadataFilter {
   key: string;
-  value: string | number;
+  value: MetadataFilterValue;
+  operator: `${FilterOperator}`; // ==, any, all,...
 }
 
 export interface MetadataFilters {
-  filters: ExactMatchFilter[];
-}
-
-export interface VectorStoreQuerySpec {
-  query: string;
-  filters: ExactMatchFilter[];
-  topK?: number;
+  filters: Array<MetadataFilter>;
+  condition?: `${FilterCondition}`; // and, or
 }
 
 export interface MetadataInfo {

packages/llamaindex/src/storage/vectorStore/utils.ts

@@ -1,5 +1,6 @@
 import type { BaseNode, Metadata } from "@llamaindex/core/schema";
 import { ObjectType, jsonToNode } from "@llamaindex/core/schema";
+import type { MetadataFilterValue } from "./types.js";
 
 const DEFAULT_TEXT_KEY = "text";
 
@@ -77,3 +78,25 @@ export function metadataDictToNode(
       return jsonToNode(nodeObj, ObjectType.TEXT);
   }
 }
+
+export const parseNumberValue = (value: MetadataFilterValue): number => {
+  if (typeof value !== "number") throw new Error("Value must be a number");
+  return value;
+};
+
+export const parsePrimitiveValue = (value: MetadataFilterValue): string => {
+  if (typeof value !== "number" && typeof value !== "string") {
+    throw new Error("Value must be a string or number");
+  }
+  return value.toString();
+};
+
+export const parseArrayValue = (value: MetadataFilterValue): string[] => {
+  const isPrimitiveArray =
+    Array.isArray(value) &&
+    value.every((v) => typeof v === "string" || typeof v === "number");
+  if (!isPrimitiveArray) {
+    throw new Error("Value must be an array of strings or numbers");
+  }
+  return value.map(String);
+};

packages/llamaindex/src/synthesizers/utils.ts

@@ -7,7 +7,7 @@ import {
   type BaseNode,
 } from "@llamaindex/core/schema";
 import type { SimplePrompt } from "../Prompt.js";
-import { imageToDataUrl } from "../embeddings/utils.js";
+import { imageToDataUrl } from "../internal/utils.js";
 
 export async function createMessageContent(
   prompt: SimplePrompt,

packages/llamaindex/tests/CHANGELOG.md

@@ -1,5 +1,11 @@
 # @llamaindex/core-test
 
+## 0.0.5
+
+### Patch Changes
+
+- b974eea: Add support for Metadata filters
+
 ## 0.0.4
 
 ### Patch Changes

packages/llamaindex/tests/ingestion/IngestionCache.test.ts

@@ -1,10 +1,10 @@
 import type { BaseNode } from "@llamaindex/core/schema";
 import { TextNode } from "@llamaindex/core/schema";
+import type { TransformComponent } from "llamaindex";
 import {
   IngestionCache,
   getTransformationHash,
 } from "llamaindex/ingestion/IngestionCache";
-import type { TransformComponent } from "llamaindex/ingestion/index";
 import { SimpleNodeParser } from "llamaindex/nodeParsers/index";
 import { beforeAll, describe, expect, test } from "vitest";
 
@@ -28,7 +28,7 @@ describe("IngestionCache", () => {
 });
 
 describe("getTransformationHash", () => {
-  let nodes: BaseNode[], transform: TransformComponent;
+  let nodes: BaseNode[], transform: TransformComponent<any>;
 
   beforeAll(() => {
     nodes = [new TextNode({ text: "some text", id_: "some id" })];

packages/llamaindex/tests/mocks/TestableMilvusVectorStore.ts

@@ -0,0 +1,24 @@
+import type { BaseNode } from "@llamaindex/core/schema";
+import type { MilvusClient } from "@zilliz/milvus2-sdk-node";
+import { MilvusVectorStore } from "llamaindex";
+import { type Mocked } from "vitest";
+
+export class TestableMilvusVectorStore extends MilvusVectorStore {
+  public nodes: BaseNode[] = [];
+
+  private fakeTimeout = (ms: number) => {
+    return new Promise((resolve) => setTimeout(resolve, ms));
+  };
+
+  public async add(nodes: BaseNode[]): Promise<string[]> {
+    this.nodes.push(...nodes);
+    await this.fakeTimeout(100);
+    return nodes.map((node) => node.id_);
+  }
+
+  constructor() {
+    super({
+      milvusClient: {} as Mocked<MilvusClient>,
+    });
+  }
+}

packages/llamaindex/tests/package.json

@@ -1,13 +1,13 @@
 {
   "name": "@llamaindex/llamaindex-test",
   "private": true,
-  "version": "0.0.4",
+  "version": "0.0.5",
   "type": "module",
   "scripts": {
     "test": "vitest run"
   },
   "devDependencies": {
     "llamaindex": "workspace:*",
-    "vitest": "^1.6.0"
+    "vitest": "^2.0.2"
   }
 }

packages/llamaindex/tests/readers/.snap/basic.pdf.snap

@@ -1,95 +1,36 @@
 Sample PDF
-
 This is a simple PDF file. Fun fun fun.
-
 Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Phasellus facilisis odio sed mi.
-
 Curabitur suscipit. Nullam vel nisi. Etiam semper ipsum ut lectus. Proin aliquam, erat eget
-
 pharetra commodo, eros mi condimentum quam, sed commodo justo quam ut velit.
-
-Integer
- 
-a
- 
-erat.
- 
-Cras
- 
-laoreet
- 
-ligula
- 
-cursus
- 
-enim.
- 
-Aenean
- 
-scelerisque
- 
-velit
- 
-et
- 
-tellus.
-
+Integer a erat. Cras laoreet ligula cursus enim. Aenean scelerisque velit et tellus.
 Vestibulum dictum aliquet sem. Nulla facilisi. Vestibulum accumsan ante vitae elit. Nulla
-
 erat dolor, blandit in, rutrum quis, semper pulvinar, enim. Nullam varius congue risus.
-
 Vivamus sollicitudin, metus ut interdum eleifend, nisi tellus pellentesque elit, tristique
-
 accumsan eros quam et risus. Suspendisse libero odio, mattis sit amet, aliquet eget,
-
 hendrerit vel, nulla. Sed vitae augue. Aliquam erat volutpat. Aliquam feugiat vulputate nisl.
-
 Suspendisse quis nulla pretium ante pretium mollis. Proin velit ligula, sagittis at, egestas a,
-
 pulvinar quis, nisl.
-
 Pellentesque sit amet lectus. Praesent pulvinar, nunc quis iaculis sagittis, justo quam
-
 lobortis tortor, sed vestibulum dui metus venenatis est. Nunc cursus ligula. Nulla facilisi.
-
 Phasellus ullamcorper consectetuer ante. Duis tincidunt, urna id condimentum luctus, nibh
-
 ante vulputate sapien, id sagittis massa orci ut enim. Pellentesque vestibulum convallis
-
 sem. Nulla consequat quam ut nisl. Nullam est. Curabitur tincidunt dapibus lorem. Proin
-
 velit turpis, scelerisque sit amet, iaculis nec, rhoncus ac, ipsum. Phasellus lorem arcu,
-
 feugiat eu, gravida eu, consequat molestie, ipsum. Nullam vel est ut ipsum volutpat
-
 feugiat. Aenean pellentesque.
-
 In mauris. Pellentesque dui nisi, iaculis eu, rhoncus in, venenatis ac, ante. Ut odio justo,
-
 scelerisque vel, facilisis non, commodo a, pede. Cras nec massa sit amet tortor volutpat
-
 varius. Donec lacinia, neque a luctus aliquet, pede massa imperdiet ante, at varius lorem
-
 pede sed sapien. Fusce erat nibh, aliquet in, eleifend eget, commodo eget, erat. Fusce
-
 consectetuer. Cras risus tortor, porttitor nec, tristique sed, convallis semper, eros. Fusce
-
 vulputate ipsum a mauris. Phasellus mollis. Curabitur sed urna. Aliquam nec sapien non
-
 nibh pulvinar convallis. Vivamus facilisis augue quis quam. Proin cursus aliquet metus.
-
 Suspendisse lacinia. Nulla at tellus ac turpis eleifend scelerisque. Maecenas a pede vitae
-
 enim commodo interdum. Donec odio. Sed sollicitudin dui vitae justo.
-
 Morbi elit nunc, facilisis a, mollis a, molestie at, lectus. Suspendisse eget mauris eu tellus
-
 molestie cursus. Duis ut magna at justo dignissim condimentum. Cum sociis natoque
-
 penatibus et magnis dis parturient montes, nascetur ridiculus mus. Vivamus varius. Ut sit
-
 amet diam suscipit mauris ornare aliquam. Sed varius. Duis arcu. Etiam tristique massa
-
 eget dui. Phasellus congue. Aenean est erat, tincidunt eget, venenatis quis, commodo at,
-
 quam.

packages/llamaindex/tests/readers/pdf-reader.test.ts

@@ -20,7 +20,7 @@ describe("pdf reader", () => {
     const documents = await reader.loadData(
       "../../../examples/data/brk-2022.pdf",
     );
-    expect(documents.length).toBe(140);
+    expect(documents.length).toBe(144);
   });
   test("manga.pdf", async () => {
     const documents = await reader.loadData("../../../examples/data/manga.pdf");

packages/llamaindex/tests/vectorStores/MilvusVectorStore.test.ts

@@ -0,0 +1,333 @@
+import type { BaseNode } from "@llamaindex/core/schema";
+import { TextNode } from "@llamaindex/core/schema";
+import {
+  MilvusVectorStore,
+  VectorStoreQueryMode,
+  type MetadataFilters,
+} from "llamaindex";
+import { beforeEach, describe, expect, it, vi } from "vitest";
+import { TestableMilvusVectorStore } from "../mocks/TestableMilvusVectorStore.js";
+
+type FilterTestCase = {
+  title: string;
+  filters?: MetadataFilters;
+  expected: number;
+  expectedFilterStr: string | undefined;
+  mockResultIds: string[];
+};
+
+describe("MilvusVectorStore", () => {
+  let store: MilvusVectorStore;
+  let nodes: BaseNode[];
+
+  beforeEach(() => {
+    store = new TestableMilvusVectorStore();
+    nodes = [
+      new TextNode({
+        id_: "1",
+        embedding: [0.1, 0.2],
+        text: "The dog is brown",
+        metadata: {
+          name: "Anakin",
+          dogId: "1",
+          private: "true",
+          weight: 1.2,
+          type: ["husky", "puppy"],
+        },
+      }),
+      new TextNode({
+        id_: "2",
+        embedding: [0.1, 0.2],
+        text: "The dog is yellow",
+        metadata: {
+          name: "Luke",
+          dogId: "2",
+          private: "false",
+          weight: 2.3,
+          type: ["puppy"],
+        },
+      }),
+      new TextNode({
+        id_: "3",
+        embedding: [0.1, 0.2],
+        text: "The dog is red",
+        metadata: {
+          name: "Leia",
+          dogId: "3",
+          private: "false",
+          weight: 3.4,
+          type: ["husky"],
+        },
+      }),
+    ];
+  });
+
+  describe("[MilvusVectorStore] manage nodes", () => {
+    it("able to add nodes to store", async () => {
+      const ids = await store.add(nodes);
+      expect(ids).length(3);
+    });
+  });
+
+  describe("[MilvusVectorStore] filter nodes with supported operators", () => {
+    const testcases: FilterTestCase[] = [
+      {
+        title: "No filter",
+        expected: 3,
+        mockResultIds: ["1", "2", "3"],
+        expectedFilterStr: undefined,
+      },
+      {
+        title: "Filter EQ",
+        filters: {
+          filters: [
+            {
+              key: "private",
+              value: "false",
+              operator: "==",
+            },
+          ],
+        },
+        expected: 2,
+        mockResultIds: ["2", "3"],
+        expectedFilterStr: 'metadata["private"] == "false"',
+      },
+      {
+        title: "Filter NE",
+        filters: {
+          filters: [
+            {
+              key: "private",
+              value: "false",
+              operator: "!=",
+            },
+          ],
+        },
+        expected: 1,
+        mockResultIds: ["1"],
+        expectedFilterStr: 'metadata["private"] != "false"',
+      },
+      {
+        title: "Filter GT",
+        filters: {
+          filters: [
+            {
+              key: "weight",
+              value: 2.3,
+              operator: ">",
+            },
+          ],
+        },
+        expected: 1,
+        mockResultIds: ["3"],
+        expectedFilterStr: 'metadata["weight"] > 2.3',
+      },
+      {
+        title: "Filter GTE",
+        filters: {
+          filters: [
+            {
+              key: "weight",
+              value: 2.3,
+              operator: ">=",
+            },
+          ],
+        },
+        expected: 2,
+        mockResultIds: ["2", "3"],
+        expectedFilterStr: 'metadata["weight"] >= 2.3',
+      },
+      {
+        title: "Filter LT",
+        filters: {
+          filters: [
+            {
+              key: "weight",
+              value: 2.3,
+              operator: "<",
+            },
+          ],
+        },
+        expected: 1,
+        mockResultIds: ["1"],
+        expectedFilterStr: 'metadata["weight"] < 2.3',
+      },
+      {
+        title: "Filter LTE",
+        filters: {
+          filters: [
+            {
+              key: "weight",
+              value: 2.3,
+              operator: "<=",
+            },
+          ],
+        },
+        expected: 2,
+        mockResultIds: ["1", "2"],
+        expectedFilterStr: 'metadata["weight"] <= 2.3',
+      },
+      {
+        title: "Filter IN",
+        filters: {
+          filters: [
+            {
+              key: "dogId",
+              value: ["1", "3"],
+              operator: "in",
+            },
+          ],
+        },
+        expected: 2,
+        mockResultIds: ["1", "3"],
+        expectedFilterStr: 'metadata["dogId"] in ["1", "3"]',
+      },
+      {
+        title: "Filter NIN",
+        filters: {
+          filters: [
+            {
+              key: "name",
+              value: ["Anakin", "Leia"],
+              operator: "nin",
+            },
+          ],
+        },
+        expected: 1,
+        mockResultIds: ["2"],
+        expectedFilterStr:
+          'metadata["name"] != "Anakin" && metadata["name"] != "Leia"',
+      },
+      {
+        title: "Filter OR",
+        filters: {
+          filters: [
+            {
+              key: "private",
+              value: "false",
+              operator: "==",
+            },
+            {
+              key: "dogId",
+              value: ["1", "3"],
+              operator: "in",
+            },
+          ],
+          condition: "or",
+        },
+        expected: 3,
+        mockResultIds: ["1", "2", "3"],
+        expectedFilterStr:
+          'metadata["private"] == "false" or metadata["dogId"] in ["1", "3"]',
+      },
+      {
+        title: "Filter AND",
+        filters: {
+          filters: [
+            {
+              key: "private",
+              value: "false",
+              operator: "==",
+            },
+            {
+              key: "dogId",
+              value: "10",
+              operator: "==",
+            },
+          ],
+          condition: "and",
+        },
+        expected: 0,
+        mockResultIds: [],
+        expectedFilterStr:
+          'metadata["private"] == "false" and metadata["dogId"] == "10"',
+      },
+    ];
+
+    testcases.forEach((tc) => {
+      it(`[${tc.title}] should return ${tc.expected} nodes`, async () => {
+        expect(store.toMilvusFilter(tc.filters)).toBe(tc.expectedFilterStr);
+
+        vi.spyOn(store, "query").mockResolvedValue({
+          ids: tc.mockResultIds,
+          similarities: [0.1, 0.2, 0.3],
+        });
+
+        await store.add(nodes);
+        const result = await store.query({
+          queryEmbedding: [0.1, 0.2],
+          similarityTopK: 3,
+          mode: VectorStoreQueryMode.DEFAULT,
+          filters: tc.filters,
+        });
+        expect(result.ids).length(tc.expected);
+      });
+    });
+  });
+
+  describe("[MilvusVectorStore] filter nodes with unsupported operators", () => {
+    const testcases: Array<
+      Omit<FilterTestCase, "expectedFilterStr" | "mockResultIds">
+    > = [
+      {
+        title: "Filter ANY",
+        filters: {
+          filters: [
+            {
+              key: "type",
+              value: ["husky", "puppy"],
+              operator: "any",
+            },
+          ],
+        },
+        expected: 3,
+      },
+      {
+        title: "Filter ALL",
+        filters: {
+          filters: [
+            {
+              key: "type",
+              value: ["husky", "puppy"],
+              operator: "all",
+            },
+          ],
+        },
+        expected: 1,
+      },
+      {
+        title: "Filter CONTAINS",
+        filters: {
+          filters: [
+            {
+              key: "type",
+              value: "puppy",
+              operator: "contains",
+            },
+          ],
+        },
+        expected: 2,
+      },
+      {
+        title: "Filter TEXT_MATCH",
+        filters: {
+          filters: [
+            {
+              key: "name",
+              value: "Luk",
+              operator: "text_match",
+            },
+          ],
+        },
+        expected: 1,
+      },
+    ];
+
+    testcases.forEach((tc) => {
+      it(`[Unsupported Operator] [${tc.title}] should throw error`, async () => {
+        const errorMsg = `Operator ${tc.filters?.filters[0].operator} is not supported.`;
+        expect(() => store.toMilvusFilter(tc.filters)).toThrow(errorMsg);
+      });
+    });
+  });
+});

packages/llamaindex/tests/vectorStores/SimpleVectorStore.test.ts

@@ -0,0 +1,299 @@
+import {
+  BaseEmbedding,
+  BaseNode,
+  SimpleVectorStore,
+  TextNode,
+  VectorStoreQueryMode,
+  type Metadata,
+  type MetadataFilters,
+} from "llamaindex";
+import { beforeEach, describe, expect, it } from "vitest";
+
+type FilterTestCase = {
+  title: string;
+  filters?: MetadataFilters;
+  expected: number;
+};
+
+describe("SimpleVectorStore", () => {
+  let nodes: BaseNode[];
+  let store: SimpleVectorStore;
+
+  beforeEach(() => {
+    nodes = [
+      new TextNode({
+        id_: "1",
+        embedding: [0.1, 0.2],
+        text: "The dog is brown",
+        metadata: {
+          name: "Anakin",
+          dogId: "1",
+          private: "true",
+          weight: 1.2,
+          type: ["husky", "puppy"],
+        },
+      }),
+      new TextNode({
+        id_: "2",
+        embedding: [0.1, 0.2],
+        text: "The dog is yellow",
+        metadata: {
+          name: "Luke",
+          dogId: "2",
+          private: "false",
+          weight: 2.3,
+          type: ["puppy"],
+        },
+      }),
+      new TextNode({
+        id_: "3",
+        embedding: [0.1, 0.2],
+        text: "The dog is red",
+        metadata: {
+          name: "Leia",
+          dogId: "3",
+          private: "false",
+          weight: 3.4,
+          type: ["husky"],
+        },
+      }),
+    ];
+    store = new SimpleVectorStore({
+      embedModel: {} as BaseEmbedding, // Mocking the embedModel
+      data: {
+        embeddingDict: {},
+        textIdToRefDocId: {},
+        metadataDict: nodes.reduce(
+          (acc, node) => {
+            acc[node.id_] = node.metadata;
+            return acc;
+          },
+          {} as Record<string, Metadata>,
+        ),
+      },
+    });
+  });
+
+  describe("[SimpleVectorStore] manage nodes", () => {
+    it("able to add nodes to store", async () => {
+      const ids = await store.add(nodes);
+      expect(ids).length(3);
+    });
+  });
+
+  describe("[SimpleVectorStore] query nodes", () => {
+    const testcases: FilterTestCase[] = [
+      {
+        title: "No filter",
+        expected: 3,
+      },
+      {
+        title: "Filter EQ",
+        filters: {
+          filters: [
+            {
+              key: "private",
+              value: "false",
+              operator: "==",
+            },
+          ],
+        },
+        expected: 2,
+      },
+      {
+        title: "Filter NE",
+        filters: {
+          filters: [
+            {
+              key: "private",
+              value: "false",
+              operator: "!=",
+            },
+          ],
+        },
+        expected: 1,
+      },
+      {
+        title: "Filter GT",
+        filters: {
+          filters: [
+            {
+              key: "weight",
+              value: 2.3,
+              operator: ">",
+            },
+          ],
+        },
+        expected: 1,
+      },
+      {
+        title: "Filter GTE",
+        filters: {
+          filters: [
+            {
+              key: "weight",
+              value: 2.3,
+              operator: ">=",
+            },
+          ],
+        },
+        expected: 2,
+      },
+      {
+        title: "Filter LT",
+        filters: {
+          filters: [
+            {
+              key: "weight",
+              value: 2.3,
+              operator: "<",
+            },
+          ],
+        },
+        expected: 1,
+      },
+      {
+        title: "Filter LTE",
+        filters: {
+          filters: [
+            {
+              key: "weight",
+              value: 2.3,
+              operator: "<=",
+            },
+          ],
+        },
+        expected: 2,
+      },
+      {
+        title: "Filter IN",
+        filters: {
+          filters: [
+            {
+              key: "dogId",
+              value: ["1", "3"],
+              operator: "in",
+            },
+          ],
+        },
+        expected: 2,
+      },
+      {
+        title: "Filter NIN",
+        filters: {
+          filters: [
+            {
+              key: "name",
+              value: ["Anakin", "Leia"],
+              operator: "nin",
+            },
+          ],
+        },
+        expected: 1,
+      },
+      {
+        title: "Filter ANY",
+        filters: {
+          filters: [
+            {
+              key: "type",
+              value: ["husky", "puppy"],
+              operator: "any",
+            },
+          ],
+        },
+        expected: 3,
+      },
+      {
+        title: "Filter ALL",
+        filters: {
+          filters: [
+            {
+              key: "type",
+              value: ["husky", "puppy"],
+              operator: "all",
+            },
+          ],
+        },
+        expected: 1,
+      },
+      {
+        title: "Filter CONTAINS",
+        filters: {
+          filters: [
+            {
+              key: "type",
+              value: "puppy",
+              operator: "contains",
+            },
+          ],
+        },
+        expected: 2,
+      },
+      {
+        title: "Filter TEXT_MATCH",
+        filters: {
+          filters: [
+            {
+              key: "name",
+              value: "Luk",
+              operator: "text_match",
+            },
+          ],
+        },
+        expected: 1,
+      },
+      {
+        title: "Filter OR",
+        filters: {
+          filters: [
+            {
+              key: "private",
+              value: "false",
+              operator: "==",
+            },
+            {
+              key: "dogId",
+              value: ["1", "3"],
+              operator: "in",
+            },
+          ],
+          condition: "or",
+        },
+        expected: 3,
+      },
+      {
+        title: "Filter AND",
+        filters: {
+          filters: [
+            {
+              key: "private",
+              value: "false",
+              operator: "==",
+            },
+            {
+              key: "dogId",
+              value: "10",
+              operator: "==",
+            },
+          ],
+          condition: "and",
+        },
+        expected: 0,
+      },
+    ];
+
+    testcases.forEach((tc) => {
+      it(`[${tc.title}] should return ${tc.expected} nodes`, async () => {
+        await store.add(nodes);
+        const result = await store.query({
+          queryEmbedding: [0.1, 0.2],
+          similarityTopK: 3,
+          mode: VectorStoreQueryMode.DEFAULT,
+          filters: tc.filters,
+        });
+        expect(result.ids).length(tc.expected);
+      });
+    });
+  });
+});

packages/wasm-tools/package.json

@@ -8,10 +8,10 @@
     "@types/node": "^20.12.11"
   },
   "devDependencies": {
-    "@swc/cli": "^0.3.12",
+    "@swc/cli": "^0.4.0",
     "@swc/core": "^1.6.3",
     "assemblyscript": "^0.27.27",
-    "typescript": "^5.5.2"
+    "typescript": "^5.5.3"
   },
   "engines": {
     "node": ">=18.0.0"