fastembed - npm Package Compare versions

Comparing version 2.0.0 to 2.1.0

lib/cjs/fastembed.d.ts

@@ -20,2 +20,10 @@ /// <reference types="node" />
 }
+export declare enum SparseEmbeddingModel {
+    SpladePPEnV1 = "prithivida/Splade_PP_en_v1",
+    CUSTOM = "custom"
+}
+export type SparseVector = {
+    values: number[];
+    indices: number[];
+};
 export interface InitOptionsBase {
@@ -32,2 +40,7 @@ executionProviders?: ExecutionProvider[];
 }
+interface SparseModelInfo {
+    model: SparseEmbeddingModel;
+    vocabSize: number;
+    description: string;
+}
 export interface InitStandardOptions extends InitOptionsBase {
@@ -44,2 +57,13 @@ model: Exclude<EmbeddingModel, EmbeddingModel.CUSTOM>;
 export type InitOptions = InitStandardOptions | InitCustomOptions;
+export interface InitSparseStandardOptions extends InitOptionsBase {
+    model: Exclude<SparseEmbeddingModel, SparseEmbeddingModel.CUSTOM>;
+    modelAbsoluteDirPath?: undefined;
+    modelName?: string;
+}
+export interface InitSparseCustomOptions extends InitOptionsBase {
+    model: SparseEmbeddingModel.CUSTOM;
+    modelAbsoluteDirPath: fs.PathLike;
+    modelName: string;
+}
+export type InitSparseOptions = InitSparseStandardOptions | InitSparseCustomOptions;
 declare abstract class Embedding {
@@ -51,2 +75,8 @@ abstract listSupportedModels(): ModelInfo[];
 }
+declare abstract class SparseEmbedding {
+    abstract listSupportedModels(): SparseModelInfo[];
+    abstract embed(texts: string[], batchSize?: number): AsyncGenerator<SparseVector[], void, unknown>;
+    abstract passageEmbed(texts: string[], batchSize: number): AsyncGenerator<SparseVector[], void, unknown>;
+    abstract queryEmbed(query: string): Promise<SparseVector>;
+}
 export declare class FlagEmbedding extends Embedding {
@@ -68,3 +98,18 @@ private tokenizer;
 }
+export declare class SparseTextEmbedding extends SparseEmbedding {
+    private tokenizer;
+    private session;
+    private model;
+    private vocabSize;
+    private constructor();
+    static init(options: InitSparseStandardOptions): Promise<SparseTextEmbedding>;
+    static init(options: InitSparseCustomOptions): Promise<SparseTextEmbedding>;
+    private static loadTokenizer;
+    private static retrieveModel;
+    embed(textStrings: string[], batchSize?: number): AsyncGenerator<SparseVector[], void, unknown>;
+    passageEmbed(texts: string[], batchSize?: number): AsyncGenerator<SparseVector[], void, unknown>;
+    queryEmbed(query: string): Promise<SparseVector>;
+    listSupportedModels(): SparseModelInfo[];
+}
 export {};
 //# sourceMappingURL=fastembed.d.ts.map

lib/cjs/fastembed.d.ts.map

@@ -1,1 +0,1 @@
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lib/cjs/fastembed.js

@@ -29,3 +29,3 @@ "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-exports.FlagEmbedding = exports.EmbeddingModel = exports.ExecutionProvider = void 0;
+exports.SparseTextEmbedding = exports.FlagEmbedding = exports.SparseEmbeddingModel = exports.EmbeddingModel = exports.ExecutionProvider = void 0;
 const tokenizers_1 = require("@anush008/tokenizers");
@@ -38,2 +38,3 @@ const fs_1 = __importDefault(require("fs"));
 const tar_1 = __importDefault(require("tar"));
+const hub_1 = require("@huggingface/hub");
 var ExecutionProvider;
@@ -58,2 +59,7 @@ (function (ExecutionProvider) {
 })(EmbeddingModel || (exports.EmbeddingModel = EmbeddingModel = {}));
+var SparseEmbeddingModel;
+(function (SparseEmbeddingModel) {
+    SparseEmbeddingModel["SpladePPEnV1"] = "prithivida/Splade_PP_en_v1";
+    SparseEmbeddingModel["CUSTOM"] = "custom";
+})(SparseEmbeddingModel || (exports.SparseEmbeddingModel = SparseEmbeddingModel = {}));
 function normalize(v) {
@@ -74,2 +80,4 @@ const norm = Math.sqrt(v.reduce((acc, val) => acc + val * val, 0));
 }
+class SparseEmbedding {
+}
 class FlagEmbedding extends Embedding {
@@ -331,2 +339,201 @@ constructor(tokenizer, session, model) {
 exports.FlagEmbedding = FlagEmbedding;
+// Sparse embedding implementation class
+class SparseTextEmbedding extends SparseEmbedding {
+    constructor(tokenizer, session, model, vocabSize) {
+        super();
+        this.tokenizer = tokenizer;
+        this.session = session;
+        this.model = model;
+        this.vocabSize = vocabSize;
+    }
+    static async init({ model = SparseEmbeddingModel.SpladePPEnV1, executionProviders = [ExecutionProvider.CPU], maxLength = 512, cacheDir = "local_cache", showDownloadProgress = true, modelAbsoluteDirPath = "", modelName = "", } = {}) {
+        if (model === SparseEmbeddingModel.CUSTOM) {
+            if (!modelAbsoluteDirPath) {
+                throw new Error("For custom model, modelAbsoluteDirPath is required in SparseTextEmbedding.init");
+            }
+            if (!modelName) {
+                throw new Error("For custom model, modelName is required in SparseTextEmbedding.init");
+            }
+        }
+        const modelDir = model === SparseEmbeddingModel.CUSTOM
+            ? modelAbsoluteDirPath
+            : await SparseTextEmbedding.retrieveModel(model, cacheDir, showDownloadProgress);
+        const { tokenizer, vocabSize } = this.loadTokenizer(modelDir, maxLength);
+        const defaultModelName = "model.onnx";
+        const modelPath = path_1.default.join(modelDir.toString(), "onnx", modelName || defaultModelName);
+        if (!fs_1.default.existsSync(modelPath)) {
+            throw new Error(`Model file not found at ${modelPath}`);
+        }
+        const session = await ort.InferenceSession.create(modelPath, {
+            executionProviders,
+            graphOptimizationLevel: "all",
+        });
+        return new SparseTextEmbedding(tokenizer, session, model, vocabSize);
+    }
+    static loadTokenizer(modelDir, maxLength) {
+        const tokenizerPath = path_1.default.join(modelDir.toString(), "tokenizer.json");
+        if (!fs_1.default.existsSync(tokenizerPath)) {
+            throw new Error(`Tokenizer file not found at ${tokenizerPath}`);
+        }
+        const configPath = path_1.default.join(modelDir.toString(), "config.json");
+        if (!fs_1.default.existsSync(configPath)) {
+            throw new Error(`Config file not found at ${configPath}`);
+        }
+        const config = JSON.parse(fs_1.default.readFileSync(configPath, "utf-8"));
+        const tokenizerFilePath = path_1.default.join(modelDir.toString(), "tokenizer_config.json");
+        if (!fs_1.default.existsSync(tokenizerFilePath)) {
+            throw new Error(`Tokenizer file not found at ${tokenizerFilePath}`);
+        }
+        const tokenizerConfig = JSON.parse(fs_1.default.readFileSync(tokenizerFilePath, "utf-8"));
+        maxLength = Math.min(maxLength, tokenizerConfig["model_max_length"]);
+        const tokensMapPath = path_1.default.join(modelDir.toString(), "special_tokens_map.json");
+        if (!fs_1.default.existsSync(tokensMapPath)) {
+            throw new Error(`Tokens map file not found at ${tokensMapPath}`);
+        }
+        const tokensMap = JSON.parse(fs_1.default.readFileSync(tokensMapPath, "utf-8"));
+        const tokenizer = tokenizers_1.Tokenizer.fromFile(tokenizerPath);
+        tokenizer.setTruncation(maxLength);
+        tokenizer.setPadding({
+            maxLength,
+            padId: config["pad_token_id"],
+            padToken: tokenizerConfig["pad_token"],
+        });
+        for (let token of Object.values(tokensMap)) {
+            if (typeof token === "string") {
+                tokenizer.addSpecialTokens([token]);
+            }
+            else if (isAddedTokenMap(token)) {
+                const addedToken = new tokenizers_1.AddedToken(token["content"], true, {
+                    singleWord: token["single_word"],
+                    leftStrip: token["lstrip"],
+                    rightStrip: token["rstrip"],
+                    normalized: token["normalized"],
+                });
+                tokenizer.addAddedTokens([addedToken]);
+            }
+        }
+        const vocabSize = config["vocab_size"] || 30522;
+        return { tokenizer, vocabSize };
+    }
+    static async retrieveModel(model, cacheDir, showDownloadProgress = true) {
+        if (!fs_1.default.existsSync(cacheDir)) {
+            fs_1.default.mkdirSync(cacheDir, {
+                mode: 0o777,
+            });
+        }
+        const modelDir = path_1.default.join(cacheDir.toString(), model.replace("/", "_"));
+        if (fs_1.default.existsSync(modelDir)) {
+            return modelDir;
+        }
+        fs_1.default.mkdirSync(modelDir, { mode: 0o777 });
+        // Download required files from hf
+        const filesToDownload = [
+            "onnx/model.onnx",
+            "tokenizer.json",
+            "tokenizer_config.json",
+            "config.json",
+            "special_tokens_map.json",
+        ];
+        for (const fileName of filesToDownload) {
+            const outputPath = path_1.default.join(modelDir, fileName);
+            const outputDir = path_1.default.dirname(outputPath);
+            if (!fs_1.default.existsSync(outputDir)) {
+                fs_1.default.mkdirSync(outputDir, { recursive: true, mode: 0o777 });
+            }
+            // Use HuggingFace Hub library to download
+            const downloaded = await (0, hub_1.downloadFileToCacheDir)({
+                repo: model,
+                path: fileName,
+            });
+            // Copy from HF cache to our cache directory
+            // In Node.js, downloadFile returns a string path
+            if (downloaded && typeof downloaded === "string") {
+                fs_1.default.copyFileSync(downloaded, outputPath);
+            }
+        }
+        return modelDir;
+    }
+    async *embed(textStrings, batchSize = 256) {
+        for (let i = 0; i < textStrings.length; i += batchSize) {
+            const batchTexts = textStrings.slice(i, i + batchSize);
+            const encodedTexts = await Promise.all(batchTexts.map((textString) => this.tokenizer.encode(textString)));
+            const idsArray = [];
+            const maskArray = [];
+            const typeIdsArray = [];
+            encodedTexts.forEach((text) => {
+                const ids = text.getIds().map(BigInt);
+                const mask = text.getAttentionMask();
+                const typeIds = text.getTypeIds().map(BigInt);
+                idsArray.push(ids);
+                maskArray.push(mask);
+                typeIdsArray.push(typeIds);
+            });
+            const maxLength = idsArray[0].length;
+            const batchInputIds = new ort.Tensor("int64", idsArray.flat(), [batchTexts.length, maxLength]);
+            const batchAttentionMask = new ort.Tensor("int64", maskArray.flat().map(BigInt), [batchTexts.length, maxLength]);
+            const batchTokenTypeId = new ort.Tensor("int64", typeIdsArray.flat(), [batchTexts.length, maxLength]);
+            const inputs = {
+                input_ids: batchInputIds,
+                input_mask: batchAttentionMask,
+                segment_ids: batchTokenTypeId,
+            };
+            const output = await this.session.run(inputs);
+            // SPLADE postprocessing: log(1 + ReLU(logits))
+            // @ts-expect-error this is incorrect it is there?
+            const logits = output.output.cpuData;
+            const dims = output.output.dims;
+            const [currentBatchSize, seqLen, vocabSize] = dims;
+            const sparseVectors = [];
+            for (let batchIdx = 0; batchIdx < currentBatchSize; batchIdx++) {
+                const values = new Float32Array(vocabSize).fill(0);
+                // Apply log(1 + ReLU(logits)) and max pooling
+                for (let seqIdx = 0; seqIdx < seqLen; seqIdx++) {
+                    const attentionValue = maskArray[batchIdx][seqIdx];
+                    if (attentionValue > 0) {
+                        for (let vocabIdx = 0; vocabIdx < vocabSize; vocabIdx++) {
+                            const logitIdx = batchIdx * seqLen * vocabSize + seqIdx * vocabSize + vocabIdx;
+                            const logitValue = logits[logitIdx];
+                            // ReLU
+                            const reluValue = Math.max(0, logitValue);
+                            // log(1 + ReLU)
+                            const logValue = Math.log(1 + reluValue);
+                            // Max pooling over sequence
+                            values[vocabIdx] = Math.max(values[vocabIdx], logValue);
+                        }
+                    }
+                }
+                // Convert to sparse representation (only non-zero values)
+                const sparseVector = {
+                    values: [],
+                    indices: []
+                };
+                for (let tokenId = 0; tokenId < vocabSize; tokenId++) {
+                    if (values[tokenId] > 0) {
+                        sparseVector.indices.push(tokenId);
+                        sparseVector.values.push(values[tokenId]);
+                    }
+                }
+                sparseVectors.push(sparseVector);
+            }
+            yield sparseVectors;
+        }
+    }
+    passageEmbed(texts, batchSize = 256) {
+        // SPLADE doesn't use passage/query prefixes like dense models
+        return this.embed(texts, batchSize);
+    }
+    async queryEmbed(query) {
+        return (await this.embed([query]).next()).value[0];
+    }
+    listSupportedModels() {
+        return [
+            {
+                model: SparseEmbeddingModel.SpladePPEnV1,
+                vocabSize: 30522,
+                description: "SPLADE++ English model for sparse retrieval",
+            },
+        ];
+    }
+}
+exports.SparseTextEmbedding = SparseTextEmbedding;
 function isAddedTokenMap(token) {
@@ -333,0 +540,0 @@ return (typeof token === "object" &&

lib/cjs/fastembed.js.map

@@ -1,1 +0,1 @@
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lib/cjs/index.d.ts

@@ -1,2 +0,2 @@
-export { EmbeddingModel, ExecutionProvider, FlagEmbedding, } from "./fastembed.js";
+export { EmbeddingModel, ExecutionProvider, FlagEmbedding, SparseEmbeddingModel, SparseTextEmbedding, SparseVector } from "./fastembed.js";
 //# sourceMappingURL=index.d.ts.map

lib/cjs/index.d.ts.map

@@ -1,1 +0,1 @@
-{"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,EACL,cAAc,EACd,iBAAiB,EACjB,aAAa,GACd,MAAM,gBAAgB,CAAC"}
+{"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,EACL,cAAc,EACd,iBAAiB,EACjB,aAAa,EACb,oBAAoB,EACpB,mBAAmB,EACnB,YAAY,EACb,MAAM,gBAAgB,CAAC"}

lib/cjs/index.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-exports.FlagEmbedding = exports.ExecutionProvider = exports.EmbeddingModel = void 0;
+exports.SparseTextEmbedding = exports.SparseEmbeddingModel = exports.FlagEmbedding = exports.ExecutionProvider = exports.EmbeddingModel = void 0;
 var fastembed_js_1 = require("./fastembed.js");
@@ -8,2 +8,4 @@ Object.defineProperty(exports, "EmbeddingModel", { enumerable: true, get: function () { return fastembed_js_1.EmbeddingModel; } });
 Object.defineProperty(exports, "FlagEmbedding", { enumerable: true, get: function () { return fastembed_js_1.FlagEmbedding; } });
+Object.defineProperty(exports, "SparseEmbeddingModel", { enumerable: true, get: function () { return fastembed_js_1.SparseEmbeddingModel; } });
+Object.defineProperty(exports, "SparseTextEmbedding", { enumerable: true, get: function () { return fastembed_js_1.SparseTextEmbedding; } });
 //# sourceMappingURL=index.js.map

lib/cjs/index.js.map

@@ -1,1 +0,1 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":";;;AAAA,+CAIwB;AAHtB,8GAAA,cAAc,OAAA;AACd,iHAAA,iBAAiB,OAAA;AACjB,6GAAA,aAAa,OAAA"}
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":";;;AAAA,+CAOwB;AANtB,8GAAA,cAAc,OAAA;AACd,iHAAA,iBAAiB,OAAA;AACjB,6GAAA,aAAa,OAAA;AACb,oHAAA,oBAAoB,OAAA;AACpB,mHAAA,mBAAmB,OAAA"}

lib/esm/fastembed.d.ts

@@ -20,2 +20,10 @@ /// <reference types="node" />
 }
+export declare enum SparseEmbeddingModel {
+    SpladePPEnV1 = "prithivida/Splade_PP_en_v1",
+    CUSTOM = "custom"
+}
+export type SparseVector = {
+    values: number[];
+    indices: number[];
+};
 export interface InitOptionsBase {
@@ -32,2 +40,7 @@ executionProviders?: ExecutionProvider[];
 }
+interface SparseModelInfo {
+    model: SparseEmbeddingModel;
+    vocabSize: number;
+    description: string;
+}
 export interface InitStandardOptions extends InitOptionsBase {
@@ -44,2 +57,13 @@ model: Exclude<EmbeddingModel, EmbeddingModel.CUSTOM>;
 export type InitOptions = InitStandardOptions | InitCustomOptions;
+export interface InitSparseStandardOptions extends InitOptionsBase {
+    model: Exclude<SparseEmbeddingModel, SparseEmbeddingModel.CUSTOM>;
+    modelAbsoluteDirPath?: undefined;
+    modelName?: string;
+}
+export interface InitSparseCustomOptions extends InitOptionsBase {
+    model: SparseEmbeddingModel.CUSTOM;
+    modelAbsoluteDirPath: fs.PathLike;
+    modelName: string;
+}
+export type InitSparseOptions = InitSparseStandardOptions | InitSparseCustomOptions;
 declare abstract class Embedding {
@@ -51,2 +75,8 @@ abstract listSupportedModels(): ModelInfo[];
 }
+declare abstract class SparseEmbedding {
+    abstract listSupportedModels(): SparseModelInfo[];
+    abstract embed(texts: string[], batchSize?: number): AsyncGenerator<SparseVector[], void, unknown>;
+    abstract passageEmbed(texts: string[], batchSize: number): AsyncGenerator<SparseVector[], void, unknown>;
+    abstract queryEmbed(query: string): Promise<SparseVector>;
+}
 export declare class FlagEmbedding extends Embedding {
@@ -68,3 +98,18 @@ private tokenizer;
 }
+export declare class SparseTextEmbedding extends SparseEmbedding {
+    private tokenizer;
+    private session;
+    private model;
+    private vocabSize;
+    private constructor();
+    static init(options: InitSparseStandardOptions): Promise<SparseTextEmbedding>;
+    static init(options: InitSparseCustomOptions): Promise<SparseTextEmbedding>;
+    private static loadTokenizer;
+    private static retrieveModel;
+    embed(textStrings: string[], batchSize?: number): AsyncGenerator<SparseVector[], void, unknown>;
+    passageEmbed(texts: string[], batchSize?: number): AsyncGenerator<SparseVector[], void, unknown>;
+    queryEmbed(query: string): Promise<SparseVector>;
+    listSupportedModels(): SparseModelInfo[];
+}
 export {};
 //# sourceMappingURL=fastembed.d.ts.map

lib/esm/fastembed.d.ts.map

@@ -1,1 +0,1 @@
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lib/esm/fastembed.js

@@ -8,2 +8,3 @@ import { AddedToken, Tokenizer } from "@anush008/tokenizers";
 import tar from "tar";
+import { downloadFileToCacheDir } from "@huggingface/hub";
 export var ExecutionProvider;
@@ -28,2 +29,7 @@ (function (ExecutionProvider) {
 })(EmbeddingModel || (EmbeddingModel = {}));
+export var SparseEmbeddingModel;
+(function (SparseEmbeddingModel) {
+    SparseEmbeddingModel["SpladePPEnV1"] = "prithivida/Splade_PP_en_v1";
+    SparseEmbeddingModel["CUSTOM"] = "custom";
+})(SparseEmbeddingModel || (SparseEmbeddingModel = {}));
 function normalize(v) {
@@ -44,2 +50,4 @@ const norm = Math.sqrt(v.reduce((acc, val) => acc + val * val, 0));
 }
+class SparseEmbedding {
+}
 export class FlagEmbedding extends Embedding {
@@ -300,2 +308,200 @@ constructor(tokenizer, session, model) {
 }
+// Sparse embedding implementation class
+export class SparseTextEmbedding extends SparseEmbedding {
+    constructor(tokenizer, session, model, vocabSize) {
+        super();
+        this.tokenizer = tokenizer;
+        this.session = session;
+        this.model = model;
+        this.vocabSize = vocabSize;
+    }
+    static async init({ model = SparseEmbeddingModel.SpladePPEnV1, executionProviders = [ExecutionProvider.CPU], maxLength = 512, cacheDir = "local_cache", showDownloadProgress = true, modelAbsoluteDirPath = "", modelName = "", } = {}) {
+        if (model === SparseEmbeddingModel.CUSTOM) {
+            if (!modelAbsoluteDirPath) {
+                throw new Error("For custom model, modelAbsoluteDirPath is required in SparseTextEmbedding.init");
+            }
+            if (!modelName) {
+                throw new Error("For custom model, modelName is required in SparseTextEmbedding.init");
+            }
+        }
+        const modelDir = model === SparseEmbeddingModel.CUSTOM
+            ? modelAbsoluteDirPath
+            : await SparseTextEmbedding.retrieveModel(model, cacheDir, showDownloadProgress);
+        const { tokenizer, vocabSize } = this.loadTokenizer(modelDir, maxLength);
+        const defaultModelName = "model.onnx";
+        const modelPath = path.join(modelDir.toString(), "onnx", modelName || defaultModelName);
+        if (!fs.existsSync(modelPath)) {
+            throw new Error(`Model file not found at ${modelPath}`);
+        }
+        const session = await ort.InferenceSession.create(modelPath, {
+            executionProviders,
+            graphOptimizationLevel: "all",
+        });
+        return new SparseTextEmbedding(tokenizer, session, model, vocabSize);
+    }
+    static loadTokenizer(modelDir, maxLength) {
+        const tokenizerPath = path.join(modelDir.toString(), "tokenizer.json");
+        if (!fs.existsSync(tokenizerPath)) {
+            throw new Error(`Tokenizer file not found at ${tokenizerPath}`);
+        }
+        const configPath = path.join(modelDir.toString(), "config.json");
+        if (!fs.existsSync(configPath)) {
+            throw new Error(`Config file not found at ${configPath}`);
+        }
+        const config = JSON.parse(fs.readFileSync(configPath, "utf-8"));
+        const tokenizerFilePath = path.join(modelDir.toString(), "tokenizer_config.json");
+        if (!fs.existsSync(tokenizerFilePath)) {
+            throw new Error(`Tokenizer file not found at ${tokenizerFilePath}`);
+        }
+        const tokenizerConfig = JSON.parse(fs.readFileSync(tokenizerFilePath, "utf-8"));
+        maxLength = Math.min(maxLength, tokenizerConfig["model_max_length"]);
+        const tokensMapPath = path.join(modelDir.toString(), "special_tokens_map.json");
+        if (!fs.existsSync(tokensMapPath)) {
+            throw new Error(`Tokens map file not found at ${tokensMapPath}`);
+        }
+        const tokensMap = JSON.parse(fs.readFileSync(tokensMapPath, "utf-8"));
+        const tokenizer = Tokenizer.fromFile(tokenizerPath);
+        tokenizer.setTruncation(maxLength);
+        tokenizer.setPadding({
+            maxLength,
+            padId: config["pad_token_id"],
+            padToken: tokenizerConfig["pad_token"],
+        });
+        for (let token of Object.values(tokensMap)) {
+            if (typeof token === "string") {
+                tokenizer.addSpecialTokens([token]);
+            }
+            else if (isAddedTokenMap(token)) {
+                const addedToken = new AddedToken(token["content"], true, {
+                    singleWord: token["single_word"],
+                    leftStrip: token["lstrip"],
+                    rightStrip: token["rstrip"],
+                    normalized: token["normalized"],
+                });
+                tokenizer.addAddedTokens([addedToken]);
+            }
+        }
+        const vocabSize = config["vocab_size"] || 30522;
+        return { tokenizer, vocabSize };
+    }
+    static async retrieveModel(model, cacheDir, showDownloadProgress = true) {
+        if (!fs.existsSync(cacheDir)) {
+            fs.mkdirSync(cacheDir, {
+                mode: 0o777,
+            });
+        }
+        const modelDir = path.join(cacheDir.toString(), model.replace("/", "_"));
+        if (fs.existsSync(modelDir)) {
+            return modelDir;
+        }
+        fs.mkdirSync(modelDir, { mode: 0o777 });
+        // Download required files from hf
+        const filesToDownload = [
+            "onnx/model.onnx",
+            "tokenizer.json",
+            "tokenizer_config.json",
+            "config.json",
+            "special_tokens_map.json",
+        ];
+        for (const fileName of filesToDownload) {
+            const outputPath = path.join(modelDir, fileName);
+            const outputDir = path.dirname(outputPath);
+            if (!fs.existsSync(outputDir)) {
+                fs.mkdirSync(outputDir, { recursive: true, mode: 0o777 });
+            }
+            // Use HuggingFace Hub library to download
+            const downloaded = await downloadFileToCacheDir({
+                repo: model,
+                path: fileName,
+            });
+            // Copy from HF cache to our cache directory
+            // In Node.js, downloadFile returns a string path
+            if (downloaded && typeof downloaded === "string") {
+                fs.copyFileSync(downloaded, outputPath);
+            }
+        }
+        return modelDir;
+    }
+    async *embed(textStrings, batchSize = 256) {
+        for (let i = 0; i < textStrings.length; i += batchSize) {
+            const batchTexts = textStrings.slice(i, i + batchSize);
+            const encodedTexts = await Promise.all(batchTexts.map((textString) => this.tokenizer.encode(textString)));
+            const idsArray = [];
+            const maskArray = [];
+            const typeIdsArray = [];
+            encodedTexts.forEach((text) => {
+                const ids = text.getIds().map(BigInt);
+                const mask = text.getAttentionMask();
+                const typeIds = text.getTypeIds().map(BigInt);
+                idsArray.push(ids);
+                maskArray.push(mask);
+                typeIdsArray.push(typeIds);
+            });
+            const maxLength = idsArray[0].length;
+            const batchInputIds = new ort.Tensor("int64", idsArray.flat(), [batchTexts.length, maxLength]);
+            const batchAttentionMask = new ort.Tensor("int64", maskArray.flat().map(BigInt), [batchTexts.length, maxLength]);
+            const batchTokenTypeId = new ort.Tensor("int64", typeIdsArray.flat(), [batchTexts.length, maxLength]);
+            const inputs = {
+                input_ids: batchInputIds,
+                input_mask: batchAttentionMask,
+                segment_ids: batchTokenTypeId,
+            };
+            const output = await this.session.run(inputs);
+            // SPLADE postprocessing: log(1 + ReLU(logits))
+            // @ts-expect-error this is incorrect it is there?
+            const logits = output.output.cpuData;
+            const dims = output.output.dims;
+            const [currentBatchSize, seqLen, vocabSize] = dims;
+            const sparseVectors = [];
+            for (let batchIdx = 0; batchIdx < currentBatchSize; batchIdx++) {
+                const values = new Float32Array(vocabSize).fill(0);
+                // Apply log(1 + ReLU(logits)) and max pooling
+                for (let seqIdx = 0; seqIdx < seqLen; seqIdx++) {
+                    const attentionValue = maskArray[batchIdx][seqIdx];
+                    if (attentionValue > 0) {
+                        for (let vocabIdx = 0; vocabIdx < vocabSize; vocabIdx++) {
+                            const logitIdx = batchIdx * seqLen * vocabSize + seqIdx * vocabSize + vocabIdx;
+                            const logitValue = logits[logitIdx];
+                            // ReLU
+                            const reluValue = Math.max(0, logitValue);
+                            // log(1 + ReLU)
+                            const logValue = Math.log(1 + reluValue);
+                            // Max pooling over sequence
+                            values[vocabIdx] = Math.max(values[vocabIdx], logValue);
+                        }
+                    }
+                }
+                // Convert to sparse representation (only non-zero values)
+                const sparseVector = {
+                    values: [],
+                    indices: []
+                };
+                for (let tokenId = 0; tokenId < vocabSize; tokenId++) {
+                    if (values[tokenId] > 0) {
+                        sparseVector.indices.push(tokenId);
+                        sparseVector.values.push(values[tokenId]);
+                    }
+                }
+                sparseVectors.push(sparseVector);
+            }
+            yield sparseVectors;
+        }
+    }
+    passageEmbed(texts, batchSize = 256) {
+        // SPLADE doesn't use passage/query prefixes like dense models
+        return this.embed(texts, batchSize);
+    }
+    async queryEmbed(query) {
+        return (await this.embed([query]).next()).value[0];
+    }
+    listSupportedModels() {
+        return [
+            {
+                model: SparseEmbeddingModel.SpladePPEnV1,
+                vocabSize: 30522,
+                description: "SPLADE++ English model for sparse retrieval",
+            },
+        ];
+    }
+}
 function isAddedTokenMap(token) {
@@ -302,0 +508,0 @@ return (typeof token === "object" &&

lib/esm/fastembed.js.map

@@ -1,1 +0,1 @@
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lib/esm/index.d.ts

@@ -1,2 +0,2 @@
-export { EmbeddingModel, ExecutionProvider, FlagEmbedding, } from "./fastembed.js";
+export { EmbeddingModel, ExecutionProvider, FlagEmbedding, SparseEmbeddingModel, SparseTextEmbedding, SparseVector } from "./fastembed.js";
 //# sourceMappingURL=index.d.ts.map

lib/esm/index.d.ts.map

@@ -1,1 +0,1 @@
-{"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,EACL,cAAc,EACd,iBAAiB,EACjB,aAAa,GACd,MAAM,gBAAgB,CAAC"}
+{"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,EACL,cAAc,EACd,iBAAiB,EACjB,aAAa,EACb,oBAAoB,EACpB,mBAAmB,EACnB,YAAY,EACb,MAAM,gBAAgB,CAAC"}

lib/esm/index.js

@@ -1,2 +0,2 @@
-export { EmbeddingModel, ExecutionProvider, FlagEmbedding, } from "./fastembed.js";
+export { EmbeddingModel, ExecutionProvider, FlagEmbedding, SparseEmbeddingModel, SparseTextEmbedding } from "./fastembed.js";
 //# sourceMappingURL=index.js.map

lib/esm/index.js.map

@@ -1,1 +0,1 @@
-{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,EACL,cAAc,EACd,iBAAiB,EACjB,aAAa,GACd,MAAM,gBAAgB,CAAC"}
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,EACL,cAAc,EACd,iBAAiB,EACjB,aAAa,EACb,oBAAoB,EACpB,mBAAmB,EAEpB,MAAM,gBAAgB,CAAC"}

package.json

 {
   "name": "fastembed",
-  "version": "2.0.0",
+  "version": "2.1.0",
   "description": "NodeJS implementation of @Qdrant/fastembed",
@@ -42,2 +42,3 @@ "keywords": [
     "@anush008/tokenizers": "^0.0.0",
+    "@huggingface/hub": "^2.7.1",
     "onnxruntime-node": "1.21.0",
@@ -44,0 +45,0 @@ "progress": "^2.0.3",

No alert changes

Improved metrics

Total package byte prevSize

109291

64.8%

Lines of code

1283

64.91%

Worsened metrics

Dependency count

5

25%

Dependency changes

• + Added

  @huggingface/hub@^2.7.1

• + Added

  @huggingface/blake3-jit@0.0.2
  (transitive)

• + Added

  @huggingface/hub@2.13.3
  (transitive)

• + Added

  @huggingface/tasks@0.21.27
  (transitive)

• + Added

  @huggingface/xetchunk-wasm@0.1.0
  (transitive)

• + Added

  ansi-regex@5.0.1
  (transitive)

• + Added

  cli-progress@3.12.0
  (transitive)

• + Added

  emoji-regex@8.0.0
  (transitive)

• + Added

  gearhash-jit@1.0.2
  (transitive)

• + Added

  is-fullwidth-code-point@3.0.0
  (transitive)

• + Added

  string-width@4.2.3
  (transitive)

• + Added

  strip-ansi@6.0.1
  (transitive)