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fastembed - npm Package Compare versions
+135
-168
@@ -25,23 +25,2 @@ "use strict"; | ||
| }; | ||
| var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) { | ||
| function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } | ||
| return new (P || (P = Promise))(function (resolve, reject) { | ||
| function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } | ||
| function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } | ||
| function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } | ||
| step((generator = generator.apply(thisArg, _arguments || [])).next()); | ||
| }); | ||
| }; | ||
| var __await = (this && this.__await) || function (v) { return this instanceof __await ? (this.v = v, this) : new __await(v); } | ||
| var __asyncGenerator = (this && this.__asyncGenerator) || function (thisArg, _arguments, generator) { | ||
| if (!Symbol.asyncIterator) throw new TypeError("Symbol.asyncIterator is not defined."); | ||
| var g = generator.apply(thisArg, _arguments || []), i, q = []; | ||
| return i = {}, verb("next"), verb("throw"), verb("return"), i[Symbol.asyncIterator] = function () { return this; }, i; | ||
| function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; } | ||
| function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } } | ||
| function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); } | ||
| function fulfill(value) { resume("next", value); } | ||
| function reject(value) { resume("throw", value); } | ||
| function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); } | ||
| }; | ||
| var __importDefault = (this && this.__importDefault) || function (mod) { | ||
@@ -122,19 +101,17 @@ return (mod && mod.__esModule) ? mod : { "default": mod }; | ||
| } | ||
| static init({ model = EmbeddingModel.BGESmallENV15, executionProviders = [ExecutionProvider.CPU], maxLength = 512, cacheDir = "local_cache", showDownloadProgress = true, } = {}) { | ||
| return __awaiter(this, void 0, void 0, function* () { | ||
| const modelDir = yield FlagEmbedding.retrieveModel(model, cacheDir, showDownloadProgress); | ||
| const tokenizer = this.loadTokenizer(modelDir, maxLength); | ||
| const modelPath = path_1.default.join(modelDir.toString(), model === EmbeddingModel.MLE5Large || | ||
| model === EmbeddingModel.AllMiniLML6V2 | ||
| ? "model.onnx" | ||
| : "model_optimized.onnx"); | ||
| if (!fs_1.default.existsSync(modelPath)) { | ||
| throw new Error(`Model file not found at ${modelPath}`); | ||
| } | ||
| const session = yield ort.InferenceSession.create(modelPath, { | ||
| executionProviders, | ||
| graphOptimizationLevel: "all", | ||
| }); | ||
| return new FlagEmbedding(tokenizer, session, model); | ||
| static async init({ model = EmbeddingModel.BGESmallENV15, executionProviders = [ExecutionProvider.CPU], maxLength = 512, cacheDir = "local_cache", showDownloadProgress = true, } = {}) { | ||
| const modelDir = await FlagEmbedding.retrieveModel(model, cacheDir, showDownloadProgress); | ||
| const tokenizer = this.loadTokenizer(modelDir, maxLength); | ||
| const modelPath = path_1.default.join(modelDir.toString(), model === EmbeddingModel.MLE5Large || | ||
| model === EmbeddingModel.AllMiniLML6V2 | ||
| ? "model.onnx" | ||
| : "model_optimized.onnx"); | ||
| if (!fs_1.default.existsSync(modelPath)) { | ||
| throw new Error(`Model file not found at ${modelPath}`); | ||
| } | ||
| const session = await ort.InferenceSession.create(modelPath, { | ||
| executionProviders, | ||
| graphOptimizationLevel: "all", | ||
| }); | ||
| return new FlagEmbedding(tokenizer, session, model); | ||
| } | ||
@@ -185,139 +162,131 @@ static loadTokenizer(modelDir, maxLength) { | ||
| } | ||
| static downloadFileFromGCS(outputFilePath, model, showDownloadProgress = true) { | ||
| return __awaiter(this, void 0, void 0, function* () { | ||
| if (fs_1.default.existsSync(outputFilePath)) { | ||
| return outputFilePath; | ||
| } | ||
| // The AllMiniLML6V2 model URL doesn't follow the same naming convention as the other models | ||
| // So, we transform "fast-all-MiniLM-L6-v2" -> "sentence-transformers-all-MiniLM-L6-v2" in the download URL | ||
| // The model directory name in the GCS storage remains "fast-all-MiniLM-L6-v2" | ||
| if (model === EmbeddingModel.AllMiniLML6V2) { | ||
| model = "sentence-transformers" + model.substring(model.indexOf("-")); | ||
| } | ||
| const url = `https://storage.googleapis.com/qdrant-fastembed/${model}.tar.gz`; | ||
| const fileStream = fs_1.default.createWriteStream(outputFilePath); | ||
| return new Promise((resolve, reject) => { | ||
| https_1.default | ||
| .get(url, { headers: { "User-Agent": "Mozilla/5.0" } }, (response) => { | ||
| const totalSizeInBytes = parseInt(response.headers["content-length"] || "0", 10); | ||
| if (totalSizeInBytes === 0) { | ||
| console.warn(`Warning: Content-length header is missing or zero in the response from ${url}.`); | ||
| } | ||
| if (showDownloadProgress) { | ||
| const progressBar = new progress_1.default(`Downloading ${model} [:bar] :percent :etas`, { | ||
| complete: "=", | ||
| width: 20, | ||
| total: totalSizeInBytes, | ||
| }); | ||
| response.on("data", (chunk) => { | ||
| progressBar.tick(chunk.length, { speed: "N/A" }); | ||
| }); | ||
| } | ||
| response.on("error", (error) => { | ||
| reject(error); | ||
| static async downloadFileFromGCS(outputFilePath, model, showDownloadProgress = true) { | ||
| if (fs_1.default.existsSync(outputFilePath)) { | ||
| return outputFilePath; | ||
| } | ||
| // The AllMiniLML6V2 model URL doesn't follow the same naming convention as the other models | ||
| // So, we transform "fast-all-MiniLM-L6-v2" -> "sentence-transformers-all-MiniLM-L6-v2" in the download URL | ||
| // The model directory name in the GCS storage remains "fast-all-MiniLM-L6-v2" | ||
| if (model === EmbeddingModel.AllMiniLML6V2) { | ||
| model = "sentence-transformers" + model.substring(model.indexOf("-")); | ||
| } | ||
| const url = `https://storage.googleapis.com/qdrant-fastembed/${model}.tar.gz`; | ||
| const fileStream = fs_1.default.createWriteStream(outputFilePath); | ||
| return new Promise((resolve, reject) => { | ||
| https_1.default | ||
| .get(url, { headers: { "User-Agent": "Mozilla/5.0" } }, (response) => { | ||
| const totalSizeInBytes = parseInt(response.headers["content-length"] || "0", 10); | ||
| if (totalSizeInBytes === 0) { | ||
| console.warn(`Warning: Content-length header is missing or zero in the response from ${url}.`); | ||
| } | ||
| if (showDownloadProgress) { | ||
| const progressBar = new progress_1.default(`Downloading ${model} [:bar] :percent :etas`, { | ||
| complete: "=", | ||
| width: 20, | ||
| total: totalSizeInBytes, | ||
| }); | ||
| response.pipe(fileStream); | ||
| fileStream.on("finish", () => { | ||
| fileStream.close(); | ||
| resolve(outputFilePath); | ||
| response.on("data", (chunk) => { | ||
| progressBar.tick(chunk.length, { speed: "N/A" }); | ||
| }); | ||
| fileStream.on("error", (error) => { | ||
| reject(error); | ||
| }); | ||
| }) | ||
| .on("error", (error) => { | ||
| fs_1.default.unlink(outputFilePath, () => { | ||
| reject(error); | ||
| }); | ||
| } | ||
| response.on("error", (error) => { | ||
| reject(error); | ||
| }); | ||
| response.pipe(fileStream); | ||
| fileStream.on("finish", () => { | ||
| fileStream.close(); | ||
| resolve(outputFilePath); | ||
| }); | ||
| fileStream.on("error", (error) => { | ||
| reject(error); | ||
| }); | ||
| }) | ||
| .on("error", (error) => { | ||
| fs_1.default.unlink(outputFilePath, () => { | ||
| reject(error); | ||
| }); | ||
| }); | ||
| }); | ||
| } | ||
| static decompressToCache(targzPath, cacheDir) { | ||
| return __awaiter(this, void 0, void 0, function* () { | ||
| // Implementation for decompressing a .tar.gz file to a cache directory | ||
| if (path_1.default.extname(targzPath.toString()) === ".gz") { | ||
| yield tar_1.default.x({ | ||
| file: targzPath, | ||
| // @ts-ignore | ||
| cwd: cacheDir, | ||
| }); | ||
| } | ||
| else { | ||
| throw new Error(`Unsupported file extension: ${targzPath}`); | ||
| } | ||
| }); | ||
| static async decompressToCache(targzPath, cacheDir) { | ||
| // Implementation for decompressing a .tar.gz file to a cache directory | ||
| if (path_1.default.extname(targzPath.toString()) === ".gz") { | ||
| await tar_1.default.x({ | ||
| file: targzPath, | ||
| // @ts-ignore | ||
| cwd: cacheDir, | ||
| }); | ||
| } | ||
| else { | ||
| throw new Error(`Unsupported file extension: ${targzPath}`); | ||
| } | ||
| } | ||
| static retrieveModel(model, cacheDir, showDownloadProgress = true) { | ||
| return __awaiter(this, void 0, void 0, function* () { | ||
| if (!fs_1.default.existsSync(cacheDir)) { | ||
| fs_1.default.mkdirSync(cacheDir, { | ||
| mode: 0o777, | ||
| }); | ||
| } | ||
| const modelDir = path_1.default.join(cacheDir.toString(), model); | ||
| if (fs_1.default.existsSync(modelDir)) { | ||
| return modelDir; | ||
| } | ||
| const modelTarGz = path_1.default.join(cacheDir.toString(), `${model}.tar.gz`); | ||
| yield this.downloadFileFromGCS(modelTarGz, model, showDownloadProgress); | ||
| yield this.decompressToCache(modelTarGz, cacheDir); | ||
| fs_1.default.unlinkSync(modelTarGz); | ||
| 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); | ||
| if (fs_1.default.existsSync(modelDir)) { | ||
| return modelDir; | ||
| }); | ||
| } | ||
| const modelTarGz = path_1.default.join(cacheDir.toString(), `${model}.tar.gz`); | ||
| await this.downloadFileFromGCS(modelTarGz, model, showDownloadProgress); | ||
| await this.decompressToCache(modelTarGz, cacheDir); | ||
| fs_1.default.unlinkSync(modelTarGz); | ||
| return modelDir; | ||
| } | ||
| embed(textStrings, batchSize = 256) { | ||
| return __asyncGenerator(this, arguments, function* embed_1() { | ||
| for (let i = 0; i < textStrings.length; i += batchSize) { | ||
| const batchTexts = textStrings.slice(i, i + batchSize); | ||
| const encodedTexts = yield __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().map(BigInt); | ||
| 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(), [batchTexts.length, maxLength]); | ||
| const batchTokenTypeId = new ort.Tensor("int64", typeIdsArray.flat(), [batchTexts.length, maxLength]); | ||
| const inputs = { | ||
| input_ids: batchInputIds, | ||
| attention_mask: batchAttentionMask, | ||
| token_type_ids: batchTokenTypeId, | ||
| }; | ||
| // Exclude token_type_ids for MLE5Large | ||
| if (this.model === EmbeddingModel.MLE5Large) { | ||
| delete inputs.token_type_ids; | ||
| } | ||
| const output = yield __await(this.session.run(inputs)); | ||
| // Remove attention pooling | ||
| // Ref: https://github.com/qdrant/fastembed/commit/a335c8898f11042fdb311fce2dab3acf50c23011 | ||
| // const lastHiddenState: number[][][] = create3DArray( | ||
| // output.last_hidden_state.data as unknown[] as number[], | ||
| // output.last_hidden_state.dims as number[] | ||
| // ); | ||
| // const embeddings = lastHiddenState.map((layer, layerIdx) => { | ||
| // const weightedSum = layer.reduce((acc, tokenEmbedding, idx) => { | ||
| // const attentionWeight = maskArray[layerIdx][idx]; | ||
| // return acc.map( | ||
| // (val, i) => val + tokenEmbedding[i] * Number(attentionWeight) | ||
| // ); | ||
| // }, new Array(layer[0].length).fill(0)); | ||
| // const inputMaskSum = maskArray[layerIdx].reduce( | ||
| // (acc, attentionWeight) => acc + Number(attentionWeight), | ||
| // 0 | ||
| // ); | ||
| // return weightedSum.map((val) => val / Math.max(inputMaskSum, 1e-9)); | ||
| // }); | ||
| // const embeddings = lastHiddenState.map((sentence) => sentence[0]); | ||
| const embeddings = getEmbeddings(output.last_hidden_state.data, output.last_hidden_state.dims); | ||
| yield yield __await(embeddings.map(normalize)); | ||
| 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().map(BigInt); | ||
| 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(), [batchTexts.length, maxLength]); | ||
| const batchTokenTypeId = new ort.Tensor("int64", typeIdsArray.flat(), [batchTexts.length, maxLength]); | ||
| const inputs = { | ||
| input_ids: batchInputIds, | ||
| attention_mask: batchAttentionMask, | ||
| token_type_ids: batchTokenTypeId, | ||
| }; | ||
| // Exclude token_type_ids for MLE5Large | ||
| if (this.model === EmbeddingModel.MLE5Large) { | ||
| delete inputs.token_type_ids; | ||
| } | ||
| }); | ||
| const output = await this.session.run(inputs); | ||
| // Remove attention pooling | ||
| // Ref: https://github.com/qdrant/fastembed/commit/a335c8898f11042fdb311fce2dab3acf50c23011 | ||
| // const lastHiddenState: number[][][] = create3DArray( | ||
| // output.last_hidden_state.data as unknown[] as number[], | ||
| // output.last_hidden_state.dims as number[] | ||
| // ); | ||
| // const embeddings = lastHiddenState.map((layer, layerIdx) => { | ||
| // const weightedSum = layer.reduce((acc, tokenEmbedding, idx) => { | ||
| // const attentionWeight = maskArray[layerIdx][idx]; | ||
| // return acc.map( | ||
| // (val, i) => val + tokenEmbedding[i] * Number(attentionWeight) | ||
| // ); | ||
| // }, new Array(layer[0].length).fill(0)); | ||
| // const inputMaskSum = maskArray[layerIdx].reduce( | ||
| // (acc, attentionWeight) => acc + Number(attentionWeight), | ||
| // 0 | ||
| // ); | ||
| // return weightedSum.map((val) => val / Math.max(inputMaskSum, 1e-9)); | ||
| // }); | ||
| // const embeddings = lastHiddenState.map((sentence) => sentence[0]); | ||
| const embeddings = getEmbeddings(output.last_hidden_state.data, output.last_hidden_state.dims); | ||
| yield embeddings.map(normalize); | ||
| } | ||
| } | ||
@@ -328,6 +297,4 @@ passageEmbed(texts, batchSize = 256) { | ||
| } | ||
| queryEmbed(query) { | ||
| return __awaiter(this, void 0, void 0, function* () { | ||
| return (yield this.embed([`query: ${query}`]).next()).value[0]; | ||
| }); | ||
| async queryEmbed(query) { | ||
| return (await this.embed([`query: ${query}`]).next()).value[0]; | ||
| } | ||
@@ -334,0 +301,0 @@ listSupportedModels() { |
@@ -1,1 +0,1 @@ | ||
| 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|
@@ -1,3 +0,2 @@ | ||
| import { EmbeddingModel, ExecutionProvider, FlagEmbedding } from "./fastembed"; | ||
| export { EmbeddingModel, ExecutionProvider, FlagEmbedding }; | ||
| export { EmbeddingModel, ExecutionProvider, FlagEmbedding, } from "./fastembed.js"; | ||
| //# sourceMappingURL=index.d.ts.map |
@@ -1,1 +0,1 @@ | ||
| {"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,cAAc,EAAE,iBAAiB,EAAE,aAAa,EAAE,MAAM,aAAa,CAAC;AAC/E,OAAO,EAAE,cAAc,EAAE,iBAAiB,EAAE,aAAa,EAAE,CAAC"} | ||
| {"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"} |
+4
-4
| "use strict"; | ||
| Object.defineProperty(exports, "__esModule", { value: true }); | ||
| exports.FlagEmbedding = exports.ExecutionProvider = exports.EmbeddingModel = void 0; | ||
| const fastembed_1 = require("./fastembed"); | ||
| Object.defineProperty(exports, "EmbeddingModel", { enumerable: true, get: function () { return fastembed_1.EmbeddingModel; } }); | ||
| Object.defineProperty(exports, "ExecutionProvider", { enumerable: true, get: function () { return fastembed_1.ExecutionProvider; } }); | ||
| Object.defineProperty(exports, "FlagEmbedding", { enumerable: true, get: function () { return fastembed_1.FlagEmbedding; } }); | ||
| var fastembed_js_1 = require("./fastembed.js"); | ||
| Object.defineProperty(exports, "EmbeddingModel", { enumerable: true, get: function () { return fastembed_js_1.EmbeddingModel; } }); | ||
| Object.defineProperty(exports, "ExecutionProvider", { enumerable: true, get: function () { return fastembed_js_1.ExecutionProvider; } }); | ||
| Object.defineProperty(exports, "FlagEmbedding", { enumerable: true, get: function () { return fastembed_js_1.FlagEmbedding; } }); | ||
| //# sourceMappingURL=index.js.map |
@@ -1,1 +0,1 @@ | ||
| {"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":";;;AAAA,2CAA+E;AACtE,+FADA,0BAAc,OACA;AAAE,kGADA,6BAAiB,OACA;AAAE,8FADA,yBAAa,OACA"} | ||
| {"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"} |
+135
-168
@@ -1,22 +0,1 @@ | ||
| var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) { | ||
| function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } | ||
| return new (P || (P = Promise))(function (resolve, reject) { | ||
| function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } | ||
| function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } | ||
| function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } | ||
| step((generator = generator.apply(thisArg, _arguments || [])).next()); | ||
| }); | ||
| }; | ||
| var __await = (this && this.__await) || function (v) { return this instanceof __await ? (this.v = v, this) : new __await(v); } | ||
| var __asyncGenerator = (this && this.__asyncGenerator) || function (thisArg, _arguments, generator) { | ||
| if (!Symbol.asyncIterator) throw new TypeError("Symbol.asyncIterator is not defined."); | ||
| var g = generator.apply(thisArg, _arguments || []), i, q = []; | ||
| return i = {}, verb("next"), verb("throw"), verb("return"), i[Symbol.asyncIterator] = function () { return this; }, i; | ||
| function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; } | ||
| function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } } | ||
| function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); } | ||
| function fulfill(value) { resume("next", value); } | ||
| function reject(value) { resume("throw", value); } | ||
| function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); } | ||
| }; | ||
| import fs from "fs"; | ||
@@ -92,19 +71,17 @@ import https from "https"; | ||
| } | ||
| static init({ model = EmbeddingModel.BGESmallENV15, executionProviders = [ExecutionProvider.CPU], maxLength = 512, cacheDir = "local_cache", showDownloadProgress = true, } = {}) { | ||
| return __awaiter(this, void 0, void 0, function* () { | ||
| const modelDir = yield FlagEmbedding.retrieveModel(model, cacheDir, showDownloadProgress); | ||
| const tokenizer = this.loadTokenizer(modelDir, maxLength); | ||
| const modelPath = path.join(modelDir.toString(), model === EmbeddingModel.MLE5Large || | ||
| model === EmbeddingModel.AllMiniLML6V2 | ||
| ? "model.onnx" | ||
| : "model_optimized.onnx"); | ||
| if (!fs.existsSync(modelPath)) { | ||
| throw new Error(`Model file not found at ${modelPath}`); | ||
| } | ||
| const session = yield ort.InferenceSession.create(modelPath, { | ||
| executionProviders, | ||
| graphOptimizationLevel: "all", | ||
| }); | ||
| return new FlagEmbedding(tokenizer, session, model); | ||
| static async init({ model = EmbeddingModel.BGESmallENV15, executionProviders = [ExecutionProvider.CPU], maxLength = 512, cacheDir = "local_cache", showDownloadProgress = true, } = {}) { | ||
| const modelDir = await FlagEmbedding.retrieveModel(model, cacheDir, showDownloadProgress); | ||
| const tokenizer = this.loadTokenizer(modelDir, maxLength); | ||
| const modelPath = path.join(modelDir.toString(), model === EmbeddingModel.MLE5Large || | ||
| model === EmbeddingModel.AllMiniLML6V2 | ||
| ? "model.onnx" | ||
| : "model_optimized.onnx"); | ||
| if (!fs.existsSync(modelPath)) { | ||
| throw new Error(`Model file not found at ${modelPath}`); | ||
| } | ||
| const session = await ort.InferenceSession.create(modelPath, { | ||
| executionProviders, | ||
| graphOptimizationLevel: "all", | ||
| }); | ||
| return new FlagEmbedding(tokenizer, session, model); | ||
| } | ||
@@ -155,139 +132,131 @@ static loadTokenizer(modelDir, maxLength) { | ||
| } | ||
| static downloadFileFromGCS(outputFilePath, model, showDownloadProgress = true) { | ||
| return __awaiter(this, void 0, void 0, function* () { | ||
| if (fs.existsSync(outputFilePath)) { | ||
| return outputFilePath; | ||
| } | ||
| // The AllMiniLML6V2 model URL doesn't follow the same naming convention as the other models | ||
| // So, we transform "fast-all-MiniLM-L6-v2" -> "sentence-transformers-all-MiniLM-L6-v2" in the download URL | ||
| // The model directory name in the GCS storage remains "fast-all-MiniLM-L6-v2" | ||
| if (model === EmbeddingModel.AllMiniLML6V2) { | ||
| model = "sentence-transformers" + model.substring(model.indexOf("-")); | ||
| } | ||
| const url = `https://storage.googleapis.com/qdrant-fastembed/${model}.tar.gz`; | ||
| const fileStream = fs.createWriteStream(outputFilePath); | ||
| return new Promise((resolve, reject) => { | ||
| https | ||
| .get(url, { headers: { "User-Agent": "Mozilla/5.0" } }, (response) => { | ||
| const totalSizeInBytes = parseInt(response.headers["content-length"] || "0", 10); | ||
| if (totalSizeInBytes === 0) { | ||
| console.warn(`Warning: Content-length header is missing or zero in the response from ${url}.`); | ||
| } | ||
| if (showDownloadProgress) { | ||
| const progressBar = new Progress(`Downloading ${model} [:bar] :percent :etas`, { | ||
| complete: "=", | ||
| width: 20, | ||
| total: totalSizeInBytes, | ||
| }); | ||
| response.on("data", (chunk) => { | ||
| progressBar.tick(chunk.length, { speed: "N/A" }); | ||
| }); | ||
| } | ||
| response.on("error", (error) => { | ||
| reject(error); | ||
| static async downloadFileFromGCS(outputFilePath, model, showDownloadProgress = true) { | ||
| if (fs.existsSync(outputFilePath)) { | ||
| return outputFilePath; | ||
| } | ||
| // The AllMiniLML6V2 model URL doesn't follow the same naming convention as the other models | ||
| // So, we transform "fast-all-MiniLM-L6-v2" -> "sentence-transformers-all-MiniLM-L6-v2" in the download URL | ||
| // The model directory name in the GCS storage remains "fast-all-MiniLM-L6-v2" | ||
| if (model === EmbeddingModel.AllMiniLML6V2) { | ||
| model = "sentence-transformers" + model.substring(model.indexOf("-")); | ||
| } | ||
| const url = `https://storage.googleapis.com/qdrant-fastembed/${model}.tar.gz`; | ||
| const fileStream = fs.createWriteStream(outputFilePath); | ||
| return new Promise((resolve, reject) => { | ||
| https | ||
| .get(url, { headers: { "User-Agent": "Mozilla/5.0" } }, (response) => { | ||
| const totalSizeInBytes = parseInt(response.headers["content-length"] || "0", 10); | ||
| if (totalSizeInBytes === 0) { | ||
| console.warn(`Warning: Content-length header is missing or zero in the response from ${url}.`); | ||
| } | ||
| if (showDownloadProgress) { | ||
| const progressBar = new Progress(`Downloading ${model} [:bar] :percent :etas`, { | ||
| complete: "=", | ||
| width: 20, | ||
| total: totalSizeInBytes, | ||
| }); | ||
| response.pipe(fileStream); | ||
| fileStream.on("finish", () => { | ||
| fileStream.close(); | ||
| resolve(outputFilePath); | ||
| response.on("data", (chunk) => { | ||
| progressBar.tick(chunk.length, { speed: "N/A" }); | ||
| }); | ||
| fileStream.on("error", (error) => { | ||
| reject(error); | ||
| }); | ||
| }) | ||
| .on("error", (error) => { | ||
| fs.unlink(outputFilePath, () => { | ||
| reject(error); | ||
| }); | ||
| } | ||
| response.on("error", (error) => { | ||
| reject(error); | ||
| }); | ||
| response.pipe(fileStream); | ||
| fileStream.on("finish", () => { | ||
| fileStream.close(); | ||
| resolve(outputFilePath); | ||
| }); | ||
| fileStream.on("error", (error) => { | ||
| reject(error); | ||
| }); | ||
| }) | ||
| .on("error", (error) => { | ||
| fs.unlink(outputFilePath, () => { | ||
| reject(error); | ||
| }); | ||
| }); | ||
| }); | ||
| } | ||
| static decompressToCache(targzPath, cacheDir) { | ||
| return __awaiter(this, void 0, void 0, function* () { | ||
| // Implementation for decompressing a .tar.gz file to a cache directory | ||
| if (path.extname(targzPath.toString()) === ".gz") { | ||
| yield tar.x({ | ||
| file: targzPath, | ||
| // @ts-ignore | ||
| cwd: cacheDir, | ||
| }); | ||
| } | ||
| else { | ||
| throw new Error(`Unsupported file extension: ${targzPath}`); | ||
| } | ||
| }); | ||
| static async decompressToCache(targzPath, cacheDir) { | ||
| // Implementation for decompressing a .tar.gz file to a cache directory | ||
| if (path.extname(targzPath.toString()) === ".gz") { | ||
| await tar.x({ | ||
| file: targzPath, | ||
| // @ts-ignore | ||
| cwd: cacheDir, | ||
| }); | ||
| } | ||
| else { | ||
| throw new Error(`Unsupported file extension: ${targzPath}`); | ||
| } | ||
| } | ||
| static retrieveModel(model, cacheDir, showDownloadProgress = true) { | ||
| return __awaiter(this, void 0, void 0, function* () { | ||
| if (!fs.existsSync(cacheDir)) { | ||
| fs.mkdirSync(cacheDir, { | ||
| mode: 0o777, | ||
| }); | ||
| } | ||
| const modelDir = path.join(cacheDir.toString(), model); | ||
| if (fs.existsSync(modelDir)) { | ||
| return modelDir; | ||
| } | ||
| const modelTarGz = path.join(cacheDir.toString(), `${model}.tar.gz`); | ||
| yield this.downloadFileFromGCS(modelTarGz, model, showDownloadProgress); | ||
| yield this.decompressToCache(modelTarGz, cacheDir); | ||
| fs.unlinkSync(modelTarGz); | ||
| static async retrieveModel(model, cacheDir, showDownloadProgress = true) { | ||
| if (!fs.existsSync(cacheDir)) { | ||
| fs.mkdirSync(cacheDir, { | ||
| mode: 0o777, | ||
| }); | ||
| } | ||
| const modelDir = path.join(cacheDir.toString(), model); | ||
| if (fs.existsSync(modelDir)) { | ||
| return modelDir; | ||
| }); | ||
| } | ||
| const modelTarGz = path.join(cacheDir.toString(), `${model}.tar.gz`); | ||
| await this.downloadFileFromGCS(modelTarGz, model, showDownloadProgress); | ||
| await this.decompressToCache(modelTarGz, cacheDir); | ||
| fs.unlinkSync(modelTarGz); | ||
| return modelDir; | ||
| } | ||
| embed(textStrings, batchSize = 256) { | ||
| return __asyncGenerator(this, arguments, function* embed_1() { | ||
| for (let i = 0; i < textStrings.length; i += batchSize) { | ||
| const batchTexts = textStrings.slice(i, i + batchSize); | ||
| const encodedTexts = yield __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().map(BigInt); | ||
| 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(), [batchTexts.length, maxLength]); | ||
| const batchTokenTypeId = new ort.Tensor("int64", typeIdsArray.flat(), [batchTexts.length, maxLength]); | ||
| const inputs = { | ||
| input_ids: batchInputIds, | ||
| attention_mask: batchAttentionMask, | ||
| token_type_ids: batchTokenTypeId, | ||
| }; | ||
| // Exclude token_type_ids for MLE5Large | ||
| if (this.model === EmbeddingModel.MLE5Large) { | ||
| delete inputs.token_type_ids; | ||
| } | ||
| const output = yield __await(this.session.run(inputs)); | ||
| // Remove attention pooling | ||
| // Ref: https://github.com/qdrant/fastembed/commit/a335c8898f11042fdb311fce2dab3acf50c23011 | ||
| // const lastHiddenState: number[][][] = create3DArray( | ||
| // output.last_hidden_state.data as unknown[] as number[], | ||
| // output.last_hidden_state.dims as number[] | ||
| // ); | ||
| // const embeddings = lastHiddenState.map((layer, layerIdx) => { | ||
| // const weightedSum = layer.reduce((acc, tokenEmbedding, idx) => { | ||
| // const attentionWeight = maskArray[layerIdx][idx]; | ||
| // return acc.map( | ||
| // (val, i) => val + tokenEmbedding[i] * Number(attentionWeight) | ||
| // ); | ||
| // }, new Array(layer[0].length).fill(0)); | ||
| // const inputMaskSum = maskArray[layerIdx].reduce( | ||
| // (acc, attentionWeight) => acc + Number(attentionWeight), | ||
| // 0 | ||
| // ); | ||
| // return weightedSum.map((val) => val / Math.max(inputMaskSum, 1e-9)); | ||
| // }); | ||
| // const embeddings = lastHiddenState.map((sentence) => sentence[0]); | ||
| const embeddings = getEmbeddings(output.last_hidden_state.data, output.last_hidden_state.dims); | ||
| yield yield __await(embeddings.map(normalize)); | ||
| 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().map(BigInt); | ||
| 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(), [batchTexts.length, maxLength]); | ||
| const batchTokenTypeId = new ort.Tensor("int64", typeIdsArray.flat(), [batchTexts.length, maxLength]); | ||
| const inputs = { | ||
| input_ids: batchInputIds, | ||
| attention_mask: batchAttentionMask, | ||
| token_type_ids: batchTokenTypeId, | ||
| }; | ||
| // Exclude token_type_ids for MLE5Large | ||
| if (this.model === EmbeddingModel.MLE5Large) { | ||
| delete inputs.token_type_ids; | ||
| } | ||
| }); | ||
| const output = await this.session.run(inputs); | ||
| // Remove attention pooling | ||
| // Ref: https://github.com/qdrant/fastembed/commit/a335c8898f11042fdb311fce2dab3acf50c23011 | ||
| // const lastHiddenState: number[][][] = create3DArray( | ||
| // output.last_hidden_state.data as unknown[] as number[], | ||
| // output.last_hidden_state.dims as number[] | ||
| // ); | ||
| // const embeddings = lastHiddenState.map((layer, layerIdx) => { | ||
| // const weightedSum = layer.reduce((acc, tokenEmbedding, idx) => { | ||
| // const attentionWeight = maskArray[layerIdx][idx]; | ||
| // return acc.map( | ||
| // (val, i) => val + tokenEmbedding[i] * Number(attentionWeight) | ||
| // ); | ||
| // }, new Array(layer[0].length).fill(0)); | ||
| // const inputMaskSum = maskArray[layerIdx].reduce( | ||
| // (acc, attentionWeight) => acc + Number(attentionWeight), | ||
| // 0 | ||
| // ); | ||
| // return weightedSum.map((val) => val / Math.max(inputMaskSum, 1e-9)); | ||
| // }); | ||
| // const embeddings = lastHiddenState.map((sentence) => sentence[0]); | ||
| const embeddings = getEmbeddings(output.last_hidden_state.data, output.last_hidden_state.dims); | ||
| yield embeddings.map(normalize); | ||
| } | ||
| } | ||
@@ -298,6 +267,4 @@ passageEmbed(texts, batchSize = 256) { | ||
| } | ||
| queryEmbed(query) { | ||
| return __awaiter(this, void 0, void 0, function* () { | ||
| return (yield this.embed([`query: ${query}`]).next()).value[0]; | ||
| }); | ||
| async queryEmbed(query) { | ||
| return (await this.embed([`query: ${query}`]).next()).value[0]; | ||
| } | ||
@@ -304,0 +271,0 @@ listSupportedModels() { |
@@ -1,1 +0,1 @@ | ||
| 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|
@@ -1,3 +0,2 @@ | ||
| import { EmbeddingModel, ExecutionProvider, FlagEmbedding } from "./fastembed"; | ||
| export { EmbeddingModel, ExecutionProvider, FlagEmbedding }; | ||
| export { EmbeddingModel, ExecutionProvider, FlagEmbedding, } from "./fastembed.js"; | ||
| //# sourceMappingURL=index.d.ts.map |
@@ -1,1 +0,1 @@ | ||
| {"version":3,"file":"index.d.ts","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,cAAc,EAAE,iBAAiB,EAAE,aAAa,EAAE,MAAM,aAAa,CAAC;AAC/E,OAAO,EAAE,cAAc,EAAE,iBAAiB,EAAE,aAAa,EAAE,CAAC"} | ||
| {"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"} |
+1
-2
@@ -1,3 +0,2 @@ | ||
| import { EmbeddingModel, ExecutionProvider, FlagEmbedding } from "./fastembed"; | ||
| export { EmbeddingModel, ExecutionProvider, FlagEmbedding }; | ||
| export { EmbeddingModel, ExecutionProvider, FlagEmbedding, } from "./fastembed.js"; | ||
| //# sourceMappingURL=index.js.map |
@@ -1,1 +0,1 @@ | ||
| {"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,EAAE,cAAc,EAAE,iBAAiB,EAAE,aAAa,EAAE,MAAM,aAAa,CAAC;AAC/E,OAAO,EAAE,cAAc,EAAE,iBAAiB,EAAE,aAAa,EAAE,CAAC"} | ||
| {"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/index.ts"],"names":[],"mappings":"AAAA,OAAO,EACL,cAAc,EACd,iBAAiB,EACjB,aAAa,GACd,MAAM,gBAAgB,CAAC"} |
+2
-1
| { | ||
| "name": "fastembed", | ||
| "version": "1.14.2", | ||
| "version": "1.14.3", | ||
| "description": "NodeJS implementation of @Qdrant/fastembed", | ||
@@ -33,2 +33,3 @@ "keywords": [ | ||
| ], | ||
| "sideEffects": false, | ||
| "scripts": { | ||
@@ -35,0 +36,0 @@ "prepublishOnly": "npm run tsc", |
New alerts
Filesystem access
Supply chain riskAccesses the file system, and could potentially read sensitive data.
Fixed alerts
AI-detected potential code anomaly
Supply chain riskAI has identified unusual behaviors that may pose a security risk.
Improved metrics
- Number of low supply chain risk alerts
4
-20%Worsened metrics
- Total package byte prevSize
64706
-8.36%- Lines of code
772
-8.2%No dependency changes