mirror of
https://github.com/lancedb/lancedb.git
synced 2026-01-03 18:32:55 +00:00
feat(nodejs): huggingface compatible transformers (#1462)
This commit is contained in:
Cory Grinstead
GitHub
759
nodejs/examples/package-lock.json
generated
759
nodejs/examples/package-lock.json
generated
@@ -9,7 +9,8 @@
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@@ -17,7 +18,7 @@
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@@ -29,17 +30,16 @@
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"safe-buffer": "~5.2.0"
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"engines": {
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"node": ">=0.10.0"
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"pump": "^3.0.0",
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"bare-path": "^2.1.0"
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"dependencies": {
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"b4a": "^1.6.4",
|
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"fast-fifo": "^1.2.0",
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||||
"streamx": "^2.15.0"
|
||||
}
|
||||
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"node_modules/text-decoder": {
|
||||
"version": "1.1.1",
|
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"resolved": "https://registry.npmjs.org/text-decoder/-/text-decoder-1.1.1.tgz",
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"integrity": "sha512-8zll7REEv4GDD3x4/0pW+ppIxSNs7H1J10IKFZsuOMscumCdM2a+toDGLPA3T+1+fLBql4zbt5z83GEQGGV5VA==",
|
||||
"dependencies": {
|
||||
"b4a": "^1.6.4"
|
||||
}
|
||||
},
|
||||
"node_modules/tunnel-agent": {
|
||||
"version": "0.6.0",
|
||||
"resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz",
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"integrity": "sha512-McnNiV1l8RYeY8tBgEpuodCC1mLUdbSN+CYBL7kJsJNInOP8UjDDEwdk6Mw60vdLLrr5NHKZhMAOSrR2NZuQ+w==",
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||||
"dependencies": {
|
||||
"safe-buffer": "^5.0.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": "*"
|
||||
}
|
||||
},
|
||||
"node_modules/typescript": {
|
||||
"version": "5.5.2",
|
||||
"resolved": "https://registry.npmjs.org/typescript/-/typescript-5.5.2.tgz",
|
||||
@@ -74,6 +808,21 @@
|
||||
"engines": {
|
||||
"node": ">=14.17"
|
||||
}
|
||||
},
|
||||
"node_modules/undici-types": {
|
||||
"version": "5.26.5",
|
||||
"resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
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||||
"integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="
|
||||
},
|
||||
"node_modules/util-deprecate": {
|
||||
"version": "1.0.2",
|
||||
"resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz",
|
||||
"integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw=="
|
||||
},
|
||||
"node_modules/wrappy": {
|
||||
"version": "1.0.2",
|
||||
"resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
|
||||
"integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -10,7 +10,8 @@
|
||||
"author": "Lance Devs",
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"@lancedb/lancedb": "file:../"
|
||||
"@lancedb/lancedb": "file:../",
|
||||
"@xenova/transformers": "^2.17.2"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"typescript": "^5.0.0"
|
||||
|
||||
50
nodejs/examples/sentence-transformers.js
Normal file
50
nodejs/examples/sentence-transformers.js
Normal file
@@ -0,0 +1,50 @@
|
||||
import * as lancedb from "@lancedb/lancedb";
|
||||
|
||||
import { LanceSchema, getRegistry } from "@lancedb/lancedb/embedding";
|
||||
import { Utf8 } from "apache-arrow";
|
||||
|
||||
const db = await lancedb.connect("/tmp/db");
|
||||
const func = await getRegistry().get("huggingface").create();
|
||||
|
||||
const facts = [
|
||||
"Albert Einstein was a theoretical physicist.",
|
||||
"The capital of France is Paris.",
|
||||
"The Great Wall of China is one of the Seven Wonders of the World.",
|
||||
"Python is a popular programming language.",
|
||||
"Mount Everest is the highest mountain in the world.",
|
||||
"Leonardo da Vinci painted the Mona Lisa.",
|
||||
"Shakespeare wrote Hamlet.",
|
||||
"The human body has 206 bones.",
|
||||
"The speed of light is approximately 299,792 kilometers per second.",
|
||||
"Water boils at 100 degrees Celsius.",
|
||||
"The Earth orbits the Sun.",
|
||||
"The Pyramids of Giza are located in Egypt.",
|
||||
"Coffee is one of the most popular beverages in the world.",
|
||||
"Tokyo is the capital city of Japan.",
|
||||
"Photosynthesis is the process by which plants make their food.",
|
||||
"The Pacific Ocean is the largest ocean on Earth.",
|
||||
"Mozart was a prolific composer of classical music.",
|
||||
"The Internet is a global network of computers.",
|
||||
"Basketball is a sport played with a ball and a hoop.",
|
||||
"The first computer virus was created in 1983.",
|
||||
"Artificial neural networks are inspired by the human brain.",
|
||||
"Deep learning is a subset of machine learning.",
|
||||
"IBM's Watson won Jeopardy! in 2011.",
|
||||
"The first computer programmer was Ada Lovelace.",
|
||||
"The first chatbot was ELIZA, created in the 1960s.",
|
||||
].map((text) => ({ text }));
|
||||
|
||||
const factsSchema = LanceSchema({
|
||||
text: func.sourceField(new Utf8()),
|
||||
vector: func.vectorField(),
|
||||
});
|
||||
|
||||
const tbl = await db.createTable("facts", facts, {
|
||||
mode: "overwrite",
|
||||
schema: factsSchema,
|
||||
});
|
||||
|
||||
const query = "How many bones are in the human body?";
|
||||
const actual = await tbl.search(query).limit(1).toArray();
|
||||
|
||||
console.log("Answer: ", actual[0]["text"]);
|
||||
@@ -578,7 +578,7 @@ async function applyEmbeddingsFromMetadata(
|
||||
schema: Schema,
|
||||
): Promise<ArrowTable> {
|
||||
const registry = getRegistry();
|
||||
const functions = registry.parseFunctions(schema.metadata);
|
||||
const functions = await registry.parseFunctions(schema.metadata);
|
||||
|
||||
const columns = Object.fromEntries(
|
||||
table.schema.fields.map((field) => [
|
||||
|
||||
@@ -240,6 +240,7 @@ export class LocalConnection extends Connection {
|
||||
): Promise<Table> {
|
||||
if (typeof nameOrOptions !== "string" && "name" in nameOrOptions) {
|
||||
const { name, data, ...options } = nameOrOptions;
|
||||
|
||||
return this.createTable(name, data, options);
|
||||
}
|
||||
if (data === undefined) {
|
||||
|
||||
@@ -41,6 +41,7 @@ export interface EmbeddingFunctionConstructor<
|
||||
> {
|
||||
new (modelOptions?: T["TOptions"]): T;
|
||||
}
|
||||
|
||||
/**
|
||||
* An embedding function that automatically creates vector representation for a given column.
|
||||
*/
|
||||
@@ -82,6 +83,8 @@ export abstract class EmbeddingFunction<
|
||||
*/
|
||||
abstract toJSON(): Partial<M>;
|
||||
|
||||
async init?(): Promise<void>;
|
||||
|
||||
/**
|
||||
* sourceField is used in combination with `LanceSchema` to provide a declarative data model
|
||||
*
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
import { DataType, Field, Schema } from "../arrow";
|
||||
import { Field, Schema } from "../arrow";
|
||||
import { isDataType } from "../arrow";
|
||||
import { sanitizeType } from "../sanitize";
|
||||
import { EmbeddingFunction } from "./embedding_function";
|
||||
@@ -22,6 +22,7 @@ export { EmbeddingFunction } from "./embedding_function";
|
||||
|
||||
// We need to explicitly export '*' so that the `register` decorator actually registers the class.
|
||||
export * from "./openai";
|
||||
export * from "./transformers";
|
||||
export * from "./registry";
|
||||
|
||||
/**
|
||||
|
||||
@@ -18,9 +18,14 @@ import {
|
||||
} from "./embedding_function";
|
||||
import "reflect-metadata";
|
||||
import { OpenAIEmbeddingFunction } from "./openai";
|
||||
import { TransformersEmbeddingFunction } from "./transformers";
|
||||
|
||||
type CreateReturnType<T> = T extends { init: () => Promise<void> }
|
||||
? Promise<T>
|
||||
: T;
|
||||
|
||||
interface EmbeddingFunctionCreate<T extends EmbeddingFunction> {
|
||||
create(options?: T["TOptions"]): T;
|
||||
create(options?: T["TOptions"]): CreateReturnType<T>;
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -61,38 +66,43 @@ export class EmbeddingFunctionRegistry {
|
||||
};
|
||||
}
|
||||
|
||||
get(name: "openai"): EmbeddingFunctionCreate<OpenAIEmbeddingFunction>;
|
||||
get(
|
||||
name: "huggingface",
|
||||
): EmbeddingFunctionCreate<TransformersEmbeddingFunction>;
|
||||
get<T extends EmbeddingFunction<unknown>>(
|
||||
name: string,
|
||||
): EmbeddingFunctionCreate<T> | undefined;
|
||||
/**
|
||||
* Fetch an embedding function by name
|
||||
* @param name The name of the function
|
||||
*/
|
||||
get<T extends EmbeddingFunction<unknown>, Name extends string = "">(
|
||||
name: Name extends "openai" ? "openai" : string,
|
||||
//This makes it so that you can use string constants as "types", or use an explicitly supplied type
|
||||
// ex:
|
||||
// `registry.get("openai") -> EmbeddingFunctionCreate<OpenAIEmbeddingFunction>`
|
||||
// `registry.get<MyCustomEmbeddingFunction>("my_func") -> EmbeddingFunctionCreate<MyCustomEmbeddingFunction> | undefined`
|
||||
//
|
||||
// the reason this is important is that we always know our built in functions are defined so the user isnt forced to do a non null/undefined
|
||||
// ```ts
|
||||
// const openai: OpenAIEmbeddingFunction = registry.get("openai").create()
|
||||
// ```
|
||||
): Name extends "openai"
|
||||
? EmbeddingFunctionCreate<OpenAIEmbeddingFunction>
|
||||
: EmbeddingFunctionCreate<T> | undefined {
|
||||
type Output = Name extends "openai"
|
||||
? EmbeddingFunctionCreate<OpenAIEmbeddingFunction>
|
||||
: EmbeddingFunctionCreate<T> | undefined;
|
||||
|
||||
get(name: string) {
|
||||
const factory = this.#functions.get(name);
|
||||
if (!factory) {
|
||||
return undefined as Output;
|
||||
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
||||
return undefined as any;
|
||||
}
|
||||
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
||||
let create: any;
|
||||
if (factory.prototype.init) {
|
||||
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
||||
create = async function (options?: any) {
|
||||
const instance = new factory(options);
|
||||
await instance.init!();
|
||||
return instance;
|
||||
};
|
||||
} else {
|
||||
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
||||
create = function (options?: any) {
|
||||
const instance = new factory(options);
|
||||
return instance;
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
create: function (options?: T["TOptions"]) {
|
||||
return new factory(options);
|
||||
},
|
||||
} as Output;
|
||||
create,
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -105,10 +115,10 @@ export class EmbeddingFunctionRegistry {
|
||||
/**
|
||||
* @ignore
|
||||
*/
|
||||
parseFunctions(
|
||||
async parseFunctions(
|
||||
this: EmbeddingFunctionRegistry,
|
||||
metadata: Map<string, string>,
|
||||
): Map<string, EmbeddingFunctionConfig> {
|
||||
): Promise<Map<string, EmbeddingFunctionConfig>> {
|
||||
if (!metadata.has("embedding_functions")) {
|
||||
return new Map();
|
||||
} else {
|
||||
@@ -118,25 +128,30 @@ export class EmbeddingFunctionRegistry {
|
||||
vectorColumn: string;
|
||||
model: EmbeddingFunction["TOptions"];
|
||||
};
|
||||
|
||||
const functions = <FunctionConfig[]>(
|
||||
JSON.parse(metadata.get("embedding_functions")!)
|
||||
);
|
||||
return new Map(
|
||||
functions.map((f) => {
|
||||
|
||||
const items: [string, EmbeddingFunctionConfig][] = await Promise.all(
|
||||
functions.map(async (f) => {
|
||||
const fn = this.get(f.name);
|
||||
if (!fn) {
|
||||
throw new Error(`Function "${f.name}" not found in registry`);
|
||||
}
|
||||
const func = await this.get(f.name)!.create(f.model);
|
||||
return [
|
||||
f.name,
|
||||
{
|
||||
sourceColumn: f.sourceColumn,
|
||||
vectorColumn: f.vectorColumn,
|
||||
function: this.get(f.name)!.create(f.model),
|
||||
function: func,
|
||||
},
|
||||
];
|
||||
}),
|
||||
);
|
||||
|
||||
return new Map(items);
|
||||
}
|
||||
}
|
||||
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
||||
|
||||
193
nodejs/lancedb/embedding/transformers.ts
Normal file
193
nodejs/lancedb/embedding/transformers.ts
Normal file
@@ -0,0 +1,193 @@
|
||||
// Copyright 2023 Lance Developers.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
import { Float, Float32 } from "../arrow";
|
||||
import { EmbeddingFunction } from "./embedding_function";
|
||||
import { register } from "./registry";
|
||||
|
||||
export type XenovaTransformerOptions = {
|
||||
/** The wasm compatible model to use */
|
||||
model: string;
|
||||
/**
|
||||
* The wasm compatible tokenizer to use
|
||||
* If not provided, it will use the default tokenizer for the model
|
||||
*/
|
||||
tokenizer?: string;
|
||||
/**
|
||||
* The number of dimensions of the embeddings
|
||||
*
|
||||
* We will attempt to infer this from the model config if not provided.
|
||||
* Since there isn't a standard way to get this information from the model,
|
||||
* you may need to manually specify this if using a model that doesn't have a 'hidden_size' in the config.
|
||||
* */
|
||||
ndims?: number;
|
||||
/** Options for the tokenizer */
|
||||
tokenizerOptions?: {
|
||||
textPair?: string | string[];
|
||||
padding?: boolean | "max_length";
|
||||
addSpecialTokens?: boolean;
|
||||
truncation?: boolean;
|
||||
maxLength?: number;
|
||||
};
|
||||
};
|
||||
|
||||
@register("huggingface")
|
||||
export class TransformersEmbeddingFunction extends EmbeddingFunction<
|
||||
string,
|
||||
Partial<XenovaTransformerOptions>
|
||||
> {
|
||||
#model?: import("@xenova/transformers").PreTrainedModel;
|
||||
#tokenizer?: import("@xenova/transformers").PreTrainedTokenizer;
|
||||
#modelName: XenovaTransformerOptions["model"];
|
||||
#initialized = false;
|
||||
#tokenizerOptions: XenovaTransformerOptions["tokenizerOptions"];
|
||||
#ndims?: number;
|
||||
|
||||
constructor(
|
||||
options: Partial<XenovaTransformerOptions> = {
|
||||
model: "Xenova/all-MiniLM-L6-v2",
|
||||
},
|
||||
) {
|
||||
super();
|
||||
|
||||
const modelName = options?.model ?? "Xenova/all-MiniLM-L6-v2";
|
||||
this.#tokenizerOptions = {
|
||||
padding: true,
|
||||
...options.tokenizerOptions,
|
||||
};
|
||||
|
||||
this.#ndims = options.ndims;
|
||||
this.#modelName = modelName;
|
||||
}
|
||||
toJSON() {
|
||||
// biome-ignore lint/suspicious/noExplicitAny: <explanation>
|
||||
const obj: Record<string, any> = {
|
||||
model: this.#modelName,
|
||||
};
|
||||
if (this.#ndims) {
|
||||
obj["ndims"] = this.#ndims;
|
||||
}
|
||||
if (this.#tokenizerOptions) {
|
||||
obj["tokenizerOptions"] = this.#tokenizerOptions;
|
||||
}
|
||||
if (this.#tokenizer) {
|
||||
obj["tokenizer"] = this.#tokenizer.name;
|
||||
}
|
||||
return obj;
|
||||
}
|
||||
|
||||
async init() {
|
||||
let transformers;
|
||||
try {
|
||||
// SAFETY:
|
||||
// since typescript transpiles `import` to `require`, we need to do this in an unsafe way
|
||||
// We can't use `require` because `@xenova/transformers` is an ESM module
|
||||
// and we can't use `import` directly because typescript will transpile it to `require`.
|
||||
// and we want to remain compatible with both ESM and CJS modules
|
||||
// so we use `eval` to bypass typescript for this specific import.
|
||||
transformers = await eval('import("@xenova/transformers")');
|
||||
} catch (e) {
|
||||
throw new Error(`error loading @xenova/transformers\nReason: ${e}`);
|
||||
}
|
||||
|
||||
try {
|
||||
this.#model = await transformers.AutoModel.from_pretrained(
|
||||
this.#modelName,
|
||||
);
|
||||
} catch (e) {
|
||||
throw new Error(
|
||||
`error loading model ${this.#modelName}. Make sure you are using a wasm compatible model.\nReason: ${e}`,
|
||||
);
|
||||
}
|
||||
try {
|
||||
this.#tokenizer = await transformers.AutoTokenizer.from_pretrained(
|
||||
this.#modelName,
|
||||
);
|
||||
} catch (e) {
|
||||
throw new Error(
|
||||
`error loading tokenizer for ${this.#modelName}. Make sure you are using a wasm compatible model:\nReason: ${e}`,
|
||||
);
|
||||
}
|
||||
this.#initialized = true;
|
||||
}
|
||||
|
||||
ndims(): number {
|
||||
if (this.#ndims) {
|
||||
return this.#ndims;
|
||||
} else {
|
||||
const config = this.#model!.config;
|
||||
|
||||
const ndims = config["hidden_size"];
|
||||
if (!ndims) {
|
||||
throw new Error(
|
||||
"hidden_size not found in model config, you may need to manually specify the embedding dimensions. ",
|
||||
);
|
||||
}
|
||||
return ndims;
|
||||
}
|
||||
}
|
||||
|
||||
embeddingDataType(): Float {
|
||||
return new Float32();
|
||||
}
|
||||
|
||||
async computeSourceEmbeddings(data: string[]): Promise<number[][]> {
|
||||
// this should only happen if the user is trying to use the function directly.
|
||||
// Anything going through the registry should already be initialized.
|
||||
if (!this.#initialized) {
|
||||
return Promise.reject(
|
||||
new Error(
|
||||
"something went wrong: embedding function not initialized. Please call init()",
|
||||
),
|
||||
);
|
||||
}
|
||||
const tokenizer = this.#tokenizer!;
|
||||
const model = this.#model!;
|
||||
|
||||
const inputs = await tokenizer(data, this.#tokenizerOptions);
|
||||
let tokens = await model.forward(inputs);
|
||||
tokens = tokens[Object.keys(tokens)[0]];
|
||||
|
||||
const [nItems, nTokens] = tokens.dims;
|
||||
|
||||
tokens = tensorDiv(tokens.sum(1), nTokens);
|
||||
|
||||
// TODO: support other data types
|
||||
const tokenData = tokens.data;
|
||||
const stride = this.ndims();
|
||||
|
||||
const embeddings = [];
|
||||
for (let i = 0; i < nItems; i++) {
|
||||
const start = i * stride;
|
||||
const end = start + stride;
|
||||
const slice = tokenData.slice(start, end);
|
||||
embeddings.push(Array.from(slice) as number[]); // TODO: Avoid copy here
|
||||
}
|
||||
return embeddings;
|
||||
}
|
||||
|
||||
async computeQueryEmbeddings(data: string): Promise<number[]> {
|
||||
return (await this.computeSourceEmbeddings([data]))[0];
|
||||
}
|
||||
}
|
||||
|
||||
const tensorDiv = (
|
||||
src: import("@xenova/transformers").Tensor,
|
||||
divBy: number,
|
||||
) => {
|
||||
for (let i = 0; i < src.data.length; ++i) {
|
||||
src.data[i] /= divBy;
|
||||
}
|
||||
return src;
|
||||
};
|
||||
@@ -493,7 +493,7 @@ export class LocalTable extends Table {
|
||||
const mode = options?.mode ?? "append";
|
||||
const schema = await this.schema();
|
||||
const registry = getRegistry();
|
||||
const functions = registry.parseFunctions(schema.metadata);
|
||||
const functions = await registry.parseFunctions(schema.metadata);
|
||||
|
||||
const buffer = await fromDataToBuffer(
|
||||
data,
|
||||
|
||||
762
nodejs/package-lock.json
generated
762
nodejs/package-lock.json
generated
File diff suppressed because it is too large
Load Diff
@@ -32,12 +32,13 @@
|
||||
},
|
||||
"license": "Apache 2.0",
|
||||
"devDependencies": {
|
||||
"@aws-sdk/client-dynamodb": "^3.33.0",
|
||||
"@aws-sdk/client-kms": "^3.33.0",
|
||||
"@aws-sdk/client-s3": "^3.33.0",
|
||||
"@aws-sdk/client-dynamodb": "^3.33.0",
|
||||
"@biomejs/biome": "^1.7.3",
|
||||
"@jest/globals": "^29.7.0",
|
||||
"@napi-rs/cli": "^2.18.3",
|
||||
"@types/axios": "^0.14.0",
|
||||
"@types/jest": "^29.1.2",
|
||||
"@types/tmp": "^0.2.6",
|
||||
"apache-arrow-13": "npm:apache-arrow@13.0.0",
|
||||
@@ -53,8 +54,7 @@
|
||||
"typedoc": "^0.26.4",
|
||||
"typedoc-plugin-markdown": "^4.2.1",
|
||||
"typescript": "^5.3.3",
|
||||
"typescript-eslint": "^7.1.0",
|
||||
"@types/axios": "^0.14.0"
|
||||
"typescript-eslint": "^7.1.0"
|
||||
},
|
||||
"ava": {
|
||||
"timeout": "3m"
|
||||
@@ -85,6 +85,7 @@
|
||||
"reflect-metadata": "^0.2.2"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@xenova/transformers": ">=2.17 < 3",
|
||||
"openai": "^4.29.2"
|
||||
},
|
||||
"peerDependencies": {
|
||||
|
||||
Reference in New Issue
Block a user