Fix eslint config to not exclude js/d.ts files (langchain-ai#150)

langchain/.eslintrc.cjs

@@ -12,7 +12,7 @@ module.exports = {
     sourceType: "module",
   },
   plugins: ["@typescript-eslint"],
-  ignorePatterns: ["dist", "docs", "node_modules", "*.d.ts", "*.cjs", "*.js"],
+  ignorePatterns: [".eslintrc.cjs", "node_modules"],
   rules: {
     "@typescript-eslint/explicit-module-boundary-types": 0,
     "@typescript-eslint/no-empty-function": 0,

langchain/package.json

@@ -40,7 +40,7 @@
   },
   "scripts": {
     "build": "yarn clean && tsc --declaration --outDir dist/ && node create-entrypoints.js",
-    "lint": "eslint .",
+    "lint": "eslint src",
     "doc": "typedoc",
     "lint:fix": "yarn lint --fix",
     "precommit": "tsc --noEmit && lint-staged",

langchain/src/types/huggingface.d.ts

@@ -3,68 +3,80 @@ declare module "huggingface" {
   // exports.types key in package.json
   declare class HuggingFace {
     private readonly apiKey;
+
     private readonly defaultOptions;
+
     constructor(apiKey: string, defaultOptions?: Options);
+
     /**
      * Tries to fill in a hole with a missing word (token to be precise). That’s the base task for BERT models.
      */
     fillMask(args: FillMaskArgs, options?: Options): Promise<FillMaskReturn>;
+
     /**
      * This task is well known to summarize longer text into shorter text. Be careful, some models have a maximum length of input. That means that the summary cannot handle full books for instance. Be careful when choosing your model.
      */
     summarization(
       args: SummarizationArgs,
       options?: Options
     ): Promise<SummarizationReturn>;
+
     /**
      * Want to have a nice know-it-all bot that can answer any question?. Recommended model: deepset/roberta-base-squad2
      */
     questionAnswer(
       args: QuestionAnswerArgs,
       options?: Options
     ): Promise<QuestionAnswerReturn>;
+
     /**
      * Don’t know SQL? Don’t want to dive into a large spreadsheet? Ask questions in plain english! Recommended model: google/tapas-base-finetuned-wtq.
      */
     tableQuestionAnswer(
       args: TableQuestionAnswerArgs,
       options?: Options
     ): Promise<TableQuestionAnswerReturn>;
+
     /**
      * Usually used for sentiment-analysis this will output the likelihood of classes of an input. Recommended model: distilbert-base-uncased-finetuned-sst-2-english
      */
     textClassification(
       args: TextClassificationArgs,
       options?: Options
     ): Promise<TextClassificationReturn>;
+
     /**
      * Use to continue text from a prompt. This is a very generic task. Recommended model: gpt2 (it’s a simple model, but fun to play with).
      */
     textGeneration(
       args: TextGenerationArgs,
       options?: Options
     ): Promise<TextGenerationReturn>;
+
     /**
      * Usually used for sentence parsing, either grammatical, or Named Entity Recognition (NER) to understand keywords contained within text. Recommended model: dbmdz/bert-large-cased-finetuned-conll03-english
      */
     tokenClassification(
       args: TokenClassificationArgs,
       options?: Options
     ): Promise<TokenClassificationReturn>;
+
     /**
      * This task is well known to translate text from one language to another. Recommended model: Helsinki-NLP/opus-mt-ru-en.
      */
     translation(
       args: TranslationArgs,
       options?: Options
     ): Promise<TranslationReturn>;
+
     /**
      * This task is super useful to try out classification with zero code, you simply pass a sentence/paragraph and the possible labels for that sentence, and you get a result. Recommended model: facebook/bart-large-mnli.
      */
     zeroShotClassification(
       args: ZeroShotClassificationArgs,
       options?: Options
     ): Promise<ZeroShotClassificationReturn>;
+
     /**
      * This task corresponds to any chatbot like structure. Models tend to have shorter max_length, so please check with caution when using a given model if you need long range dependency or not. Recommended model: microsoft/DialoGPT-large.
      *
@@ -73,13 +85,15 @@ declare module "huggingface" {
       args: ConversationalArgs,
       options?: Options
     ): Promise<ConversationalReturn>;
+
     /**
      * This task reads some text and outputs raw float values, that are usually consumed as part of a semantic database/semantic search.
      */
     featureExtraction(
       args: FeatureExtractionArgs,
       options?: Options
     ): Promise<FeatureExtractionReturn>;
+
     /**
      * This task reads some audio input and outputs the said words within the audio files.
      * Recommended model (english language): facebook/wav2vec2-large-960h-lv60-self
@@ -88,6 +102,7 @@ declare module "huggingface" {
       args: AutomaticSpeechRecognitionArgs,
       options?: Options
     ): Promise<AutomaticSpeechRecognitionReturn>;
+
     /**
      * This task reads some audio input and outputs the likelihood of classes.
      * Recommended model:  superb/hubert-large-superb-er
@@ -96,6 +111,7 @@ declare module "huggingface" {
       args: AudioClassificationArgs,
       options?: Options
     ): Promise<AudioClassificationReturn>;
+
     /**
      * This task reads some image input and outputs the likelihood of classes.
      * Recommended model: google/vit-base-patch16-224
@@ -104,6 +120,7 @@ declare module "huggingface" {
       args: ImageClassificationArgs,
       options?: Options
     ): Promise<ImageClassificationReturn>;
+
     /**
      * This task reads some image input and outputs the likelihood of classes & bounding boxes of detected objects.
      * Recommended model: facebook/detr-resnet-50
@@ -112,6 +129,7 @@ declare module "huggingface" {
       args: ObjectDetectionArgs,
       options?: Options
     ): Promise<ObjectDetectionReturn>;
+
     /**
      * This task reads some image input and outputs the likelihood of classes & bounding boxes of detected objects.
      * Recommended model: facebook/detr-resnet-50-panoptic
@@ -120,14 +138,16 @@ declare module "huggingface" {
       args: ImageSegmentationArgs,
       options?: Options
     ): Promise<ImageSegmentationReturn>;
+
     request(
       args: Args & {
-        data?: any;
+        data?: unknown;
       },
       options?: Options & {
         binary?: boolean;
       }
-    ): Promise<any>;
+    ): Promise<unknown>;
+
     private static toArray;
   }
 

langchain/src/util/axios-fetch-adapter.d.ts

@@ -1,3 +1,4 @@
+// eslint-disable-next-line import/no-extraneous-dependencies
 import { AxiosRequestConfig, AxiosPromise } from "axios";
 
 export default function fetchAdapter(config: AxiosRequestConfig): AxiosPromise;

langchain/src/util/axios-fetch-adapter.js

@@ -1,3 +1,4 @@
+/* eslint-disable no-param-reassign */
 /* eslint-disable import/no-extraneous-dependencies */
 
 /**
@@ -30,7 +31,7 @@ export default async function fetchAdapter(config) {
         setTimeout(() => {
           const message = config.timeoutErrorMessage
             ? config.timeoutErrorMessage
-            : "timeout of " + config.timeout + "ms exceeded";
+            : `timeout of ${config.timeout}ms exceeded`;
           res(createError(message, config, "ECONNABORTED", request));
         }, config.timeout);
       })
@@ -42,6 +43,7 @@ export default async function fetchAdapter(config) {
     if (data instanceof Error) {
       reject(data);
     } else {
+      // eslint-disable-next-line no-unused-expressions
       Object.prototype.toString.call(config.settle) === "[object Function]"
         ? config.settle(resolve, reject, data)
         : settle(resolve, reject, data);
@@ -66,7 +68,7 @@ async function getResponse(request, config) {
     status: stageOne.status,
     statusText: stageOne.statusText,
     headers: new Headers(stageOne.headers), // Make a copy of headers
-    config: config,
+    config,
     request,
   };
 
@@ -105,12 +107,12 @@ function createRequest(config) {
     const password = config.auth.password
       ? decodeURI(encodeURIComponent(config.auth.password))
       : "";
-    headers.set("Authorization", `Basic ${btoa(username + ":" + password)}`);
+    headers.set("Authorization", `Basic ${btoa(`${username}:${password}`)}`);
   }
 
   const method = config.method.toUpperCase();
   const options = {
-    headers: headers,
+    headers,
     method,
   };
   if (method !== "GET" && method !== "HEAD") {
@@ -178,7 +180,7 @@ function createError(message, config, code, request, response) {
     );
   }
 
-  var error = new Error(message);
+  const error = new Error(message);
   return enhanceError(error, config, code, request, response);
 }