feat(*): improve error handling in contextual vectors

and update typescript defn

Co-authored-by: Rachna <rachna@graype.in>

src/doc-v2.js

@@ -459,10 +459,14 @@ var doc = function ( docData, addons ) {
   // eslint-disable-next-line complexity
   contextualVectors = function ( { lemma = true, specificWordVectors = [], similarWordVectors = false, wordVectorsLimit = 0 } = {} ) {
     // Error handling!
+    if ( docData.wordVectors === null )
+      throw Error( 'wink-nlp: word vectors are not loaded: load them winkNLP\'s instantiation time.' );
     if ( !Array.isArray( specificWordVectors ) )
       throw Error( `wink-nlp: expecting a valid Javascript array for similarWordVectos, instead found "${typeof specificWordVectors}".`);
     if ( !Number.isInteger( wordVectorsLimit ) || wordVectorsLimit >= docData.wordVectors.size )
       throw Error( 'wink-nlp: invalid value or type encountered for wordVectorsLimit.' );
+    if ( lemma && !docData.currPipe.pos )
+      throw Error( 'wink-nlp: Can\'t create lemma vectors without pos: add a "pos" to NLP pipe.' );
     // Initialize contextual vectors.
     const cv = Object.create( null );
     // Following properties are constants, therefore can be directly copied.

types/index.d.ts

@@ -271,6 +271,13 @@ declare module 'wink-nlp' {
     out<T, U>(itsf: ItsFunction<T>, asf: AsFunction<T, U>): U | T[] | string[];
   }
 
+  export interface Include {
+    lemma?: boolean;
+    specificWordVectors?: string[];
+    similarWordVectors?: boolean;
+    wordVectorsLimit?: number;
+  }
+
   export interface Document {
     entities(): Entities;
     customEntities(): CustomEntities;
@@ -282,7 +289,7 @@ declare module 'wink-nlp' {
     tokens(): Tokens;
     printTokens(): void;
     pipeConfig(): string[];
-    contextualVectors(lemma: boolean, specifcWordVectors: string[], similarWordVectors: boolean, wordVectorsLimit: number): string;
+    contextualVectors(include: Include): string;
   }
 
   export interface CerExample {
@@ -317,6 +324,7 @@ declare module 'wink-nlp' {
     readDoc(text: string): Document;
     // returns number of learned entities
     learnCustomEntities(examples: CustomEntityExample[], config?: CerConfig): number;
+    vectorOf(word: string): number[];
     its: ItsHelpers;
     as: AsHelpers;
   }