community[minor]: FirestoreVectorStore support for distance method

libs/langchain-community/src/vectorstores/firestore.ts

@@ -1,4 +1,5 @@
 import {
+ MaxMarginalRelevanceSearchOptions,
  VectorStore,
 } from '@langchain/core/vectorstores';
 import type { EmbeddingsInterface } from '@langchain/core/embeddings';
@@ -26,12 +27,15 @@ import {VectorQuerySnapshot} from '@google-cloud/firestore';
  * @param collection Firestore collection to store the vectors.
  * @param textKey Corresponds to the plaintext of 'pageContent'.
  * @param embeddingKey Key to store the embedding under.
+ * @param metadataKey Key to store the metadata under.
+ * @param vectorType Type of vector comparison
  */
 export interface FirestoreVectorSearchLibArgs extends AsyncCallerParams {
  readonly collection: CollectionReference;
  readonly textKey?: string;
  readonly embeddingKey?: string;
  readonly metadataKey?: string;
+ readonly vectorType?: SupportedVectorTypes;
 }
 
 /**
@@ -41,6 +45,8 @@ export interface FirestoreVectorSearchLibArgs extends AsyncCallerParams {
  */
 type FirestoreFilter = Filter
 
+type SupportedVectorTypes = 'EUCLIDEAN' | 'COSINE' | 'DOT_PRODUCT'
+
 /**
  * Class that is a wrapper around Firestore.
  */
@@ -55,7 +61,9 @@ export class FirestoreVectorSearch extends VectorStore {
 
  private readonly metadataKey: string;
 
- // private caller: AsyncCaller;
+ private readonly vectorType: SupportedVectorTypes;
+
+ private caller: AsyncCaller;
 
  _vectorstoreType(): string {
  return 'firestore';
@@ -70,7 +78,8 @@ export class FirestoreVectorSearch extends VectorStore {
  this.textKey = args.textKey ?? 'text';
  this.embeddingKey = args.embeddingKey ?? 'embedding';
  this.metadataKey = args.metadataKey ?? 'metadata';
- // this.caller = new AsyncCaller(args);
+ this.vectorType = args.vectorType ?? 'COSINE'
+ this.caller = new AsyncCaller(args);
  }
 
  /**
@@ -87,7 +96,7 @@ export class FirestoreVectorSearch extends VectorStore {
  ) {
  const docs = vectors.map((embedding, idx) => ({
  [this.textKey]: documents[idx].pageContent,
- [this.embeddingKey]: embedding,
+ [this.embeddingKey]: FieldValue.vector(embedding),
  [this.metadataKey]: documents[idx].metadata,
  }));
  if (options?.ids === undefined) {
@@ -155,8 +164,9 @@ export class FirestoreVectorSearch extends VectorStore {
  // apply pre-filter query where available
  if (filter) qry = qry.where(filter)
  // apply vector query
+ const distanceMeasure = this.vectorType
  const vectorQuery = qry
- .findNearest(this.embeddingKey, FieldValue.vector(query), {limit:k, distanceMeasure: 'EUCLIDEAN'})
+ .findNearest(this.embeddingKey, FieldValue.vector(query), {limit:k, distanceMeasure})
  // make the query
  const vectorQuerySnapshot:VectorQuerySnapshot = await vectorQuery.get();
  // convert the results