diff --git a/libs/langchain-community/src/vectorstores/firestore.ts b/libs/langchain-community/src/vectorstores/firestore.ts
new file mode 100644
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+++ b/libs/langchain-community/src/vectorstores/firestore.ts
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+import { VectorStore } from "@langchain/core/vectorstores";
+import type { EmbeddingsInterface } from "@langchain/core/embeddings";
+import { Document, DocumentInterface } from "@langchain/core/documents";
+import {
+  AsyncCaller,
+  AsyncCallerParams,
+} from "@langchain/core/utils/async_caller";
+import {
+  CollectionReference,
+  DocumentData,
+  FieldValue,
+  Query,
+  QueryDocumentSnapshot,
+  Filter,
+} from "firebase-admin/firestore";
+import { VectorQuerySnapshot } from "@google-cloud/firestore";
+
+// TODO: better to use common method than our own one
+// import { cosineSimilarity } from "@langchain/core/dist/utils/math";
+
+/**
+ * Type that defines the arguments required to initialize the
+ * FirestoreVectorSearch class. It includes the Firestore collection,
+ * text key, embedding key.
+ *
+ * @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;
+}
+
+/**
+ * Type that defines the filter used in the
+ * similaritySearchVectorWithScore method.
+ * It applies a pre-filter query
+ */
+type FirestoreFilter = Filter;
+
+type SupportedVectorTypes = "EUCLIDEAN" | "COSINE" | "DOT_PRODUCT";
+
+/**
+ * Class that is a wrapper around Firestore.
+ */
+export class FirestoreVectorSearch extends VectorStore {
+  declare FilterType: FirestoreFilter;
+
+  private readonly collection: CollectionReference<DocumentData>;
+
+  private readonly textKey: string;
+
+  private readonly embeddingKey: string;
+
+  private readonly metadataKey: string;
+
+  private readonly vectorType: SupportedVectorTypes;
+
+  private caller: AsyncCaller;
+
+  _vectorstoreType(): string {
+    return "firestore";
+  }
+
+  constructor(
+    embeddings: EmbeddingsInterface,
+    args: FirestoreVectorSearchLibArgs
+  ) {
+    super(embeddings, args);
+    this.collection = args.collection;
+    this.textKey = args.textKey ?? "text";
+    this.embeddingKey = args.embeddingKey ?? "embedding";
+    this.metadataKey = args.metadataKey ?? "metadata";
+    this.vectorType = args.vectorType ?? "COSINE";
+    this.caller = new AsyncCaller(args);
+  }
+
+  /**
+   * Method to add vectors and their corresponding documents to Firestore
+   * collection.
+   * @param vectors Vectors to be added.
+   * @param documents Corresponding documents to be added.
+   * @returns Promise that resolves when the vectors and documents have been added.
+   */
+  async addVectors(
+    vectors: number[][],
+    documents: DocumentInterface[],
+    options?: { ids?: string[] }
+  ) {
+    const docs = vectors.map((embedding, idx) => ({
+      [this.textKey]: documents[idx].pageContent,
+      [this.embeddingKey]: FieldValue.vector(embedding),
+      [this.metadataKey]: documents[idx].metadata,
+    }));
+    if (options?.ids === undefined) {
+      const batch = this.collection.firestore.batch();
+      const newDocIds: string[] = [];
+
+      // Add new documents to the batch
+      docs.forEach((doc) => {
+        const newDocRef = this.collection.doc(); // Generate a new document reference
+        batch.set(newDocRef, doc);
+        newDocIds.push(newDocRef.id); // Keep track of the generated document IDs
+      });
+
+      await batch.commit();
+      return newDocIds;
+    } else {
+      if (options.ids.length !== vectors.length) {
+        throw new Error(
+          'If provided, "options.ids" must be an array with the same length as "vectors".'
+        );
+      }
+      const batch = this.collection.firestore.batch();
+      const { ids } = options;
+      for (let i = 0; i < docs.length; i += 1) {
+        const docRef = this.collection.doc(ids[i]);
+        // Update the fields you want
+        batch.update(docRef, docs[i]);
+      }
+      await batch.commit();
+      return options?.ids;
+    }
+  }
+
+  /**
+   * Method to add documents to the Firestore collection. It first converts
+   * the documents to vectors using the embeddings and then calls the
+   * addVectors method.
+   * @param documents Documents to be added.
+   * @returns Promise that resolves when the documents have been added.
+   */
+  async addDocuments(documents: DocumentInterface[], options?: { ids?: string[] }) {
+    const texts = documents.map(({ pageContent }) => pageContent);
+    return this.addVectors(
+      await this.embeddings.embedDocuments(texts),
+      documents,
+      options
+    );
+  }
+
+  /**
+   * Method that performs a similarity search on the vectors stored in the
+   * Firestore collection. It returns a list of documents and their
+   * corresponding similarity scores.
+   * @param query Query vector for the similarity search.
+   * @param k Number of nearest neighbors to return.
+   * @param filter Optional filter to be applied.
+   * @returns Promise that resolves to a list of documents and their corresponding similarity scores.
+   */
+  async similaritySearchVectorWithScore(
+    query: number[],
+    k: number,
+    filter?: FirestoreFilter
+  ): Promise<[Document, number][]> {
+    let qry: Query = this.collection;
+    // 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 }
+    );
+    // make the query
+    const vectorQuerySnapshot: VectorQuerySnapshot = await vectorQuery.get();
+    // convert the results
+    const res = vectorQuerySnapshot.docs.map(
+      (doc: QueryDocumentSnapshot): [Document, number] => {
+        const docData = doc.data();
+        const document = new Document({
+          pageContent: docData[this.textKey],
+          metadata: {
+            ...docData[this.metadataKey],
+          },
+        });
+        const resEmbed: number[] = docData[this.embeddingKey]._values;
+        // FIXME: apply the correct distance metric based on this.vectorType
+        // or remove this when Firestore returns it by itself
+        const similarity = cosine(resEmbed, query);
+        return [document, similarity];
+      }
+    );
+    return res;
+  }
+
+  /**
+   * Static method to create an instance of FirestoreVectorSearch from a
+   * list of texts. It first converts the texts to vectors and then adds
+   * them to the Firestore collection.
+   * @param texts List of texts to be converted to vectors.
+   * @param metadatas Metadata for the texts.
+   * @param embeddings Embeddings to be used for conversion.
+   * @param dbConfig Database configuration for Firestore.
+   * @returns Promise that resolves to a new instance of FirestoreVectorSearch.
+   */
+  static async fromTexts(
+    texts: string[],
+    metadatas: object[] | object,
+    embeddings: EmbeddingsInterface,
+    dbConfig: FirestoreVectorSearchLibArgs & { ids?: string[] }
+  ): Promise<FirestoreVectorSearch> {
+    const docs: Document[] = [];
+    for (let i = 0; i < texts.length; i += 1) {
+      const metadata = Array.isArray(metadatas) ? metadatas[i] : metadatas;
+      const newDoc = new Document({
+        pageContent: texts[i],
+        metadata,
+      });
+      docs.push(newDoc);
+    }
+    return FirestoreVectorSearch.fromDocuments(docs, embeddings, dbConfig);
+  }
+
+  /**
+   * Static method to create an instance of FirestoreVectorSearch from a
+   * list of documents. It first converts the documents to vectors and then
+   * adds them to the Firestore collection.
+   * @param docs List of documents to be converted to vectors.
+   * @param embeddings Embeddings to be used for conversion.
+   * @param dbConfig Database configuration for Firestore.
+   * @returns Promise that resolves to a new instance of FirestoreVectorSearch.
+   */
+  static async fromDocuments(
+    docs: DocumentInterface[],
+    embeddings: EmbeddingsInterface,
+    dbConfig: FirestoreVectorSearchLibArgs & { ids?: string[] }
+  ): Promise<FirestoreVectorSearch> {
+    const instance = new this(embeddings, dbConfig);
+    await instance.addDocuments(docs, { ids: dbConfig.ids });
+    return instance;
+  }
+}
+
+function cosine(a: number[], b: number[]): number {
+  let p = 0;
+  let p2 = 0;
+  let q2 = 0;
+  for (let i = 0; i < a.length; i += 1) {
+    p += a[i] * b[i];
+    p2 += a[i] * a[i];
+    q2 += b[i] * b[i];
+  }
+  return p / (Math.sqrt(p2) * Math.sqrt(q2));
+}