diff --git a/libs/langchain-community/src/experimental/text_splitters/semantic_chunker.ts b/libs/langchain-community/src/experimental/text_splitters/semantic_chunker.ts
new file mode 100644
index 000000000000..7271faaeaabf
--- /dev/null
+++ b/libs/langchain-community/src/experimental/text_splitters/semantic_chunker.ts
@@ -0,0 +1,365 @@
+import { BaseDocumentTransformer, Document } from "@langchain/core/documents";
+import { Embeddings } from "@langchain/core/embeddings";
+
+type Sentence = {
+  sentence: string;
+  combined_sentence_embedding?: number[];
+  combined_sentence?: string;
+  distance_to_next?: number;
+};
+
+type SentenceWithCombinedSentence = Sentence & { combined_sentence: string };
+type SentenceWithEmbedding = Sentence & {
+  combined_sentence_embedding: number[];
+  distance_to_next: number;
+};
+
+// Utility function for cosine similarity
+function cosineSimilarity(vecA: number[], vecB: number[]) {
+  const dotProduct = vecA.reduce((sum, a, i) => sum + a * vecB[i], 0)
+  const magnitudeA = Math.sqrt(vecA.reduce((sum, a) => sum + a * a, 0))
+  const magnitudeB = Math.sqrt(vecB.reduce((sum, b) => sum + b * b, 0))
+
+  return magnitudeA && magnitudeB ? dotProduct / (magnitudeA * magnitudeB) : 0
+}
+
+export function combineSentences(
+  sentences: Sentence[],
+  sentencesToCombine: number = 1
+) {
+  /**
+   * Combine sentences based on buffer size.
+   *
+   * @param sentences - List of sentences to combine.
+   * @param sentencesToCombine - Number of sentences to combine. Defaults to 1.
+   * @returns List of sentences with combined sentences.
+   */
+
+  for (let i = 0; i < sentences.length; i++) {
+    let combinedSentence = "";
+
+    // Add sentences before the current one
+    for (let j = i - sentencesToCombine; j < i; j++) {
+      if (j >= 0) {
+        combinedSentence += sentences[j].sentence + " ";
+      }
+    }
+
+    // Add the current sentence
+    combinedSentence += sentences[i].sentence;
+
+    // Add sentences after the current one
+    for (let j = i + 1; j < i + 1 + sentencesToCombine; j++) {
+      if (j < sentences.length) {
+        combinedSentence += " " + sentences[j].sentence;
+      }
+    }
+
+    // Store the combined sentence in the current sentence object
+    sentences[i].combined_sentence = combinedSentence;
+  }
+
+  return sentences as SentenceWithCombinedSentence[];
+}
+
+export function calculateCosineDistances(
+  sentences: SentenceWithEmbedding[]
+): [number[], SentenceWithEmbedding[]] {
+  /**
+   * Calculate cosine distances between sentences.
+   *
+   * @param sentences - List of sentences to calculate distances for.
+   * @returns Tuple of distances and sentences.
+   */
+
+  const distances: number[] = [];
+
+  for (let i = 0; i < sentences.length - 1; i++) {
+    const embeddingCurrent = sentences[i].combined_sentence_embedding;
+    const embeddingNext = sentences[i + 1].combined_sentence_embedding;
+
+    // Calculate cosine similarity
+    const similarity = cosineSimilarity(embeddingCurrent, embeddingNext);
+
+    // Convert to cosine distance
+    const distance = 1 - similarity;
+
+    // Append cosine distance to the list
+    distances.push(distance);
+
+    // Store distance in the dictionary
+    sentences[i].distance_to_next = distance;
+  }
+
+  return [distances, sentences];
+}
+
+enum BreakpointThresholdType {
+  PERCENTILE = "percentile",
+  STANDARD_DEVIATION = "standard_deviation",
+  INTERQUARTILE = "interquartile",
+  GRADIENT = "gradient",
+}
+
+const BREAKPOINT_DEFAULTS: Record<BreakpointThresholdType, number> = {
+  percentile: 95,
+  standard_deviation: 3,
+  interquartile: 1.5,
+  gradient: 95,
+};
+
+interface SemanticChunkerOptions {
+  sentencesToCombine?: number;
+  sentenceSplitRegex?: RegExp;
+  addStartIndex?: boolean;
+  breakpointThresholdType?: BreakpointThresholdType;
+  breakpointThresholdAmount?: number;
+  numberOfChunks?: number;
+  minChunkSize?: number;
+}
+
+export class SemanticChunker extends BaseDocumentTransformer {
+  private sentencesToCombine: number = 1;
+  private sentenceSplitRegex: RegExp = new RegExp(`(?<=[.?!])\\s+`);
+  private addStartIndex: boolean = false;
+  private breakpointThresholdType: BreakpointThresholdType =
+    BreakpointThresholdType.PERCENTILE;
+  private breakpointThresholdAmount: number =
+    BREAKPOINT_DEFAULTS[this.breakpointThresholdType];
+  private numberOfChunks?: number;
+  private minChunkSize?: number;
+
+  constructor(
+    private embeddings: Embeddings,
+    options?: SemanticChunkerOptions
+  ) {
+    super();
+
+    if (options) {
+      if (options.sentencesToCombine !== undefined)
+        this.sentencesToCombine = options.sentencesToCombine;
+      if (options.sentenceSplitRegex !== undefined)
+        this.sentenceSplitRegex = new RegExp(options.sentenceSplitRegex);
+      if (options.addStartIndex !== undefined)
+        this.addStartIndex = options.addStartIndex;
+      if (options.breakpointThresholdType !== undefined) {
+        this.breakpointThresholdType = options.breakpointThresholdType;
+        this.breakpointThresholdAmount =
+          BREAKPOINT_DEFAULTS[options.breakpointThresholdType];
+      }
+      if (options.breakpointThresholdAmount !== undefined)
+        this.breakpointThresholdAmount = options.breakpointThresholdAmount;
+      if (options.numberOfChunks !== undefined)
+        this.numberOfChunks = options.numberOfChunks;
+      if (options.minChunkSize !== undefined)
+        this.minChunkSize = options.minChunkSize;
+    }
+  }
+
+  // Utility functions
+  private mean(arr: number[]): number {
+    return arr.reduce((a, b) => a + b, 0) / arr.length;
+  }
+
+  private std(arr: number[]): number {
+    const mean = this.mean(arr);
+    return Math.sqrt(
+      arr.map((x) => (x - mean) ** 2).reduce((a, b) => a + b, 0) / arr.length
+    );
+  }
+
+  private percentile(arr: number[], p: number): number {
+    const sorted = [...arr].sort((a, b) => a - b);
+    const index = Math.ceil((p / 100) * sorted.length) - 1;
+    return sorted[Math.max(index, 0)];
+  }
+
+  private percentileValues(arr: number[], percentiles: number[]): number[] {
+    return percentiles.map((p) => this.percentile(arr, p));
+  }
+
+  private gradient(arr: number[]): number[] {
+    return arr
+      .map((_, i, a) => (i === 0 ? a[i + 1] - a[i] : a[i] - a[i - 1]))
+      .slice(1);
+  }
+
+  private _calculateBreakpointThreshold(
+    distances: number[]
+  ): [number, number[]] {
+    switch (this.breakpointThresholdType) {
+      case BreakpointThresholdType.PERCENTILE:
+        return [
+          this.percentile(distances, this.breakpointThresholdAmount),
+          distances,
+        ];
+
+      case BreakpointThresholdType.STANDARD_DEVIATION:
+        return [
+          this.mean(distances) +
+            this.breakpointThresholdAmount * this.std(distances),
+          distances,
+        ];
+
+      case BreakpointThresholdType.INTERQUARTILE:
+        const [q1, q3] = this.percentileValues(distances, [25, 75]);
+        const iqr = q3 - q1;
+        return [
+          this.mean(distances) + this.breakpointThresholdAmount * iqr,
+          distances,
+        ];
+
+      case BreakpointThresholdType.GRADIENT:
+        const distanceGradient = this.gradient(distances);
+        return [
+          this.percentile(distanceGradient, this.breakpointThresholdAmount),
+          distanceGradient,
+        ];
+
+      default:
+        throw new Error(
+          `Unexpected breakpointThresholdType: ${this.breakpointThresholdType}`
+        );
+    }
+  }
+
+  private _thresholdFromClusters(distances: number[]): number {
+    if (this.numberOfChunks === undefined) {
+      throw new Error(
+        "This should never be called if `numberOfChunks` is undefined."
+      );
+    }
+
+    const x1 = distances.length;
+    const y1 = 0.0;
+    const x2 = 1.0;
+    const y2 = 100.0;
+
+    const x = Math.max(Math.min(this.numberOfChunks, x1), x2);
+    const y = x2 === x1 ? y2 : y1 + ((y2 - y1) / (x2 - x1)) * (x - x1);
+
+    return this.percentile(distances, Math.min(Math.max(y, 0), 100));
+  }
+
+  private async _calculateSentenceDistances(
+    singleSentencesList: string[]
+  ): Promise<[number[], any[]]> {
+    const sentences = combineSentences(
+      singleSentencesList.map((sentence) => ({ sentence })),
+      this.sentencesToCombine
+    );
+
+    const embeddings = await this.embeddings.embedDocuments(
+      sentences.map((x) => x.combined_sentence)
+    );
+
+    sentences.forEach((sentence, i) => {
+      sentence.combined_sentence_embedding = embeddings[i];
+    });
+
+    return calculateCosineDistances(sentences as SentenceWithEmbedding[]);
+  }
+
+  private _getSingleSentencesList(text: string): string[] {
+    return text.split(this.sentenceSplitRegex);
+  }
+
+  async splitText(text: string): Promise<string[]> {
+    const singleSentencesList = this._getSingleSentencesList(text);
+
+    if (singleSentencesList.length <= 1) return singleSentencesList;
+
+    if (
+      this.breakpointThresholdType === BreakpointThresholdType.GRADIENT &&
+      singleSentencesList.length === 2
+    ) {
+      return singleSentencesList;
+    }
+
+    const [distances, sentences] = await this._calculateSentenceDistances(
+      singleSentencesList
+    );
+    let breakpointDistanceThreshold: number;
+    let breakpointArray: number[];
+
+    if (this.numberOfChunks !== undefined) {
+      breakpointDistanceThreshold = this._thresholdFromClusters(distances);
+      breakpointArray = distances;
+    } else {
+      [breakpointDistanceThreshold, breakpointArray] =
+        this._calculateBreakpointThreshold(distances);
+    }
+
+    const indicesAboveThresh = breakpointArray
+      .map((x, i) => (x > breakpointDistanceThreshold ? i : -1))
+      .filter((i) => i !== -1);
+
+    const chunks: string[] = [];
+    let startIndex = 0;
+
+    for (const index of indicesAboveThresh) {
+      const endIndex = index;
+      const group = sentences.slice(startIndex, endIndex + 1);
+      const combinedText = group.map((d) => d.sentence).join(" ");
+
+      if (
+        this.minChunkSize !== undefined &&
+        combinedText.length < this.minChunkSize
+      )
+        continue;
+      chunks.push(combinedText);
+      startIndex = index + 1;
+    }
+
+    if (startIndex < sentences.length) {
+      chunks.push(
+        sentences
+          .slice(startIndex)
+          .map((d) => d.sentence)
+          .join(" ")
+      );
+    }
+
+    return chunks;
+  }
+
+  async createDocuments(
+    texts: string[],
+    metadatas?: Record<string, any>[]
+  ): Promise<Document[]> {
+    const _metadatas = metadatas || Array(texts.length).fill({});
+    const documents: Document[] = [];
+
+    for (const [i, text] of texts.entries()) {
+      let startIndex = 0;
+      const chunks = await this.splitText(text);
+
+      chunks.forEach((chunk) => {
+        const metadata = { ..._metadatas[i] };
+
+        if (this.addStartIndex) metadata.start_index = startIndex;
+
+        documents.push(new Document({ pageContent: chunk, metadata }));
+        startIndex += chunk.length;
+      });
+    }
+
+    return documents;
+  }
+
+  async splitDocuments(documents: Document[]): Promise<Document[]> {
+    const texts: string[] = [];
+    const metadatas: Record<string, any>[] = [];
+
+    for (const doc of documents) {
+      texts.push(doc.pageContent);
+      metadatas.push(doc.metadata);
+    }
+
+    return this.createDocuments(texts, metadatas);
+  }
+
+  async transformDocuments(documents: Document[]): Promise<Document[]> {
+    return this.splitDocuments(documents);
+  }
+}
diff --git a/libs/langchain-community/src/experimental/text_splitters/tests/semantic_chunker.test.ts b/libs/langchain-community/src/experimental/text_splitters/tests/semantic_chunker.test.ts
new file mode 100644
index 000000000000..0d6c0100d839
--- /dev/null
+++ b/libs/langchain-community/src/experimental/text_splitters/tests/semantic_chunker.test.ts
@@ -0,0 +1,126 @@
+import {
+  combineSentences,
+  calculateCosineDistances,
+} from "../semantic_chunker.js";
+
+describe("combineSentences", () => {
+  test("combines sentences with default sentencesToCombine (1)", () => {
+    const sentences = [
+      { sentence: "This is sentence one." },
+      { sentence: "This is sentence two." },
+      { sentence: "This is sentence three." },
+    ];
+
+    const result = combineSentences(sentences);
+
+    expect(result).toEqual([
+      {
+        sentence: "This is sentence one.",
+        combined_sentence: "This is sentence one. This is sentence two.",
+      },
+      {
+        sentence: "This is sentence two.",
+        combined_sentence:
+          "This is sentence one. This is sentence two. This is sentence three.",
+      },
+      {
+        sentence: "This is sentence three.",
+        combined_sentence: "This is sentence two. This is sentence three.",
+      },
+    ]);
+  });
+
+  test("handles an empty array of sentences", () => {
+    const sentences: any[] = [];
+
+    const result = combineSentences(sentences);
+
+    expect(result).toEqual([]);
+  });
+
+  test("handles a single sentence", () => {
+    const sentences = [{ sentence: "Only one sentence here." }];
+
+    const result = combineSentences(sentences);
+
+    expect(result).toEqual([
+      {
+        sentence: "Only one sentence here.",
+        combined_sentence: "Only one sentence here.",
+      },
+    ]);
+  });
+});
+
+describe("calculateCosineDistances", () => {
+  test("calculates cosine distances between sentence embeddings", () => {
+    const sentences = [
+      {
+        sentence: "Sentence one.",
+        combined_sentence_embedding: [1, 0, 0],
+        distance_to_next: 0,
+      },
+      {
+        sentence: "Sentence two.",
+        combined_sentence_embedding: [0, 1, 0],
+        distance_to_next: 0,
+      },
+      {
+        sentence: "Sentence three.",
+        combined_sentence_embedding: [0, 0, 1],
+        distance_to_next: 0,
+      },
+    ];
+
+    const [distances, updatedSentences] = calculateCosineDistances(sentences);
+
+    expect(distances).toEqual([1, 1]);
+    expect(updatedSentences).toEqual([
+      {
+        sentence: "Sentence one.",
+        combined_sentence_embedding: [1, 0, 0],
+        distance_to_next: 1,
+      },
+      {
+        sentence: "Sentence two.",
+        combined_sentence_embedding: [0, 1, 0],
+        distance_to_next: 1,
+      },
+      {
+        sentence: "Sentence three.",
+        combined_sentence_embedding: [0, 0, 1],
+        distance_to_next: 0,
+      },
+    ]);
+  });
+
+  test("handles a single sentence with no distances to calculate", () => {
+    const sentences = [
+      {
+        sentence: "Only one sentence.",
+        combined_sentence_embedding: [1, 0, 0],
+        distance_to_next: 0,
+      },
+    ];
+
+    const [distances, updatedSentences] = calculateCosineDistances(sentences);
+
+    expect(distances).toEqual([]);
+    expect(updatedSentences).toEqual([
+      {
+        sentence: "Only one sentence.",
+        combined_sentence_embedding: [1, 0, 0],
+        distance_to_next: 0,
+      },
+    ]);
+  });
+
+  test("handles an empty array of sentences", () => {
+    const sentences: any[] = [];
+
+    const [distances, updatedSentences] = calculateCosineDistances(sentences);
+
+    expect(distances).toEqual([]);
+    expect(updatedSentences).toEqual([]);
+  });
+});