mongodb_atlas.ts

import type { Collection, Document as MongoDBDocument } from "mongodb";
import {
  MaxMarginalRelevanceSearchOptions,
  VectorStore,
} from "@langchain/core/vectorstores";
import type { EmbeddingsInterface } from "@langchain/core/embeddings";
import { Document } from "@langchain/core/documents";
import { maximalMarginalRelevance } from "@langchain/core/utils/math";
import {
  AsyncCaller,
  AsyncCallerParams,
} from "@langchain/core/utils/async_caller";

/**
 * @deprecated Install and import from the "@langchain/mongodb" integration package instead.
 * Type that defines the arguments required to initialize the
 * MongoDBAtlasVectorSearch class. It includes the MongoDB collection,
 * index name, text key, embedding key, primary key, and overwrite flag.
 *
 * @param collection MongoDB collection to store the vectors.
 * @param indexName A Collections Index Name.
 * @param textKey Corresponds to the plaintext of 'pageContent'.
 * @param embeddingKey Key to store the embedding under.
 * @param primaryKey The Key to use for upserting documents.
 */
export interface MongoDBAtlasVectorSearchLibArgs extends AsyncCallerParams {
  readonly collection: Collection<MongoDBDocument>;
  readonly indexName?: string;
  readonly textKey?: string;
  readonly embeddingKey?: string;
  readonly primaryKey?: string;
}

/**
 * @deprecated Install and import from the "@langchain/mongodb" integration package instead.
 * Type that defines the filter used in the
 * similaritySearchVectorWithScore and maxMarginalRelevanceSearch methods.
 * It includes pre-filter, post-filter pipeline, and a flag to include
 * embeddings.
 */
type MongoDBAtlasFilter = {
  preFilter?: MongoDBDocument;
  postFilterPipeline?: MongoDBDocument[];
  includeEmbeddings?: boolean;
} & MongoDBDocument;

/**
 * @deprecated Install and import from the "@langchain/mongodb" integration package instead.
 * Class that is a wrapper around MongoDB Atlas Vector Search. It is used
 * to store embeddings in MongoDB documents, create a vector search index,
 * and perform K-Nearest Neighbors (KNN) search with an approximate
 * nearest neighbor algorithm.
 */
export class MongoDBAtlasVectorSearch extends VectorStore {
  declare FilterType: MongoDBAtlasFilter;

  private readonly collection: Collection<MongoDBDocument>;

  private readonly indexName: string;

  private readonly textKey: string;

  private readonly embeddingKey: string;

  private readonly primaryKey: string;

  private caller: AsyncCaller;

  _vectorstoreType(): string {
    return "mongodb_atlas";
  }

  constructor(
    embeddings: EmbeddingsInterface,
    args: MongoDBAtlasVectorSearchLibArgs
  ) {
    super(embeddings, args);
    this.collection = args.collection;
    this.indexName = args.indexName ?? "default";
    this.textKey = args.textKey ?? "text";
    this.embeddingKey = args.embeddingKey ?? "embedding";
    this.primaryKey = args.primaryKey ?? "_id";
    this.caller = new AsyncCaller(args);
  }

  /**
   * Method to add vectors and their corresponding documents to the MongoDB
   * 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: Document[],
    options?: { ids?: string[] }
  ) {
    const docs = vectors.map((embedding, idx) => ({
      [this.textKey]: documents[idx].pageContent,
      [this.embeddingKey]: embedding,
      ...documents[idx].metadata,
    }));
    if (options?.ids === undefined) {
      await this.collection.insertMany(docs);
    } 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 { ids } = options;
      for (let i = 0; i < docs.length; i += 1) {
        await this.caller.call(async () => {
          await this.collection.updateOne(
            { [this.primaryKey]: ids[i] },
            { $set: { [this.primaryKey]: ids[i], ...docs[i] } },
            { upsert: true }
          );
        });
      }
    }
    return options?.ids ?? docs.map((doc) => doc[this.primaryKey]);
  }

  /**
   * Method to add documents to the MongoDB 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: Document[], 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
   * MongoDB 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?: MongoDBAtlasFilter
  ): Promise<[Document, number][]> {
    const postFilterPipeline = filter?.postFilterPipeline ?? [];
    const preFilter: MongoDBDocument | undefined =
      filter?.preFilter ||
      filter?.postFilterPipeline ||
      filter?.includeEmbeddings
        ? filter.preFilter
        : filter;
    const removeEmbeddingsPipeline = !filter?.includeEmbeddings
      ? [
          {
            $project: {
              [this.embeddingKey]: 0,
            },
          },
        ]
      : [];

    const pipeline: MongoDBDocument[] = [
      {
        $vectorSearch: {
          queryVector: MongoDBAtlasVectorSearch.fixArrayPrecision(query),
          index: this.indexName,
          path: this.embeddingKey,
          limit: k,
          numCandidates: 10 * k,
          ...(preFilter && { filter: preFilter }),
        },
      },
      {
        $set: {
          score: { $meta: "vectorSearchScore" },
        },
      },
      ...removeEmbeddingsPipeline,
      ...postFilterPipeline,
    ];

    const results = this.collection
      .aggregate(pipeline)
      .map<[Document, number]>((result) => {
        const { score, [this.textKey]: text, ...metadata } = result;
        return [new Document({ pageContent: text, metadata }), score];
      });

    return results.toArray();
  }

  /**
   * Return documents selected using the maximal marginal relevance.
   * Maximal marginal relevance optimizes for similarity to the query AND diversity
   * among selected documents.
   *
   * @param {string} query - Text to look up documents similar to.
   * @param {number} options.k - Number of documents to return.
   * @param {number} options.fetchK=20- Number of documents to fetch before passing to the MMR algorithm.
   * @param {number} options.lambda=0.5 - Number between 0 and 1 that determines the degree of diversity among the results,
   *                 where 0 corresponds to maximum diversity and 1 to minimum diversity.
   * @param {MongoDBAtlasFilter} options.filter - Optional Atlas Search operator to pre-filter on document fields
   *                                      or post-filter following the knnBeta search.
   *
   * @returns {Promise<Document[]>} - List of documents selected by maximal marginal relevance.
   */
  async maxMarginalRelevanceSearch(
    query: string,
    options: MaxMarginalRelevanceSearchOptions<this["FilterType"]>
  ): Promise<Document[]> {
    const { k, fetchK = 20, lambda = 0.5, filter } = options;

    const queryEmbedding = await this.embeddings.embedQuery(query);

    // preserve the original value of includeEmbeddings
    const includeEmbeddingsFlag = options.filter?.includeEmbeddings || false;

    // update filter to include embeddings, as they will be used in MMR
    const includeEmbeddingsFilter = {
      ...filter,
      includeEmbeddings: true,
    };

    const resultDocs = await this.similaritySearchVectorWithScore(
      MongoDBAtlasVectorSearch.fixArrayPrecision(queryEmbedding),
      fetchK,
      includeEmbeddingsFilter
    );

    const embeddingList = resultDocs.map(
      (doc) => doc[0].metadata[this.embeddingKey]
    );

    const mmrIndexes = maximalMarginalRelevance(
      queryEmbedding,
      embeddingList,
      lambda,
      k
    );

    return mmrIndexes.map((idx) => {
      const doc = resultDocs[idx][0];

      // remove embeddings if they were not requested originally
      if (!includeEmbeddingsFlag) {
        delete doc.metadata[this.embeddingKey];
      }
      return doc;
    });
  }

  /**
   * Static method to create an instance of MongoDBAtlasVectorSearch from a
   * list of texts. It first converts the texts to vectors and then adds
   * them to the MongoDB 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 MongoDB Atlas.
   * @returns Promise that resolves to a new instance of MongoDBAtlasVectorSearch.
   */
  static async fromTexts(
    texts: string[],
    metadatas: object[] | object,
    embeddings: EmbeddingsInterface,
    dbConfig: MongoDBAtlasVectorSearchLibArgs & { ids?: string[] }
  ): Promise<MongoDBAtlasVectorSearch> {
    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 MongoDBAtlasVectorSearch.fromDocuments(docs, embeddings, dbConfig);
  }

  /**
   * Static method to create an instance of MongoDBAtlasVectorSearch from a
   * list of documents. It first converts the documents to vectors and then
   * adds them to the MongoDB collection.
   * @param docs List of documents to be converted to vectors.
   * @param embeddings Embeddings to be used for conversion.
   * @param dbConfig Database configuration for MongoDB Atlas.
   * @returns Promise that resolves to a new instance of MongoDBAtlasVectorSearch.
   */
  static async fromDocuments(
    docs: Document[],
    embeddings: EmbeddingsInterface,
    dbConfig: MongoDBAtlasVectorSearchLibArgs & { ids?: string[] }
  ): Promise<MongoDBAtlasVectorSearch> {
    const instance = new this(embeddings, dbConfig);
    await instance.addDocuments(docs, { ids: dbConfig.ids });
    return instance;
  }

  /**
   * Static method to fix the precision of the array that ensures that
   * every number in this array is always float when casted to other types.
   * This is needed since MongoDB Atlas Vector Search does not cast integer
   * inside vector search to float automatically.
   * This method shall introduce a hint of error but should be safe to use
   * since introduced error is very small, only applies to integer numbers
   * returned by embeddings, and most embeddings shall not have precision
   * as high as 15 decimal places.
   * @param array Array of number to be fixed.
   * @returns
   */
  static fixArrayPrecision(array: number[]) {
    return array.map((value) => {
      if (Number.isInteger(value)) {
        return value + 0.000000000000001;
      }
      return value;
    });
  }
}