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mongodb_atlas.ts
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mongodb_atlas.ts
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import type { Collection, Document as MongoDBDocument } from "mongodb";
import { MaxMarginalRelevanceSearchOptions, VectorStore } from "./base.js";
import { Embeddings } from "../embeddings/base.js";
import { Document } from "../document.js";
import { maximalMarginalRelevance } from "../util/math.js";
/**
* Type that defines the arguments required to initialize the
* MongoDBAtlasVectorSearch class. It includes the MongoDB collection,
* index name, text key, and embedding key.
*/
export type MongoDBAtlasVectorSearchLibArgs = {
readonly collection: Collection<MongoDBDocument>;
readonly indexName?: string;
readonly textKey?: string;
readonly embeddingKey?: string;
};
/**
* 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;
/**
* 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;
_vectorstoreType(): string {
return "mongodb_atlas";
}
constructor(embeddings: Embeddings, args: MongoDBAtlasVectorSearchLibArgs) {
super(embeddings, args);
this.collection = args.collection;
this.indexName = args.indexName ?? "default";
this.textKey = args.textKey ?? "text";
this.embeddingKey = args.embeddingKey ?? "embedding";
}
/**
* 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[]): Promise<void> {
const docs = vectors.map((embedding, idx) => ({
[this.textKey]: documents[idx].pageContent,
[this.embeddingKey]: embedding,
...documents[idx].metadata,
}));
await this.collection.insertMany(docs);
}
/**
* 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[]): Promise<void> {
const texts = documents.map(({ pageContent }) => pageContent);
return this.addVectors(
await this.embeddings.embedDocuments(texts),
documents
);
}
/**
* 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: 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(
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: Embeddings,
dbConfig: MongoDBAtlasVectorSearchLibArgs
): 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: Embeddings,
dbConfig: MongoDBAtlasVectorSearchLibArgs
): Promise<MongoDBAtlasVectorSearch> {
const instance = new this(embeddings, dbConfig);
await instance.addDocuments(docs);
return instance;
}
}