Class MariaDBStore

MariaDB vector store integration.

Setup: Install @langchain/community and mariadb.

If you wish to generate ids, you should also install the uuid package.

npm install @langchain/community mariadb uuid

Constructor args

Instantiate 
import {
  MariaDBStore,
  DistanceStrategy,
} from "@langchain/community/vectorstores/mariadb";

// Or other embeddings
import { OpenAIEmbeddings } from "@langchain/openai";
import { PoolConfig } from "mariadb";

const embeddings = new OpenAIEmbeddings({
  model: "text-embedding-3-small",
});

// Sample config
const config = {
  connectionOptions: {
    host: "127.0.0.1",
    port: 3306,
    user: "myuser",
    password: "ChangeMe",
    database: "api",
  } as PoolConfig,
  tableName: "testlangchainjs",
  columns: {
    idColumnName: "id",
    vectorColumnName: "vector",
    contentColumnName: "content",
    metadataColumnName: "metadata",
  },
  // supported distance strategies: COSINE (default) or EUCLIDEAN
  distanceStrategy: "COSINE" as DistanceStrategy,
};

const vectorStore = await MariaDBStore.initialize(embeddings, config);

Add documents 
import type { Document } from '@langchain/core/documents';

const document1 = { pageContent: "foo", metadata: { baz: "bar" } };
const document2 = { pageContent: "thud", metadata: { bar: "baz" } };
const document3 = { pageContent: "i will be deleted :(", metadata: {} };

const documents: Document[] = [document1, document2, document3];
const ids = ["1", "2", "3"];
await vectorStore.addDocuments(documents, { ids });

Delete documents 
await vectorStore.delete({ ids: ["3"] });

Similarity search 
const results = await vectorStore.similaritySearch("thud", 1);
for (const doc of results) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
// Output: * thud [{"baz":"bar"}]

Similarity search with filter 
const resultsWithFilter = await vectorStore.similaritySearch("thud", 1, {"country": "BG"});

for (const doc of resultsWithFilter) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
// Output: * foo [{"baz":"bar"}]

Similarity search with score 
const resultsWithScore = await vectorStore.similaritySearchWithScore("qux", 1);
for (const [doc, score] of resultsWithScore) {
  console.log(`* [SIM=${score.toFixed(6)}] ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
// Output: * [SIM=0.000000] qux [{"bar":"baz","baz":"bar"}]

As a retriever 
const retriever = vectorStore.asRetriever({
  searchType: "mmr", // Leave blank for standard similarity search
  k: 1,
});
const resultAsRetriever = await retriever.invoke("thud");
console.log(resultAsRetriever);

// Output: [Document({ metadata: { "baz":"bar" }, pageContent: "thud" })]

Hierarchy

  • VectorStore
    • MariaDBStore

Constructors

constructor

Properties

FilterType chunkSize collectionId? collectionMetadata collectionName collectionTableName? contentColumnName distanceStrategy embeddings idColumnName metadataColumnName pool schemaName tableName vectorColumnName

Accessors

computedCollectionTableName computedTableName

Methods

addDocuments addVectors asRetriever delete end ensureCollectionTableInDatabase ensureTableInDatabase maxMarginalRelevanceSearch? similaritySearch similaritySearchVectorWithScore similaritySearchWithScore toJSON toJSONNotImplemented fromDocuments fromTexts initialize

Constructors

constructor

Properties

FilterType

FilterType: string | object

Defines the filter type used in search and delete operations. Can be an object for structured conditions or a string for simpler filtering.

chunkSize

chunkSize: number = 500

OptionalcollectionId

collectionId?: string

collectionMetadata

collectionMetadata: null | Metadata

collectionName

collectionName: string = "langchain"

OptionalcollectionTableName

collectionTableName?: string

contentColumnName

contentColumnName: string

distanceStrategy

distanceStrategy: DistanceStrategy

embeddings

embeddings: EmbeddingsInterface<number[]>

Embeddings interface for generating vector embeddings from text queries, enabling vector-based similarity searches.

idColumnName

idColumnName: string

metadataColumnName

metadataColumnName: string

pool

pool: Pool

schemaName

schemaName: null | string

tableName

tableName: string

vectorColumnName

vectorColumnName: string

Accessors

computedCollectionTableName

computedTableName

Methods

addDocuments

  • addDocuments(documents, options?): Promise<void>

  • Method to add documents to the vector store. It converts the documents into vectors, and adds them to the store.

    Parameters

    • documents: Document<Record<string, any>>[]

      Array of Document instances.

    • Optionaloptions: {
          ids?: string[];
      }

      Optional arguments for adding documents

      • Optionalids?: string[]

    Returns Promise<void>

    Promise that resolves when the documents have been added.

addVectors

  • addVectors(vectors, documents, options?): Promise<void>

  • Method to add vectors to the vector store. It converts the vectors into rows and inserts them into the database.

    Parameters

    • vectors: number[][]

      Array of vectors.

    • documents: Document<Record<string, any>>[]

      Array of Document instances.

    • Optionaloptions: {
          ids?: string[];
      }

      Optional arguments for adding documents

      • Optionalids?: string[]

    Returns Promise<void>

    Promise that resolves when the vectors have been added.

asRetriever

  • asRetriever(kOrFields?, filter?, callbacks?, tags?, metadata?, verbose?): VectorStoreRetriever<MariaDBStore>

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

    • OptionalkOrFields: number | Partial<VectorStoreRetrieverInput<MariaDBStore>>

      If a number is provided, it sets the k parameter (number of items to retrieve).

      • If an object is provided, it should contain various configuration options.
    • Optionalfilter: string | object

      Optional filter criteria to limit the items retrieved based on the specified filter type.

    • Optionalcallbacks: Callbacks

      Optional callbacks that may be triggered at specific stages of the retrieval process.

    • Optionaltags: string[]

      Tags to categorize or label the VectorStoreRetriever. Defaults to an empty array if not provided.

    • Optionalmetadata: Record<string, unknown>

      Additional metadata as key-value pairs to add contextual information for the retrieval process.

    • Optionalverbose: boolean

      If true, enables detailed logging for the retrieval process. Defaults to false.

    Returns VectorStoreRetriever<MariaDBStore>

    • A configured VectorStoreRetriever instance based on the provided parameters.

    Example

    Basic usage with a k value:

    const retriever = myVectorStore.asRetriever(5);

    Usage with a configuration object:

    const retriever = myVectorStore.asRetriever({
      k: 10,
      filter: myFilter,
      tags: ['example', 'test'],
      verbose: true,
      searchType: 'mmr',
      searchKwargs: { alpha: 0.5 },
    });

delete

  • delete(params): Promise<void>

  • Method to delete documents from the vector store. It deletes the documents that match the provided ids

    Parameters

    • params: {
          filter?: Record<string, unknown>;
          ids?: string[];
      }
      • Optionalfilter?: Record<string, unknown>
      • Optionalids?: string[]

    Returns Promise<void>

    Promise that resolves when the documents have been deleted.

    Example

    await vectorStore.delete(["id1", "id2"]);

end

ensureCollectionTableInDatabase

  • ensureCollectionTableInDatabase(): Promise<void>

  • Method to ensure the existence of the collection table in the database. It creates the table if it does not already exist.

    Returns Promise<void>

    Promise that resolves when the collection table has been ensured.

ensureTableInDatabase

  • ensureTableInDatabase(dimensions?): Promise<void>

  • Method to ensure the existence of the table in the database. It creates the table if it does not already exist.

    Parameters

    • dimensions: number = 1536

      Number of dimensions in your vector data type. Default to 1536.

    Returns Promise<void>

    Promise that resolves when the table has been ensured.

OptionalmaxMarginalRelevanceSearch

  • maxMarginalRelevanceSearch(query, options, _callbacks): Promise<DocumentInterface<Record<string, any>>[]>

  • Return documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.

    Parameters

    • query: string

      Text to look up documents similar to.

    • options: MaxMarginalRelevanceSearchOptions<string | object>
    • _callbacks: undefined | Callbacks

    Returns Promise<DocumentInterface<Record<string, any>>[]>

    • List of documents selected by maximal marginal relevance.

similaritySearch

  • similaritySearch(query, k?, filter?, _callbacks?): Promise<DocumentInterface<Record<string, any>>[]>

  • Searches for documents similar to a text query by embedding the query and performing a similarity search on the resulting vector.

    Parameters

    • query: string

      Text query for finding similar documents.

    • Optionalk: number

      Number of similar results to return. Defaults to 4.

    • Optionalfilter: string | object

      Optional filter based on FilterType.

    • Optional_callbacks: Callbacks

      Optional callbacks for monitoring search progress

    Returns Promise<DocumentInterface<Record<string, any>>[]>

    A promise resolving to an array of DocumentInterface instances representing similar documents.

similaritySearchVectorWithScore

  • similaritySearchVectorWithScore(query, k, filter?): Promise<[Document<Record<string, any>>, number][]>

  • Method to perform a similarity search in the vector store. It returns the k most similar documents to the query vector, along with their similarity scores.

    Parameters

    • query: number[]

      Query vector.

    • k: number

      Number of most similar documents to return.

    • Optionalfilter: Record<string, unknown>

      Optional filter to apply to the search.

    Returns Promise<[Document<Record<string, any>>, number][]>

    Promise that resolves with an array of tuples, each containing a Document and its similarity score.

similaritySearchWithScore

  • similaritySearchWithScore(query, k?, filter?, _callbacks?): Promise<[DocumentInterface<Record<string, any>>, number][]>

  • Searches for documents similar to a text query by embedding the query, and returns results with similarity scores.

    Parameters

    • query: string

      Text query for finding similar documents.

    • Optionalk: number

      Number of similar results to return. Defaults to 4.

    • Optionalfilter: string | object

      Optional filter based on FilterType.

    • Optional_callbacks: Callbacks

      Optional callbacks for monitoring search progress

    Returns Promise<[DocumentInterface<Record<string, any>>, number][]>

    A promise resolving to an array of tuples, each containing a document and its similarity score.

toJSON

  • toJSON(): Serialized

  • Returns Serialized

toJSONNotImplemented

StaticfromDocuments

  • fromDocuments(docs, embeddings, dbConfig): Promise<MariaDBStore>

  • Static method to create a new MariaDBStore instance from an array of Document instances. It adds the documents to the store.

    Parameters

    • docs: Document<Record<string, any>>[]

      Array of Document instances.

    • embeddings: EmbeddingsInterface<number[]>

      Embeddings instance.

    • dbConfig: MariaDBStoreArgs & {
          dimensions?: number;
      }

      MariaDBStoreArgs instance.

    Returns Promise<MariaDBStore>

    Promise that resolves with a new instance of MariaDBStore.

StaticfromTexts

  • fromTexts(texts, metadatas, embeddings, dbConfig): Promise<MariaDBStore>

  • Static method to create a new MariaDBStore instance from an array of texts and their metadata. It converts the texts into Document instances and adds them to the store.

    Parameters

    • texts: string[]

      Array of texts.

    • metadatas: object | object[]

      Array of metadata objects or a single metadata object.

    • embeddings: EmbeddingsInterface<number[]>

      Embeddings instance.

    • dbConfig: MariaDBStoreArgs & {
          dimensions?: number;
      }

      MariaDBStoreArgs instance.

    Returns Promise<MariaDBStore>

    Promise that resolves with a new instance of MariaDBStore.

Staticinitialize

  • initialize(embeddings, config): Promise<MariaDBStore>

  • Static method to create a new MariaDBStore instance from a connection. It creates a table if one does not exist, and calls connect to return a new instance of MariaDBStore.

    Parameters

    • embeddings: EmbeddingsInterface<number[]>

      Embeddings instance.

    • config: MariaDBStoreArgs & {
          dimensions?: number;
      }

    Returns Promise<MariaDBStore>

    A new instance of MariaDBStore.

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