Class TypeORMVectorStore

Class that provides an interface to a Postgres vector database. It extends the VectorStore base class and implements methods for adding documents and vectors, performing similarity searches, and ensuring the existence of a table in the database.

Hierarchy

  • VectorStore
    • TypeORMVectorStore

Properties

FilterType appDataSource documentEntity embeddings filter? schemaName? tableName

Methods

addDocuments addVectors asRetriever delete ensureTableInDatabase maxMarginalRelevanceSearch? similaritySearch similaritySearchVectorWithScore similaritySearchWithScore toJSON toJSONNotImplemented fromDataSource fromDocuments fromExistingIndex fromTexts

Properties

FilterType

FilterType: Metadata

appDataSource

appDataSource: DataSource

documentEntity

documentEntity: EntitySchema<any>

embeddings

embeddings: EmbeddingsInterface

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

Optionalfilter

filter?: Metadata

OptionalschemaName

schemaName?: string

tableName

tableName: string

Methods

addDocuments

  • addDocuments(documents): Promise<void>

  • Method to add documents to the vector store. It ensures the existence of the table in the database, converts the documents into vectors, and adds them to the store.

    Parameters

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

      Array of Document instances.

    Returns Promise<void>

    Promise that resolves when the documents have been added.

addVectors

  • addVectors(vectors, documents): 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.

    Returns Promise<void>

    Promise that resolves when the vectors have been added.

asRetriever

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

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

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

      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: Metadata

      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<TypeORMVectorStore>

    • 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>

  • Deletes documents from the vector store based on the specified parameters.

    Parameters

    • Optional_params: Record<string, any>

      Flexible key-value pairs defining conditions for document deletion.

    Returns Promise<void>

    A promise that resolves once the deletion is complete.

ensureTableInDatabase

  • ensureTableInDatabase(): Promise<void>

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

    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<Metadata>
    • _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: Metadata

      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<[TypeORMVectorStoreDocument, 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: Metadata

      Optional filter to apply to the search.

    Returns Promise<[TypeORMVectorStoreDocument, number][]>

    Promise that resolves with an array of tuples, each containing a TypeORMVectorStoreDocument 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: Metadata

      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

StaticfromDataSource

StaticfromDocuments

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

  • Static method to create a new TypeORMVectorStore 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

      Embeddings instance.

    • dbConfig: TypeORMVectorStoreArgs

      TypeORMVectorStoreArgs instance.

    Returns Promise<TypeORMVectorStore>

    Promise that resolves with a new instance of TypeORMVectorStore.

StaticfromExistingIndex

StaticfromTexts

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

  • Static method to create a new TypeORMVectorStore 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

      Embeddings instance.

    • dbConfig: TypeORMVectorStoreArgs

      TypeORMVectorStoreArgs instance.

    Returns Promise<TypeORMVectorStore>

    Promise that resolves with a new instance of TypeORMVectorStore.

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