Class that provides an interface to a Vercel Postgres vector database. It extends the VectorStore base class and implements methods for adding documents and vectors and performing similarity searches.

Hierarchy

  • VectorStore
    • VercelPostgres

Constructors

Properties

FilterType: Metadata
client: VercelPoolClient
contentColumnName: string
embeddings: EmbeddingsInterface

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

filter?: Metadata
idColumnName: string
metadataColumnName: string
pool: VercelPool
tableName: string
vectorColumnName: string

Methods

  • 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[];
      }
      • Optionalids?: string[]

    Returns Promise<string[]>

    Promise that resolves when the documents have been added.

  • 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[];
      }
      • Optionalids?: string[]

    Returns Promise<string[]>

    Promise that resolves when the vectors have been added.

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

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

      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<VercelPostgres>

    • A configured VectorStoreRetriever instance based on the provided parameters.

    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 },
    });
  • Deletes documents from the vector store based on the specified parameters.

    Parameters

    • params: {
          deleteAll?: boolean;
          ids?: string[];
      }
      • OptionaldeleteAll?: boolean
      • Optionalids?: string[]

    Returns Promise<void>

    A promise that resolves once the deletion is complete.

  • Generates the SQL placeholders for a specific row at the provided index.

    Parameters

    • row: (string | Record<string, any>)[]
    • index: number

      The index of the row for which placeholders need to be generated.

    Returns string

    The SQL placeholders for the row values.

  • 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.
  • Constructs the SQL query for inserting rows into the specified table.

    Parameters

    • rows: (string | Record<string, any>)[][]

      The rows of data to be inserted, consisting of values and records.

    • useIdColumn: boolean

    Returns Promise<QueryResult<any>>

    The complete SQL INSERT INTO query string.

  • 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.

  • 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<[Document<Record<string, any>>, number][]>

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

  • 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.

  • Returns Serialized

  • Static method to create a new VercelPostgres 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.

    • OptionaldbConfig: Partial<VercelPostgresFields> & {
          postgresConnectionOptions?: VercelPostgresPoolConfig;
      }

    Returns Promise<VercelPostgres>

    Promise that resolves with a new instance of VercelPostgres.

  • Static method to create a new VercelPostgres 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.

    • OptionaldbConfig: Partial<VercelPostgresFields> & {
          postgresConnectionOptions?: VercelPostgresPoolConfig;
      }

    Returns Promise<VercelPostgres>

    Promise that resolves with a new instance of VercelPostgres.

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