Class NeonPostgres

Class that provides an interface to a Neon Postgres 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
    • NeonPostgres

Constructors

constructor

Properties

FilterType contentColumnName embeddings filter? idColumnName metadataColumnName neonConnectionString schemaName? tableName vectorColumnName

Accessors

computedTableName

Methods

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

Constructors

constructor

Properties

FilterType

FilterType: Metadata

contentColumnName

contentColumnName: string

embeddings

embeddings: EmbeddingsInterface<number[]>

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

Optionalfilter

filter?: Metadata

idColumnName

idColumnName: string

metadataColumnName

metadataColumnName: string

neonConnectionString

neonConnectionString: string

OptionalschemaName

schemaName?: string

tableName

tableName: string

vectorColumnName

vectorColumnName: string

Accessors

computedTableName

Methods

addDocuments

  • addDocuments(documents, options?): Promise<string[]>

  • 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<string[]>

    Promise that resolves when the documents have been added.

addVectors

  • addVectors(vectors, documents, options?): Promise<string[]>

  • 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<string[]>

    Promise that resolves when the vectors have been added.

asRetriever

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

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

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

      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<NeonPostgres>

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

    Returns Promise<void>

    Promise that resolves when the documents have been deleted.

ensureTableInDatabase

  • ensureTableInDatabase(): Promise<void>

  • Method to ensure the existence of the table to store vectors 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.

ProtectedrunInsertQuery

  • runInsertQuery(rows, useIdColumn): Promise<Record<string, any>[]>

  • 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<Record<string, any>[]>

    The complete SQL INSERT INTO query string.

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<[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: 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.

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

StaticfromDocuments

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

  • Static method to create a new NeonPostgres 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: NeonPostgresArgs

      NeonPostgreseArgs instance.

    Returns Promise<NeonPostgres>

    Promise that resolves with a new instance of NeonPostgres.

StaticfromTexts

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

  • Static method to create a new NeonPostgres 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: NeonPostgresArgs

      NeonPostgresArgs instance.

    Returns Promise<NeonPostgres>

    Promise that resolves with a new instance of NeonPostgresArgs.

Staticinitialize

Settings

Member Visibility

On This Page

Constructors
Properties
Accessors
Methods