Class HanaDB

Hierarchy

  • VectorStore
    • HanaDB

Constructors

constructor

Properties

FilterType embeddings

Methods

addDocuments addVectors asRetriever checkColumn delete initialize maxMarginalRelevanceSearch similaritySearch similaritySearchVectorWithScore similaritySearchWithScore similaritySearchWithScoreAndVectorByVector tableExists toJSON toJSONNotImplemented fromDocuments fromTexts parseFloatArrayFromString sanitizeInt sanitizeListFloat sanitizeName

Constructors

constructor

Properties

FilterType

FilterType: Filter

embeddings

embeddings: EmbeddingsInterface

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

Methods

addDocuments

  • addDocuments(documents): Promise<void>

  • Adds an array of documents to the table. The documents are first converted to vectors using the embedDocuments method of the embeddings instance.

    Parameters

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

      Array of Document instances to be added to the table.

    Returns Promise<void>

    Promise that resolves when the documents are added.

addVectors

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

  • Adds an array of vectors and corresponding documents to the database. The vectors and documents are batch inserted into the database.

    Parameters

    • vectors: number[][]

      Array of vectors to be added to the table.

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

      Array of Document instances corresponding to the vectors.

    Returns Promise<void>

    Promise that resolves when the vectors and documents are added.

asRetriever

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

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

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

      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: Filter

      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<HanaDB>

    • 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 },
    });

checkColumn

  • checkColumn(tableName, columnName, columnType, columnLength?): Promise<void>

  • Checks if the specified column exists in the table and validates its data type and length.

    Parameters

    • tableName: string

      The name of the table.

    • columnName: string

      The name of the column to check.

    • columnType: string | string[]

      The expected data type(s) of the column.

    • OptionalcolumnLength: number

      The expected length of the column. Optional.

    Returns Promise<void>

delete

  • delete(options): Promise<void>

  • Deletes entries from the table based on the provided filter.

    Parameters

    • options: {
          filter?: Filter;
          ids?: string[];
      }
      • Optionalfilter?: Filter
      • Optionalids?: string[]

    Returns Promise<void>

    Throws

    Error if 'ids' parameter is provided, as deletion by ids is not supported.

    Throws

    Error if 'filter' parameter is not provided, as it is required for deletion. to do: adjust the call signature

initialize

maxMarginalRelevanceSearch

  • maxMarginalRelevanceSearch(query, options): Promise<Document<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<Filter>

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

    List of documents selected by maximal marginal relevance.

similaritySearch

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

  • Return docs most similar to query.

    Parameters

    • query: string

      Query text for the similarity search.

    • k: number

      Number of Documents to return. Defaults to 4.

    • Optionalfilter: Filter

      A dictionary of metadata fields and values to filter by. Defaults to None.

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

    Promise that resolves to a list of documents and their corresponding similarity scores.

similaritySearchVectorWithScore

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

  • Return docs most similar to the given embedding.

    Parameters

    • queryEmbedding: number[]
    • k: number

      Number of Documents to return. Defaults to 4.

    • Optionalfilter: Filter

      A dictionary of metadata fields and values to filter by. Defaults to None.

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

    Promise that resolves to a list of documents and their corresponding similarity scores.

similaritySearchWithScore

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

  • Return documents and score values most similar to query.

    Parameters

    • query: string

      Query text for the similarity search.

    • k: number

      Number of Documents to return. Defaults to 4.

    • Optionalfilter: Filter

      A dictionary of metadata fields and values to filter by. Defaults to None.

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

    Promise that resolves to a list of documents and their corresponding similarity scores.

similaritySearchWithScoreAndVectorByVector

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

  • Performs a similarity search based on vector comparison and returns documents along with their similarity scores and vectors.

    Parameters

    • embedding: number[]

      The vector representation of the query for similarity comparison.

    • k: number

      The number of top similar documents to return.

    • Optionalfilter: Filter

      Optional filter criteria to apply to the search query.

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

    A promise that resolves to an array of tuples, each containing a Document, its similarity score, and its vector.

tableExists

toJSON

  • toJSON(): Serialized

  • Returns Serialized

toJSONNotImplemented

StaticfromDocuments

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

  • Creates an instance of HanaDB from an array of Document instances. The documents are added to the database.

    Parameters

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

      List of documents to be converted to vectors.

    • embeddings: EmbeddingsInterface

      Embeddings instance used to convert the documents to vectors.

    • dbConfig: HanaDBArgs

      Configuration for the HanaDB.

    Returns Promise<HanaDB>

    Promise that resolves to an instance of HanaDB.

StaticfromTexts

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

  • Static method to create a HanaDB instance from raw texts. This method embeds the documents, creates a table if it does not exist, and adds the documents to the table.

    Parameters

    • texts: string[]

      Array of text documents to add.

    • metadatas: object | object[]

      metadata for each text document.

    • embeddings: EmbeddingsInterface
    • dbConfig: HanaDBArgs

      Configuration for the HanaDB.

    Returns Promise<HanaDB>

    A Promise that resolves to an instance of HanaDB.

StaticparseFloatArrayFromString

  • parseFloatArrayFromString(arrayAsString): number[]

  • Parses a string representation of a float array and returns an array of numbers.

    Parameters

    • arrayAsString: string

      The string representation of the array.

    Returns number[]

    An array of floats parsed from the string.

StaticsanitizeInt

StaticsanitizeListFloat

  • sanitizeListFloat(embedding): number[]

  • Sanitizes a list to ensure all elements are floats (numbers in TypeScript). Throws an error if any element is not a number.

    Parameters

    • embedding: number[]

      The array of numbers (floats) to be sanitized.

    Returns number[]

    The sanitized array of numbers (floats).

    Throws

    Throws an error if any element is not a number.

StaticsanitizeName

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