Class HanaDB

constructor

• new HanaDB(embeddings, args): HanaDB

• Parameters

  • embeddings: EmbeddingsInterface

  • args: HanaDBArgs

  Returns HanaDB

FilterType

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.

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.

  • Optional columnLength: 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[];
    }

    • Optional filter?: Filter

    • Optional ids?: 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

• initialize(): Promise<void>

• Returns Promise<void>

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.

  • Optional filter: 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.

  • Optional filter: 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.

  • Optional filter: 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.

  • Optional filter: 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

• tableExists(tableName): Promise<boolean>

• Parameters

  • tableName: string

  Returns Promise<boolean>

Static fromDocuments

• 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.

Static fromTexts

• 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.

Static parseFloatArrayFromString

• 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.

Static sanitizeInt

• sanitizeInt(inputInt): number

• Sanitizes the input to integer. Throws an error if the value is less than -1.

  Parameters

  • inputInt: any

    The input to be sanitized.

  Returns number

  The sanitized integer.

Static sanitizeListFloat

• 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.

Static sanitizeName

• sanitizeName(inputStr): string

• Sanitizes the input string by removing characters that are not alphanumeric or underscores.

  Parameters

  • inputStr: string

    The string to be sanitized.

  Returns string

  The sanitized string.