{"version":3,"file":"neo4j_vector.d.ts","names":["EmbeddingsInterface","VectorStore","Document","Any","SearchType","IndexType","DistanceStrategy","Metadata","Record","Neo4jVectorStoreArgs","Neo4jVectorStore","Promise","url","username","password","database","Array"],"sources":["../../src/vectorstores/neo4j_vector.d.ts"],"sourcesContent":["import type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport { VectorStore } from \"@langchain/core/vectorstores\";\nimport { Document } from \"@langchain/core/documents\";\ntype Any = any;\nexport type SearchType = \"vector\" | \"hybrid\";\nexport type IndexType = \"NODE\" | \"RELATIONSHIP\";\nexport type DistanceStrategy = \"euclidean\" | \"cosine\";\nexport type Metadata = Record<string, unknown>;\ninterface Neo4jVectorStoreArgs {\n    url: string;\n    username: string;\n    password: string;\n    database?: string;\n    preDeleteCollection?: boolean;\n    textNodeProperty?: string;\n    textNodeProperties?: string[];\n    embeddingNodeProperty?: string;\n    keywordIndexName?: string;\n    indexName?: string;\n    searchType?: SearchType;\n    indexType?: IndexType;\n    retrievalQuery?: string;\n    nodeLabel?: string;\n    createIdIndex?: boolean;\n}\n/**\n * @security *Security note*: Make sure that the database connection uses credentials\n * that are narrowly-scoped to only include necessary permissions.\n * Failure to do so may result in data corruption or loss, since the calling\n * code may attempt commands that would result in deletion, mutation\n * of data if appropriately prompted or reading sensitive data if such\n * data is present in the database.\n * The best way to guard against such negative outcomes is to (as appropriate)\n * limit the permissions granted to the credentials used with this tool.\n * For example, creating read only users for the database is a good way to\n * ensure that the calling code cannot mutate or delete data.\n *\n * @link See https://js.langchain.com/docs/security for more information.\n */\nexport declare class Neo4jVectorStore extends VectorStore {\n    private driver;\n    private database;\n    private preDeleteCollection;\n    private nodeLabel;\n    private embeddingNodeProperty;\n    private embeddingDimension;\n    private textNodeProperty;\n    private keywordIndexName;\n    private indexName;\n    private retrievalQuery;\n    private searchType;\n    private indexType;\n    private distanceStrategy;\n    private supportMetadataFilter;\n    private isEnterprise;\n    _vectorstoreType(): string;\n    constructor(embeddings: EmbeddingsInterface, config: Neo4jVectorStoreArgs);\n    static initialize(embeddings: EmbeddingsInterface, config: Neo4jVectorStoreArgs): Promise<Neo4jVectorStore>;\n    _initializeDriver({ url, username, password, database }: Neo4jVectorStoreArgs): Promise<void>;\n    _verifyConnectivity(): Promise<void>;\n    _verifyVersion(): Promise<void>;\n    close(): Promise<void>;\n    _dropIndex(): Promise<void>;\n    query(query: string, params?: Any): Promise<Any[]>;\n    static fromTexts(texts: string[], metadatas: Any, embeddings: EmbeddingsInterface, config: Neo4jVectorStoreArgs): Promise<Neo4jVectorStore>;\n    static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, config: Neo4jVectorStoreArgs): Promise<Neo4jVectorStore>;\n    static fromExistingIndex(embeddings: EmbeddingsInterface, config: Neo4jVectorStoreArgs): Promise<Neo4jVectorStore>;\n    static fromExistingGraph(embeddings: EmbeddingsInterface, config: Neo4jVectorStoreArgs): Promise<Neo4jVectorStore>;\n    createNewIndex(): Promise<void>;\n    retrieveExistingIndex(): Promise<number | null>;\n    retrieveExistingFtsIndex(textNodeProperties?: string[]): Promise<string | null>;\n    createNewKeywordIndex(textNodeProperties?: string[]): Promise<void>;\n    sortByIndexName(values: Array<{\n        [key: string]: Any;\n    }>, indexName: string): Array<{\n        [key: string]: Any;\n    }>;\n    addVectors(vectors: number[][], documents: Document[], metadatas?: Record<string, Any>[], ids?: string[]): Promise<string[]>;\n    addDocuments(documents: Document[]): Promise<string[]>;\n    similaritySearch(query: string, k?: number, params?: Record<string, Any>): Promise<Document[]>;\n    similaritySearchWithScore(query: string, k?: number, params?: Record<string, Any>): Promise<[Document, number][]>;\n    similaritySearchVectorWithScore(vector: number[], k: number, query: string, params?: Record<string, Any>): Promise<[Document, number][]>;\n}\nexport {};\n//# 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