@langchain/community/dist/vectorstores/azure_aisearch.d.ts.map

Third-party integrations for LangChain.js

{"version":3,"file":"azure_aisearch.d.ts","names":["SearchClient","SearchIndexClient","SearchIndex","VectorFilterMode","KeyCredential","TokenCredential","MaxMarginalRelevanceSearchOptions","VectorStore","EmbeddingsInterface","Document","AzureAISearchQueryType","AzureAISearchQueryOptions","AzureAISearchConfig","AzureAISearchDocument","AzureAISearchDocumentMetadata","Array","AzureAISearchAddDocumentsOptions","AzureAISearchFilterType","AzureAISearchVectorStore","Promise"],"sources":["../../src/vectorstores/azure_aisearch.d.ts"],"sourcesContent":["import { SearchClient, SearchIndexClient, SearchIndex, VectorFilterMode } from \"@azure/search-documents\";\nimport type { KeyCredential, TokenCredential } from \"@azure/core-auth\";\nimport { MaxMarginalRelevanceSearchOptions, VectorStore } from \"@langchain/core/vectorstores\";\nimport type { EmbeddingsInterface } from \"@langchain/core/embeddings\";\nimport { Document } from \"@langchain/core/documents\";\n/**\n * Azure AI Search query type.\n */\nexport declare const AzureAISearchQueryType: {\n    /** Vector search. */\n    readonly Similarity: \"similarity\";\n    /** Hybrid full text and vector search. */\n    readonly SimilarityHybrid: \"similarity_hybrid\";\n    /** Hybrid full text and vector search with semantic ranking. */\n    readonly SemanticHybrid: \"semantic_hybrid\";\n};\n/**\n * Azure AI Search query type.\n */\nexport type AzureAISearchQueryType = (typeof AzureAISearchQueryType)[keyof typeof AzureAISearchQueryType];\n/**\n * Azure AI Search settings.\n */\nexport interface AzureAISearchQueryOptions {\n    readonly type?: AzureAISearchQueryType;\n    readonly semanticConfigurationName?: string;\n}\n/**\n * Configuration options for the `AzureAISearchStore` constructor.\n */\nexport interface AzureAISearchConfig {\n    readonly client?: SearchClient<AzureAISearchDocument>;\n    readonly indexName?: string;\n    readonly endpoint?: string;\n    readonly key?: string;\n    readonly credentials?: KeyCredential | TokenCredential;\n    readonly search?: AzureAISearchQueryOptions;\n}\n/**\n * Azure AI Search options metadata schema.\n * If yout want to add custom data, use the attributes property.\n */\nexport type AzureAISearchDocumentMetadata = {\n    source: string;\n    attributes?: Array<{\n        key: string;\n        value: string;\n    }>;\n    embedding?: number[];\n};\n/**\n * Azure AI Search indexed document.\n */\nexport type AzureAISearchDocument = {\n    id: string;\n    content: string;\n    content_vector: number[];\n    metadata: AzureAISearchDocumentMetadata;\n};\n/**\n * Azure AI Search options for adding documents.\n */\nexport type AzureAISearchAddDocumentsOptions = {\n    ids?: string[];\n};\n/**\n * Azure AI Search filter type.\n */\nexport type AzureAISearchFilterType = {\n    /** OData filter. */\n    filterExpression?: string;\n    /** Determines whether or not filters are applied before or after the vector search is performed. */\n    vectorFilterMode?: VectorFilterMode;\n    /** Determines whether or not to include the embeddings in the search results. */\n    includeEmbeddings?: boolean;\n};\n/**\n * Azure AI Search vector store.\n * To use this, you should have:\n * - the `@azure/search-documents` NPM package installed\n * - an endpoint and key to the Azure AI Search instance\n *\n * If you directly provide a `SearchClient` instance, you need to ensure that\n * an index has been created. When using and endpoint and key, the index will\n * be created automatically if it does not exist.\n */\nexport declare class AzureAISearchVectorStore extends VectorStore {\n    FilterType: AzureAISearchFilterType;\n    get lc_secrets(): {\n        [key: string]: string;\n    };\n    _vectorstoreType(): string;\n    private readonly initPromise;\n    private readonly client;\n    private readonly indexName;\n    private readonly options;\n    constructor(embeddings: EmbeddingsInterface, config: AzureAISearchConfig);\n    /**\n     * Removes specified documents from the AzureAISearchVectorStore using IDs or a filter.\n     * @param params Object that includes either an array of IDs or a filter for the data to be deleted.\n     * @returns A promise that resolves when the documents have been removed.\n     */\n    delete(params: {\n        ids?: string | string[];\n        filter?: AzureAISearchFilterType;\n    }): Promise<void>;\n    private deleteMany;\n    private deleteById;\n    /**\n     * Adds documents to the AzureAISearchVectorStore.\n     * @param documents The documents to add.\n     * @param options Options for adding documents.\n     * @returns A promise that resolves to the ids of the added documents.\n     */\n    addDocuments(documents: Document[], options?: AzureAISearchAddDocumentsOptions): Promise<string[]>;\n    /**\n     * Adds vectors to the AzureAISearchVectorStore.\n     * @param vectors Vectors to be added.\n     * @param documents Corresponding documents to be added.\n     * @param options Options for adding documents.\n     * @returns A promise that resolves to the ids of the added documents.\n     */\n    addVectors(vectors: number[][], documents: Document[], options?: AzureAISearchAddDocumentsOptions): Promise<string[]>;\n    /**\n     * Performs a similarity search using query type specified in configuration.\n     * If the query type is not specified, it defaults to similarity search.\n     * @param query Query text for the similarity search.\n     * @param k=4 Number of nearest neighbors to return.\n     * @param filter Optional filter options for the documents.\n     * @returns Promise that resolves to a list of documents.\n     */\n    similaritySearch(query: string, k?: number, filter?: this[\"FilterType\"] | undefined): Promise<Document[]>;\n    /**\n     * Performs a similarity search using query type specified in configuration.\n     * If the query type is not specified, it defaults to similarity hybrid search.\n     * @param query Query text for the similarity search.\n     * @param k=4 Number of nearest neighbors to return.\n     * @param filter Optional filter options for the documents.\n     * @returns Promise that resolves to a list of documents and their corresponding similarity scores.\n     */\n    similaritySearchWithScore(query: string, k?: number, filter?: this[\"FilterType\"] | undefined): Promise<[Document, number][]>;\n    /**\n     * Performs a hybrid search using query text.\n     * @param query Query text for the similarity search.\n     * @param queryVector Query vector for the similarity search.\n     *    If not provided, the query text will be embedded.\n     * @param k=4 Number of nearest neighbors to return.\n     * @param filter Optional filter options for the documents.\n     * @returns Promise that resolves to a list of documents and their corresponding similarity scores.\n     */\n    hybridSearchVectorWithScore(query: string, queryVector?: number[], k?: number, filter?: this[\"FilterType\"] | undefined): Promise<[Document, number][]>;\n    /**\n     * Performs a hybrid search with semantic reranker using query text.\n     * @param query Query text for the similarity search.\n     * @param queryVector Query vector for the similarity search.\n     *    If not provided, the query text will be embedded.\n     * @param k=4 Number of nearest neighbors to return.\n     * @param filter Optional filter options for the documents.\n     * @returns Promise that resolves to a list of documents and their corresponding similarity scores.\n     */\n    semanticHybridSearchVectorWithScore(query: string, queryVector?: number[], k?: number, filter?: this[\"FilterType\"] | undefined): Promise<[Document, number][]>;\n    /**\n     * Performs a similarity search on the vectors stored in the collection.\n     * @param queryVector Query vector for the similarity search.\n     * @param k=4 Number of nearest neighbors to return.\n     * @param filter Optional filter options for the documents.\n     * @returns Promise that resolves to a list of documents and their corresponding similarity scores.\n     */\n    similaritySearchVectorWithScore(query: number[], k: number, filter?: this[\"FilterType\"]): Promise<[Document, number][]>;\n    /**\n     * Return documents selected using the maximal marginal relevance.\n     * Maximal marginal relevance optimizes for similarity to the query AND\n     * diversity among selected documents.\n     * @param query Text to look up documents similar to.\n     * @param options.k Number of documents to return.\n     * @param options.fetchK=20 Number of documents to fetch before passing to\n     *     the MMR algorithm.\n     * @param options.lambda=0.5 Number between 0 and 1 that determines the\n     *     degree of diversity among the results, where 0 corresponds to maximum\n     *     diversity and 1 to minimum diversity.\n     * @returns List of documents selected by maximal marginal relevance.\n     */\n    maxMarginalRelevanceSearch(query: string, options: MaxMarginalRelevanceSearchOptions<this[\"FilterType\"]>): Promise<Document[]>;\n    /**\n     * Ensures that an index exists on the AzureAISearchVectorStore.\n     * @param indexClient The Azure AI Search index client.\n     * @returns A promise that resolves when the AzureAISearchVectorStore index has been initialized.\n     * @protected\n     */\n    protected ensureIndexExists(indexClient: SearchIndexClient): Promise<void>;\n    /**\n     * Prepares the search index definition for Azure AI Search.\n     * @param indexName The name of the index.\n     * @returns The SearchIndex object.\n     * @protected\n     */\n    protected createSearchIndexDefinition(indexName: string): Promise<SearchIndex>;\n    /**\n     * Static method to create an instance of AzureAISearchVectorStore from a\n     * list of texts. It first converts the texts to vectors and then adds\n     * them to the collection.\n     * @param texts List of texts to be converted to vectors.\n     * @param metadatas Metadata for the texts.\n     * @param embeddings Embeddings to be used for conversion.\n     * @param config Database configuration for Azure AI Search.\n     * @returns Promise that resolves to a new instance of AzureAISearchVectorStore.\n     */\n    static fromTexts(texts: string[], metadatas: object[] | object, embeddings: EmbeddingsInterface, config: AzureAISearchConfig): Promise<AzureAISearchVectorStore>;\n    /**\n     * Static method to create an instance of AzureAISearchVectorStore from a\n     * list of documents. It first converts the documents to vectors and then\n     * adds them to the database.\n     * @param docs List of documents to be converted to vectors.\n     * @param embeddings Embeddings to be used for conversion.\n     * @param config Database configuration for Azure AI Search.\n     * @returns Promise that resolves to a new instance of AzureAISearchVectorStore.\n     */\n    static fromDocuments(docs: Document[], embeddings: EmbeddingsInterface, config: AzureAISearchConfig, options?: AzureAISearchAddDocumentsOptions): Promise<AzureAISearchVectorStore>;\n}\n//# sourceMappingURL=azure_aisearch.d.ts.map"],"mappings":";;;;;;;;;;AAQA;AAWYU,cAXSA,sBAWwBA,EAAAA;EAI5BC;EAOAC,SAAAA,UAAAA,EAAAA,YAAmB;EACDC;EAAbb,SAAAA,gBAAAA,EAAAA,mBAAAA;EAIKI;EAAgBC,SAAAA,cAAAA,EAAAA,iBAAAA;CACrBM;AAAyB;AAM/C;AAWA;AASYK,KA3CAN,sBAAAA,GA2CAM,CAAAA,OA3CiCN,sBA2CD,CAAA,CAAA,MAAA,OA3CsCA,sBA2CtC,CAAA;AAM5C;AAkBA;;AAU4BF,UAzEXG,yBAAAA,CAyEWH;EAA6BI,SAAAA,IAAAA,CAAAA,EAxErCF,sBAwEqCE;EAQxCK,SAAAA,yBAAAA,CAAAA,EAAAA,MAAAA;;;;;AAkB8BR,UA5F9BG,mBAAAA,CA4F8BH;EAAsBO,SAAAA,MAAAA,CAAAA,EA3F/ChB,YA2F+CgB,CA3FlCH,qBA2FkCG,CAAAA;EAAmCG,SAAAA,SAAAA,CAAAA,EAAAA,MAAAA;EASNV,SAAAA,QAAAA,CAAAA,EAAAA,MAAAA;EAARU,SAAAA,GAAAA,CAAAA,EAAAA,MAAAA;EASkBV,SAAAA,WAAAA,CAAAA,EAzGjFL,aAyGiFK,GAzGjEJ,eAyGiEI;EAATU,SAAAA,MAAAA,CAAAA,EAxG7ER,yBAwG6EQ;;;;;;AA4BLA,KA9HlFL,6BAAAA,GA8HkFK;EAcvCb,MAAAA,EAAAA,MAAAA;EAAgEG,UAAAA,CAAAA,EA1ItGM,KA0IsGN,CAAAA;IAARU,GAAAA,EAAAA,MAAAA;IAOlElB,KAAAA,EAAAA,MAAAA;EAAoBkB,CAAAA,CAAAA;EAOKjB,SAAAA,CAAAA,EAAAA,MAAAA,EAAAA;CAARiB;;;;AAWqEA,KA1JvHN,qBAAAA,GA0JuHM;EAUpGV,EAAAA,EAAAA,MAAAA;EAAwBD,OAAAA,EAAAA,MAAAA;EAA6BI,cAAAA,EAAAA,MAAAA,EAAAA;EAA+BI,QAAAA,EAhKrGF,6BAgKqGE;CAA2CE;;;AAnI7F;KAxBrDF,gCAAAA;;;;;;KAMAC,uBAAAA;;;;qBAIWd;;;;;;;;;;;;;;cAcFe,wBAAAA,SAAiCX,WAAAA;cACtCU;;;;;;;;;0BASYT,6BAA6BI;;;;;;;;aAQxCK;MACTE;;;;;;;;;0BASoBV,sBAAsBO,mCAAmCG;;;;;;;;6CAQtCV,sBAAsBO,mCAAmCG;;;;;;;;;wFASdA,QAAQV;;;;;;;;;iGASCU,SAASV;;;;;;;;;;2HAUiBU,SAASV;;;;;;;;;;mIAUDU,SAASV;;;;;;;;4FAQhDU,SAASV;;;;;;;;;;;;;;qDAchDH,wDAAwDa,QAAQV;;;;;;;2CAO1ER,oBAAoBkB;;;;;;;4DAOHA,QAAQjB;;;;;;;;;;;8EAWUM,6BAA6BI,sBAAsBO,QAAQD;;;;;;;;;;6BAU5GT,wBAAwBD,6BAA6BI,+BAA+BI,mCAAmCG,QAAQD"}