import type { PodChatKitStore } from '../chatkit/pod-store';
import type { AuthContext } from '../auth/AuthContext';
import type { EmbeddingService } from '../../ai/service/EmbeddingService';
/**
 * Vector upsert request
 */
export interface VectorUpsertRequest {
    /** Embedding model name */
    model: string;
    /** Vectors to upsert */
    vectors: Array<{
        /** Resource URI */
        subject: string;
        /** Semantic aspect (type, chunk, property) */
        aspect: string;
        /** Vector data */
        vector: number[];
    }>;
}
/**
 * Vector search request
 */
export interface VectorSearchRequest {
    /** Embedding model name */
    model: string;
    /** Query text (will be converted to vector) */
    query?: string;
    /** Pre-computed query vector */
    vector?: number[];
    /** Max results */
    limit?: number;
    /** Similarity threshold (0-1) */
    threshold?: number;
    /** Filter conditions */
    filter?: {
        subject?: {
            $eq?: string;
            $startsWith?: string;
            $in?: string[];
        };
        aspect?: {
            $eq?: string;
            $startsWith?: string;
            $in?: string[];
        };
    };
    /** Deduplicate by subject */
    distinctSubject?: boolean;
}
/**
 * Vector search result
 */
export interface VectorSearchResult {
    subject: string;
    aspect: string;
    score: number;
    distance: number;
}
/**
 * Vector delete request
 */
export interface VectorDeleteRequest {
    /** Embedding model name */
    model: string;
    /** Filter conditions (at least one field required) */
    filter: {
        subject?: {
            $eq?: string;
            $startsWith?: string;
        };
        aspect?: {
            $eq?: string;
            $startsWith?: string;
        };
    };
}
/**
 * Vector status response
 */
export interface VectorStatusResponse {
    byModel: Array<{
        model: string;
        count: number;
    }>;
    totalCount: number;
}
export interface VectorServiceOptions {
    /** CSS base URL for vector API */
    cssBaseUrl: string;
    /** Pod store for getting AI credentials */
    store: PodChatKitStore;
    /** Embedding service for generating vectors */
    embeddingService: EmbeddingService;
}
/**
 * VectorService - API Server 的向量服务
 *
 * 负责：
 * 1. 从 Pod 读取 AI 凭据
 * 2. 调用 EmbeddingService 生成向量
 * 3. 调用 CSS 的 /-/vector/* 端点存储/搜索向量
 */
export declare class VectorService {
    private readonly logger;
    private readonly cssBaseUrl;
    private readonly store;
    private readonly embeddingService;
    constructor(options: VectorServiceOptions);
    /**
     * Upsert vectors to CSS vector store
     */
    upsert(request: VectorUpsertRequest, auth: AuthContext, accessToken: string): Promise<{
        upserted: number;
        errors: string[];
        took_ms: number;
    }>;
    /**
     * Search vectors in CSS vector store
     */
    search(request: VectorSearchRequest, auth: AuthContext, accessToken: string): Promise<{
        results: VectorSearchResult[];
        model: string;
        took_ms: number;
    }>;
    /**
     * Delete vectors from CSS vector store
     */
    delete(request: VectorDeleteRequest, auth: AuthContext, accessToken: string): Promise<{
        deleted: number;
        errors: string[];
        took_ms: number;
    }>;
    /**
     * Get vector store status
     */
    status(accessToken: string): Promise<VectorStatusResponse>;
    /**
     * Generate embedding for text
     */
    embed(text: string, model: string, auth: AuthContext): Promise<number[]>;
    /**
     * Generate embeddings for multiple texts
     */
    embedBatch(texts: string[], model: string, auth: AuthContext): Promise<number[][]>;
    /**
     * Create a StoreContext from AuthContext for Pod operations
     */
    private createStoreContext;
    private getAiCredential;
    private callCssVectorApi;
    /**
     * Hash subject + aspect to numeric ID
     * Uses FNV-1a hash for good distribution
     */
    private hashSubjectAspect;
}