import OpenAI from 'openai';
import { z, ZodTypeAny } from 'zod';
import QueryCache from './query-cache';
import { VectorSearchOptions, ContentSearchResult } from '../supabase/vector-search';
export interface LLMConfig {
    model: string;
    apiKey?: string;
    baseURL?: string;
    headers?: Record<string, string>;
}
export declare abstract class LLMBase {
    protected openai: OpenAI;
    protected defaultModel: string;
    protected cache: QueryCache;
    constructor(config: LLMConfig, openaiInstance?: OpenAI, cache?: QueryCache);
    protected abstract createOpenAIInstance(config: LLMConfig): OpenAI;
    protected getApiKey(): string;
    protected abstract getProviderName(): string;
    protected isTestMode(): boolean;
    get model(): string;
    get client(): OpenAI;
    protected parseModelResponse<T extends ZodTypeAny>(response: any, zodSchema: T): z.infer<T>;
    protected enhanceWithImages(items: any[], responseFormatName: string): Promise<void>;
    fetchStructuredDataFromWeb<T extends ZodTypeAny>({ model, prompt, recommendedSources, zodSchema, userLocation, locationGranularity, systemPrompt, timeline, responseFormatName, customFormat, options }: {
        model?: string;
        prompt: string;
        recommendedSources?: string[];
        zodSchema: T;
        userLocation: any;
        locationGranularity: string;
        systemPrompt?: string;
        timeline?: string;
        responseFormatName?: string;
        customFormat?: (schema: ZodTypeAny, name: string) => any;
        options?: Record<string, any>;
    }): Promise<z.infer<T>>;
    fetchStructuredData<T extends ZodTypeAny>({ model, prompt, html, zodSchema, responseFormatName, }: {
        model?: string;
        prompt: string;
        html: string;
        zodSchema: T;
        responseFormatName?: string;
    }): Promise<z.infer<T>>;
    runQuery({ prompt, categories, systemPrompt, model, area, source, country, region, category, timeline, strategy, options }: {
        prompt?: string;
        categories?: string[];
        systemPrompt?: string;
        model?: string;
        area?: string;
        source?: string;
        country?: string;
        region?: string;
        category?: string;
        timeline?: string;
        strategy?: 'web_search' | 'rag_vector' | 'rag_cache' | 'rag_hybrid' | 'query_cache' | 'vector_only' | 'hybrid_only';
        options?: Record<string, any>;
    }): Promise<any>;
    /**
     * Search content using vector similarity from Supabase
     * This replaces Pinecone functionality with Supabase pgvector
     */
    searchContentVectors(query: string, options?: Partial<VectorSearchOptions>): Promise<ContentSearchResult[]>;
    /**
     * Search content chunks for longer documents
     */
    searchChunks(query: string, limit?: number, threshold?: number): Promise<any[]>;
    /**
     * Hybrid search combining vector and full-text search
     */
    hybridContentSearch(query: string, options?: {
        vectorWeight?: number;
        textWeight?: number;
        limit?: number;
        filters?: VectorSearchOptions['filters'];
    }): Promise<ContentSearchResult[]>;
    /**
     * Generate embedding for a given text
     * Used for custom vector operations
     */
    generateEmbedding(text: string): Promise<number[]>;
    /**
     * Search and format results based on categories
     */
    searchAndFormat(query: string, categories?: string[], area?: string, limit?: number): Promise<any>;
    /**
     * Query with RAG (Retrieval Augmented Generation)
     * Combines vector search context with LLM generation
     */
    queryWithContext({ query, systemPrompt, categories, searchOptions, model, useHybridSearch, contextLimit }: {
        query: string;
        systemPrompt?: string;
        categories?: string[];
        searchOptions?: Partial<VectorSearchOptions>;
        model?: string;
        useHybridSearch?: boolean;
        contextLimit?: number;
    }): Promise<{
        response: string;
        context: ContentSearchResult[];
    }>;
    /**
     * Generate embeddings and search in one call (convenience method)
     */
    semanticSearch(query: string, options?: {
        categories?: string[];
        limit?: number;
        threshold?: number;
        useCache?: boolean;
    }): Promise<ContentSearchResult[]>;
    /**
     * Live web search with LLM processing (separate from RAG)
     * This method is designed to be called independently for real-time web data
     */
    liveWebSearch<T extends ZodTypeAny>({ query, categories, area, region, country, timeline, zodSchema, responseFormatName, model, systemPrompt }: {
        query: string;
        categories?: string[];
        area: string;
        region?: string;
        country?: string;
        timeline?: string;
        zodSchema?: T;
        responseFormatName?: string;
        model?: string;
        systemPrompt?: string;
    }): Promise<{
        data: T extends ZodTypeAny ? z.infer<T> : any;
        source: 'web_search';
        executionTime: number;
        metadata: {
            searchQuery: string;
            area: string;
            categories: string[];
        };
    }>;
    /**
     * Execute waterfall strategy with automatic fallback and rag_level ceiling
     */
    executeWaterfallStrategy({ strategy, prompt, area, region, country, timeline, category, enableFallbacks, maxFallbacks, systemPrompt, options }: {
        strategy: 'query_cache' | 'rag_cache' | 'rag_vector' | 'rag_hybrid' | 'web_search_llm';
        prompt: string;
        area?: string;
        region?: string;
        country?: string;
        timeline?: string;
        category?: string;
        enableFallbacks?: boolean;
        maxFallbacks?: number;
        systemPrompt?: string;
        options?: Record<string, any>;
    }): Promise<{
        success: boolean;
        data: any[];
        source: string;
        strategy: string;
        originalStrategy?: string;
        fallbackUsed?: string;
        fallbackChain: string[];
        timing: {
            primary_ms: number;
            fallback_ms: number;
            total_ms: number;
        };
        meta: {
            original_count: number;
            flyer_count: number;
            total_items: number;
            cache_hit: boolean;
        };
        timestamp: string;
    }>;
    /**
     * Get RAG level ceiling from remote config
     */
    private getRagLevel;
    /**
     * Infer timeline from query text
     */
    private inferTimelineFromQuery;
    /**
     * Enhanced parallel execution using waterfall strategies
     * Returns fastest cache + web search results in parallel
     */
    executeParallelSearch({ prompt, area, region, country, timeline, category, enableFallbacks, systemPrompt }: {
        prompt: string;
        area?: string;
        region?: string;
        country?: string;
        timeline?: string;
        category?: string;
        enableFallbacks?: boolean;
        systemPrompt?: string;
    }): Promise<{
        success: boolean;
        cache: any;
        webSearch: any;
        timing: {
            total_ms: number;
            cache_completed: boolean;
            web_completed: boolean;
        };
        timestamp: string;
    }>;
    /**
     * Legacy parallel execution (kept for backwards compatibility)
     * Returns results as they become available for better UX
     */
    parallelSearch({ query, area, region, country, timeline, categories, zodSchema, includeWebSearch, model }: {
        query: string;
        area: string;
        region?: string;
        country?: string;
        timeline?: string;
        categories?: string[];
        zodSchema?: ZodTypeAny;
        includeWebSearch?: boolean;
        model?: string;
    }): Promise<{
        rag: any;
        webSearch?: any;
        timing: {
            ragTime: number;
            webSearchTime: number;
            totalTime: number;
        };
    }>;
}