/**
 * Tier 2 LLMObs Summarizer
 *
 * Extracts operational metrics from LLMObs spans for Tier 2 export.
 * CRITICAL: This emits metrics WITHOUT any sensitive content.
 *
 * Tier 2 metrics include:
 * - LLM latency by provider/model
 * - Token usage (input, output, cached, reasoning)
 * - Success/failure rates by error category
 * - Cost aggregates
 *
 * EXPLICITLY EXCLUDED:
 * - Prompts, code, tool IO
 * - File paths, stack traces
 * - User identifiers
 */
import type { ReadableSpan } from "@opentelemetry/sdk-trace-base";
/**
 * Error categories aligned with tier2-metrics.v0.1.0.json contract.
 */
export type LLMErrorCategory = "none" | "rate_limit" | "context_length" | "content_filter" | "quota" | "network" | "timeout" | "auth_error" | "api_error" | "validation_error" | "aborted" | "unknown";
/**
 * Finish reason aligned with tier2-metrics.v0.2.0.json contract.
 */
export type LLMFinishReason = "stop" | "length" | "content_filter" | "tool_calls" | "error";
/**
 * Operation name aligned with OTEL gen_ai.operation.name semantic convention.
 * @see https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-metrics/
 */
export type GenAIOperationName = "chat" | "text_completion" | "embeddings" | "execute_tool" | "invoke_agent" | "generate_content";
/**
 * Tool error categories aligned with tier2-metrics.v0.2.0.json contract.
 */
export type ToolErrorCategory = "none" | "timeout" | "execution_error" | "validation_error" | "permission_denied" | "not_found" | "unknown";
/**
 * Provider names aligned with tier2-metrics.v0.1.0.json contract.
 */
export type LLMProvider = "openai" | "anthropic" | "azure" | "bedrock" | "google" | "cohere" | "mistral";
/**
 * Metric labels for LLM requests.
 * These are the ONLY labels allowed per tier2-metrics.v0.1.0.json.
 */
export interface LLMRequestMetricLabels {
    provider: LLMProvider;
    model: string;
    status: "success" | "error";
    error_category?: LLMErrorCategory;
    finish_reason?: LLMFinishReason;
}
/**
 * Token metric labels.
 */
export interface TokenMetricLabels {
    provider: LLMProvider;
    model: string;
    token_type: "input" | "output" | "reasoning" | "cached";
}
/**
 * Tool call metric labels.
 */
export interface ToolCallMetricLabels {
    tool_name: string;
    status: "success" | "error";
}
/**
 * Summary of an LLM span extracted for Tier 2 metrics.
 * Contains NO sensitive data.
 */
export interface LLMSpanSummary {
    provider: LLMProvider;
    /** OTEL canonical provider name (e.g., aws.bedrock, azure.ai.openai) */
    providerOtel: string;
    model: string;
    /** Response model if different from request model */
    responseModel?: string;
    status: "success" | "error";
    durationMs: number;
    inputTokens: number;
    outputTokens: number;
    totalTokens: number;
    reasoningTokens: number;
    cachedTokens: number;
    errorCategory: LLMErrorCategory;
    finishReason?: LLMFinishReason;
    estimatedCost: number;
    /** OTEL operation name (chat, text_completion, etc.) */
    operationName: GenAIOperationName;
    /** Time to first token in milliseconds (streaming only) */
    timeToFirstTokenMs?: number;
}
/**
 * Tool span summary for metrics.
 */
export interface ToolSpanSummary {
    toolName: string;
    status: "success" | "error";
    durationMs: number;
    /** Error category for tool failures (added in v0.2.0) */
    errorCategory: ToolErrorCategory;
}
/**
 * Metrics client interface for emitting Tier 2 metrics.
 * Matches OTEL Metrics API patterns.
 */
export interface Tier2MetricsClient {
    recordHistogram(name: string, value: number, labels: Record<string, string>): void;
    incrementCounter(name: string, value: number, labels: Record<string, string>): void;
}
/**
 * LLMObs Tier 2 Summarizer.
 *
 * Processes Tier 1 LLMObs spans and emits Tier 2 metrics.
 * CRITICAL: Ensures NO prompts, code, tool IO appear in metrics.
 *
 * @example
 * ```typescript
 * const summarizer = new LLMObsTier2Summarizer(metricsClient);
 *
 * // Process each LLMObs span
 * for (const span of llmobsSpans) {
 *   summarizer.summarize(span);
 * }
 * ```
 */
export declare class LLMObsTier2Summarizer {
    private readonly metricsClient;
    private readonly pricing;
    /**
     * Create a Tier 2 summarizer.
     *
     * @param metricsClient - Client for emitting metrics
     * @param customPricing - Optional custom pricing per model
     */
    constructor(metricsClient: Tier2MetricsClient, customPricing?: Map<string, {
        input: number;
        output: number;
    }>);
    /**
     * Process a Tier 1 LLMObs span and emit Tier 2 metrics.
     *
     * @param span - The span to summarize
     */
    summarize(span: ReadableSpan): void;
    /**
     * Extract a summary from an LLM span (for testing/debugging).
     * Contains NO sensitive data.
     *
     * @param span - The span to extract from
     * @returns Summary with safe metrics only
     */
    extractLLMSummary(span: ReadableSpan): LLMSpanSummary;
    /**
     * Extract a summary from a tool span.
     *
     * @param span - The span to extract from
     * @returns Tool summary with safe metrics only
     */
    extractToolSummary(span: ReadableSpan): ToolSpanSummary;
    /**
     * Emit LLM span metrics.
     * Emits BOTH legacy (llm_*) and OTEL-aligned (gen_ai.*) metrics for migration.
     */
    private summarizeLLMSpan;
    /**
     * Emit tool span metrics.
     * Uses OTEL-aligned naming with gen_ai.operation.name = "execute_tool".
     */
    private summarizeToolSpan;
    /**
     * Check if span is an LLM span by name pattern.
     */
    private isLLMSpan;
    /**
     * Check if span is a tool span by name pattern.
     */
    private isToolSpan;
    /**
     * Extract span status.
     */
    private extractStatus;
    /**
     * Categorize error from span attributes.
     * CRITICAL: Does NOT expose error messages or stack traces.
     */
    private categorizeError;
    /**
     * Extract finish reason.
     * Handles gen_ai.response.finish_reasons as array per OTEL semantic conventions.
     */
    private extractFinishReason;
    /**
     * Normalize provider name to allowed values.
     * Defensive: handles non-string inputs gracefully.
     */
    private normalizeProvider;
    /**
     * Normalize provider to OTEL canonical names.
     * Maps legacy names to OTEL gen_ai.provider.name values.
     * @see https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-metrics/
     */
    private normalizeProviderOtel;
    /**
     * Extract operation name from span attributes.
     * Maps span kind and attributes to OTEL gen_ai.operation.name values.
     */
    private extractOperationName;
    /**
     * Extract time to first token from span attributes.
     * Checks multiple attribute names for compatibility.
     * Returns value in MILLISECONDS for consistent internal handling.
     *
     * IMPORTANT: Different attributes use different units:
     * - gen_ai.client.time_to_first_token: SECONDS (OTEL spec)
     * - llm.time_to_first_token_ms: MILLISECONDS
     * - llmobs.metrics.time_to_first_token_ms: MILLISECONDS
     * - timeToFirstTokenMs: MILLISECONDS
     */
    private extractTimeToFirstToken;
    /**
     * Categorize tool errors.
     * CRITICAL: Does NOT expose error messages or stack traces.
     */
    private categorizeToolError;
    /**
     * Validate operation name is in the allowed enum.
     */
    private isValidOperationName;
    /**
     * Validate tool error category is in the allowed enum.
     */
    private isValidToolErrorCategory;
    /**
     * Normalize model name with strict sanitization.
     * SECURITY: Only allows alphanumeric, dots, hyphens to prevent data injection.
     * Attacker-controlled model names could otherwise leak sensitive data to Tier 2.
     */
    private normalizeModel;
    /**
     * Normalize tool name with strict sanitization.
     * SECURITY: Only allows alphanumeric, underscores, hyphens to prevent data injection.
     * Attacker-controlled tool names could otherwise leak sensitive data to Tier 2.
     */
    private normalizeToolName;
    /**
     * Get span duration in milliseconds, clamped to non-negative.
     * Handles clock skew or corrupted data gracefully.
     */
    private getDurationMs;
    /**
     * Validate error category is in the allowed enum.
     */
    private isValidErrorCategory;
    /**
     * Validate finish reason is in the allowed enum.
     */
    private isValidFinishReason;
    /**
     * Estimate cost based on model and token counts.
     * Uses rough estimates - actual costs should be calculated server-side.
     */
    private estimateCost;
    /**
     * Get default pricing per 1000 tokens (in USD).
     */
    private getDefaultPricing;
}
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