llmverify/dist/adapters/providers/groq.js

AI Output Verification Toolkit — Local-first LLM safety, hallucination detection, PII redaction, prompt injection defense, and runtime monitoring. Zero telemetry. OWASP LLM Top 10 aligned.

"use strict";
/**
 * Groq Adapter
 *
 * Adapter for Groq API (fast inference for Llama, Mixtral, etc.)
 *
 * @module adapters/providers/groq
 * @author Haiec
 * @license MIT
 */
Object.defineProperty(exports, "__esModule", { value: true });
exports.buildGroqAdapter = buildGroqAdapter;
const types_1 = require("../types");
/**
 * Builds a Groq adapter.
 *
 * @param config - Adapter configuration
 * @returns LLM client for Groq
 *
 * @example
 * import Groq from 'groq-sdk';
 * const groq = new Groq({ apiKey: process.env.GROQ_API_KEY });
 * const llm = buildGroqAdapter({ provider: 'groq', client: groq });
 */
function buildGroqAdapter(config) {
    if (!config.client && !config.apiKey) {
        throw new types_1.AdapterConfigError('groq', 'Groq adapter requires either a client instance or apiKey. ' +
            'Install groq-sdk: npm install groq-sdk');
    }
    const client = config.client;
    const defaultModel = config.defaultModel ?? 'llama-3.1-70b-versatile';
    return {
        provider: 'groq',
        providerName: 'Groq',
        async generate(request) {
            if (!client) {
                throw new types_1.AdapterConfigError('groq', 'Client not initialized');
            }
            const model = request.model || defaultModel;
            const messages = [];
            if (request.system) {
                messages.push({ role: 'system', content: request.system });
            }
            messages.push({ role: 'user', content: request.prompt });
            const response = await client.chat.completions.create({
                model,
                messages,
                temperature: request.temperature,
                max_tokens: request.maxTokens,
                stop: request.stop
            });
            const choice = response.choices[0];
            const text = choice?.message?.content?.toString() ?? '';
            const tokens = response.usage?.completion_tokens ?? estimateTokens(text);
            return {
                text,
                tokens,
                totalTokens: response.usage?.total_tokens,
                model: response.model ?? model,
                finishReason: normalizeFinishReason(choice?.finish_reason),
                raw: response
            };
        }
    };
}
function estimateTokens(text) {
    return Math.ceil(text.split(/\s+/).length * 1.3);
}
function normalizeFinishReason(reason) {
    if (!reason)
        return undefined;
    const map = {
        'stop': 'stop',
        'length': 'length'
    };
    return map[reason] ?? reason;
}
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