hikma-engine
Version:
Code Knowledge Graph Indexer - A sophisticated TypeScript-based indexer that transforms Git repositories into multi-dimensional knowledge stores for AI agents
111 lines (110 loc) • 6.45 kB
JavaScript
;
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.RagCommand = void 0;
const chalk_1 = __importDefault(require("chalk"));
const base_1 = require("./base");
const enhanced_search_service_1 = require("../../modules/enhanced-search-service");
const llm_rag_1 = require("../../modules/llm-rag");
const python_dependency_checker_1 = require("../../utils/python-dependency-checker");
const ui_1 = require("../ui");
class RagCommand extends base_1.BaseCommand {
register() {
return this.program
.command('rag <query> [project-path]')
.description('Generate detailed code explanation with RAG (semantic search + LLM)')
.option('-k, --top-k <number>', 'Number of search results to use', '10')
.option('--dir <path>', 'Project directory (alternative to positional)')
.requiredOption('--provider <provider>', 'Provider override for embeddings and LLM: python|server|local|transformers')
.option('--server-url <url>', 'Server URL (required for server provider)')
.option('-m, --model <name>', 'Model override (deprecated: use --embedding-model and --llm-model instead)')
.option('--embedding-model <name>', 'Embedding model name for search')
.option('--llm-model <name>', 'LLM model name for explanation generation')
.option('--max-tokens <number>', 'Maximum tokens for LLM completion (for OpenAI-compatible providers)', '1500')
.option('--install-python-deps', 'Automatically install Python dependencies if missing')
.action(async (query, projectPath, options) => {
try {
if (!query || query.trim().length === 0) {
throw new Error('RAG query cannot be empty');
}
const topK = parseInt(options.topK);
if (isNaN(topK) || topK < 1 || topK > 100) {
throw new Error('top-k must be a number between 1 and 100');
}
let maxTokens = undefined;
if (options.maxTokens !== undefined) {
const parsed = parseInt(options.maxTokens);
if (isNaN(parsed) || parsed < 256 || parsed > 32768) {
throw new Error('--max-tokens must be a number between 256 and 32768');
}
maxTokens = parsed;
}
// Validate provider-specific requirements
if (options.provider === 'server' && !options.serverUrl) {
throw new Error('Server provider requires --server-url');
}
const resolvedPath = this.resolveProjectRoot(projectPath, options.dir);
const config = this.initConfigAndLogger(resolvedPath);
const globalOpts = {
dir: options.dir,
provider: options.provider,
serverUrl: options.serverUrl,
model: options.model,
embeddingModel: options.embeddingModel,
llmModel: options.llmModel,
installPythonDeps: !!options.installPythonDeps,
};
// Apply explicit CLI configuration
const explicitConfig = this.buildExplicitConfig(globalOpts);
config.updateConfig(explicitConfig);
(0, ui_1.displayCommandHeader)('RAG Explanation', `Query: "${query}" in ${resolvedPath}`);
// Initialize search service
const searchService = new enhanced_search_service_1.EnhancedSearchService(config);
(0, ui_1.displayProgress)('Initializing search service...');
await searchService.initialize();
// Perform semantic search first
const searchOptions = {
limit: topK,
};
(0, ui_1.displayProgress)('Performing semantic search...');
const results = await searchService.semanticSearch(query, searchOptions);
(0, ui_1.displayResults)(results, 'Top Results for RAG');
// If chosen provider is python, ensure deps
const llmProvider = config.getAIConfig().llmProvider.provider;
if (llmProvider === 'python') {
if (globalOpts.installPythonDeps) {
try {
await (0, python_dependency_checker_1.ensurePythonDependencies)(true, true);
}
catch (depError) {
console.log(chalk_1.default.yellow('⚠️ Failed to install Python dependencies automatically:'), depError instanceof Error ? depError.message : String(depError));
console.log(chalk_1.default.blue('💡 Try running: npm run setup-python'));
throw depError;
}
}
else {
const ready = await (0, python_dependency_checker_1.isPythonEnvironmentReady)();
if (!ready) {
console.log(chalk_1.default.yellow('⚠️ Python dependencies are not available for RAG feature'));
console.log(chalk_1.default.blue('💡 Run with --install-python-deps to install automatically, or:'));
console.log(chalk_1.default.cyan(' npm run setup-python'));
throw new Error('Python dependencies required for RAG feature');
}
}
}
const adapted = (0, llm_rag_1.adaptSearchResults)(results);
(0, ui_1.displayProgress)('Generating explanation with LLM...');
const ragResponse = await (0, llm_rag_1.generateRAGExplanation)(query, adapted, { model: options.model, maxTokens });
(0, ui_1.displayRAGExplanation)(query, ragResponse);
// Exit successfully to prevent hanging
await this.exitSuccess();
}
catch (error) {
this.handleError(error, 'RAG failed');
}
});
}
}
exports.RagCommand = RagCommand;