{
"event": "PreToolUse",
"tool_name": "Bash",
"tool_input": {
"command": "head -60 src\/Controller\/ChatController.php src\/UseCases\/Chat\/SendMessageUseCase.php src\/Infrastructure\/AI\/ChatService.php 2>\/dev\/null | head -150",
"description": "Analyze dependency patterns in real files"
}
}
{
"tool_response": {
"stdout": "==> src\/Controller\/ChatController.php <==\n<?php\n\ndeclare(strict_types=1);\n\nnamespace Controller;\n\n\/\/ @responsibility: HTTP-Endpunkte für KI-Chat (Sessions, Nachrichten, Export)\n\nuse Framework\\Controller;\nuse Infrastructure\\AI\\ModelConfig;\nuse Infrastructure\\Formatting\\ChatMessageFormatter;\nuse UseCases\\Chat\\ExportChatSessionUseCase;\nuse UseCases\\Chat\\ManageChatSessionsUseCase;\nuse UseCases\\Chat\\SendChatMessageUseCase;\n\nclass ChatController extends Controller\n{\n private ManageChatSessionsUseCase $sessionsUseCase;\n private SendChatMessageUseCase $messageUseCase;\n private ChatMessageFormatter $formatter;\n private ExportChatSessionUseCase $exportUseCase;\n\n public function __construct(\n ManageChatSessionsUseCase $sessionsUseCase,\n SendChatMessageUseCase $messageUseCase,\n ChatMessageFormatter $formatter,\n ExportChatSessionUseCase $exportUseCase\n ) {\n $this->sessionsUseCase = $sessionsUseCase;\n $this->messageUseCase = $messageUseCase;\n $this->formatter = $formatter;\n $this->exportUseCase = $exportUseCase;\n }\n\n public function index(): void\n {\n $uuid = $this->sessionsUseCase->createSession();\n header('Location: \/chat\/' . $uuid);\n exit;\n }\n\n public function show(string $uuid): void\n {\n $session = $this->sessionsUseCase->getSession($uuid);\n\n if ($session === null) {\n header('Location: \/chat');\n exit;\n }\n\n $this->view('chat.index', [\n 'title' => $session['title'] ?? 'KI-Chat',\n 'session' => $session,\n 'messages' => $this->sessionsUseCase->getMessages($session['id']),\n 'sessions' => $this->sessionsUseCase->getAllSessions(),\n 'authorProfiles' => $this->sessionsUseCase->getAuthorProfiles(),\n 'systemPrompts' => $this->sessionsUseCase->getSystemPrompts(),\n 'outputStructures' => $this->sessionsUseCase->getOutputStructures(),\n 'collections' => $this->sessionsUseCase->getAvailableCollections(),\n 'models' => ModelConfig::getAll(),\n\n==> src\/Infrastructure\/AI\/ChatService.php <==\n<?php\n\ndeclare(strict_types=1);\n\nnamespace Infrastructure\\AI;\n\n\/\/ @responsibility: RAG-Pipeline für KI-Chat (Embedding, Suche, Antwort-Generierung)\n\nuse RuntimeException;\n\nfinal readonly class ChatService\n{\n \/**\n * Constructs a new ChatService instance.\n *\n * @param OllamaService $ollama Ollama service for embeddings and optional LLM\n * @param QdrantService $qdrant Qdrant service for vector search\n * @param ClaudeService $claude Claude service for high-quality LLM responses\n *\/\n public function __construct(\n private OllamaService $ollama,\n private QdrantService $qdrant,\n private ClaudeService $claude\n ) {\n }\n\n \/**\n * Executes a complete RAG chat pipeline.\n *\n * Performs the following steps:\n * 1. Generates an embedding vector for the question (if collections selected)\n * 2. Searches for similar documents in the vector database(s)\n * 3. Builds context from the most relevant chunks\n * 4. Generates an answer using the specified LLM model\n * 5. Extracts source information\n * 6. Assembles a structured response\n *\n * If no collections are selected, steps 1-3 and 5 are skipped (no RAG).\n *\n * @param string $question The user's question to answer\n * @param string $model The LLM model (claude-* or ollama:*)\n * @param array<string> $collections Qdrant collections to search (empty = no RAG)\n * @param int $limit Maximum number of document chunks to retrieve (default: 5)\n * @param string|null $stylePrompt Optional style prompt from author profile\n * @param string|null $customSystemPrompt Optional custom system prompt (replaces default if set)\n * @param float $temperature Sampling temperature 0.0-1.0 (default: 0.7)\n * @param int $maxTokens Maximum tokens in response (default: 4096)\n *\n * @return array{\n * question: string,\n * answer: string,\n * sources: array<int, array{title: string, score: float, content?: string}>,\n * model: string,\n * usage?: array{input_tokens: int, output_tokens: int},\n * chunks_used: int\n * } Complete chat response with answer, sources, and metadata\n *\n * @throws RuntimeException If embedding generation fails\n * @throws RuntimeException If vector search fails\n * @throws RuntimeException If LLM request fails",
"stderr": "",
"interrupted": false,
"isImage": false
}
}