Protokoll #1511

{
    "event": "PreToolUse",
    "tool_name": "Edit",
    "tool_input": {
        "file_path": "\/var\/www\/dev.campus.systemische-tools.de\/src\/Infrastructure\/AI\/ChatService.php",
        "old_string": "    \/**\n     * Executes a complete RAG chat pipeline.\n     *\n     * Performs the following steps:\n     * 1. Generates an embedding vector for the question\n     * 2. Searches for similar documents in the vector database\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     * @param string      $question           The user's question to answer\n     * @param string      $model              The LLM model (claude-* or ollama:*)\n     * @param string      $collection         The Qdrant collection to search in (default: documents)\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 no relevant documents are found\n     * @throws RuntimeException If LLM request fails\n     *\n     * @example\n     * $chat = new ChatService($ollama, $qdrant, $claude);\n     * $result = $chat->chat('Was ist systemisches Coaching?', 'claude-opus-4-5-20251101', 'documents', 5, null, null, 0.7, 4096);\n     * \/\/ Returns: [\n     * \/\/   'question' => 'Was ist systemisches Coaching?',\n     * \/\/   'answer' => 'Systemisches Coaching ist...',\n     * \/\/   'sources' => [\n     * \/\/     ['title' => 'Coaching Grundlagen', 'score' => 0.89],\n     * \/\/     ['title' => 'Systemische Methoden', 'score' => 0.76]\n     * \/\/   ],\n     * \/\/   'model' => 'claude-opus-4-5-20251101',\n     * \/\/   'usage' => ['input_tokens' => 234, 'output_tokens' => 567],\n     * \/\/   'chunks_used' => 5\n     * \/\/ ]\n     *\/\n    public function chat(\n        string $question,\n        string $model = 'claude-opus-4-5-20251101',\n        string $collection = 'documents',\n        int $limit = 5,\n        ?string $stylePrompt = null,\n        ?string $customSystemPrompt = null,\n        float $temperature = 0.7,\n        int $maxTokens = 4096\n    ): array {\n        \/\/ Step 1: Generate embedding for the question\n        try {\n            $queryEmbedding = $this->ollama->getEmbedding($question);\n        } catch (RuntimeException $e) {\n            throw new RuntimeException(\n                'Embedding generation failed: ' . $e->getMessage(),\n                0,\n                $e\n            );\n        }\n\n        if ($queryEmbedding === []) {\n            throw new RuntimeException('Embedding generation returned empty vector');\n        }\n\n        \/\/ Step 2: Search for relevant document chunks\n        try {\n            $searchResults = $this->qdrant->search($queryEmbedding, $collection, $limit);\n        } catch (RuntimeException $e) {\n            throw new RuntimeException(\n                'Vector search failed: ' . $e->getMessage(),\n                0,\n                $e\n            );\n        }\n\n        if ($searchResults === []) {\n            throw new RuntimeException('No relevant documents found for the question');\n        }\n\n        \/\/ Step 3: Build context from search results\n        $context = $this->buildContext($searchResults);",
        "new_string": "    \/**\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\n     *\n     * @example\n     * $chat = new ChatService($ollama, $qdrant, $claude);\n     * \/\/ With RAG (multiple collections)\n     * $result = $chat->chat('Was ist systemisches Coaching?', 'claude-opus-4-5-20251101', ['documents', 'mail'], 5);\n     * \/\/ Without RAG (no collections)\n     * $result = $chat->chat('Erkläre mir Python', 'claude-opus-4-5-20251101', [], 5);\n     *\/\n    public function chat(\n        string $question,\n        string $model = 'claude-opus-4-5-20251101',\n        array $collections = [],\n        int $limit = 5,\n        ?string $stylePrompt = null,\n        ?string $customSystemPrompt = null,\n        float $temperature = 0.7,\n        int $maxTokens = 4096\n    ): array {\n        $searchResults = [];\n        $context = '';\n\n        \/\/ Only perform RAG if collections are selected\n        if ($collections !== []) {\n            \/\/ Step 1: Generate embedding for the question\n            try {\n                $queryEmbedding = $this->ollama->getEmbedding($question);\n            } catch (RuntimeException $e) {\n                throw new RuntimeException(\n                    'Embedding generation failed: ' . $e->getMessage(),\n                    0,\n                    $e\n                );\n            }\n\n            if ($queryEmbedding === []) {\n                throw new RuntimeException('Embedding generation returned empty vector');\n            }\n\n            \/\/ Step 2: Search across all selected collections\n            try {\n                $searchResults = $this->searchMultipleCollections($queryEmbedding, $collections, $limit);\n            } catch (RuntimeException $e) {\n                throw new RuntimeException(\n                    'Vector search failed: ' . $e->getMessage(),\n                    0,\n                    $e\n                );\n            }\n\n            \/\/ Step 3: Build context from search results (if any found)\n            if ($searchResults !== []) {\n                $context = $this->buildContext($searchResults);\n            }\n        }"
    }
}

{
    "tool_response": {
        "filePath": "\/var\/www\/dev.campus.systemische-tools.de\/src\/Infrastructure\/AI\/ChatService.php",
        "oldString": "    \/**\n     * Executes a complete RAG chat pipeline.\n     *\n     * Performs the following steps:\n     * 1. Generates an embedding vector for the question\n     * 2. Searches for similar documents in the vector database\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     * @param string      $question           The user's question to answer\n     * @param string      $model              The LLM model (claude-* or ollama:*)\n     * @param string      $collection         The Qdrant collection to search in (default: documents)\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 no relevant documents are found\n     * @throws RuntimeException If LLM request fails\n     *\n     * @example\n     * $chat = new ChatService($ollama, $qdrant, $claude);\n     * $result = $chat->chat('Was ist systemisches Coaching?', 'claude-opus-4-5-20251101', 'documents', 5, null, null, 0.7, 4096);\n     * \/\/ Returns: [\n     * \/\/   'question' => 'Was ist systemisches Coaching?',\n     * \/\/   'answer' => 'Systemisches Coaching ist...',\n     * \/\/   'sources' => [\n     * \/\/     ['title' => 'Coaching Grundlagen', 'score' => 0.89],\n     * \/\/     ['title' => 'Systemische Methoden', 'score' => 0.76]\n     * \/\/   ],\n     * \/\/   'model' => 'claude-opus-4-5-20251101',\n     * \/\/   'usage' => ['input_tokens' => 234, 'output_tokens' => 567],\n     * \/\/   'chunks_used' => 5\n     * \/\/ ]\n     *\/\n    public function chat(\n        string $question,\n        string $model = 'claude-opus-4-5-20251101',\n        string $collection = 'documents',\n        int $limit = 5,\n        ?string $stylePrompt = null,\n        ?string $customSystemPrompt = null,\n        float $temperature = 0.7,\n        int $maxTokens = 4096\n    ): array {\n        \/\/ Step 1: Generate embedding for the question\n        try {\n            $queryEmbedding = $this->ollama->getEmbedding($question);\n        } catch (RuntimeException $e) {\n            throw new RuntimeException(\n                'Embedding generation failed: ' . $e->getMessage(),\n                0,\n                $e\n            );\n        }\n\n        if ($queryEmbedding === []) {\n            throw new RuntimeException('Embedding generation returned empty vector');\n        }\n\n        \/\/ Step 2: Search for relevant document chunks\n        try {\n            $searchResults = $this->qdrant->search($queryEmbedding, $collection, $limit);\n        } catch (RuntimeException $e) {\n            throw new RuntimeException(\n                'Vector search failed: ' . $e->getMessage(),\n                0,\n                $e\n            );\n        }\n\n        if ($searchResults === []) {\n            throw new RuntimeException('No relevant documents found for the question');\n        }\n\n        \/\/ Step 3: Build context from search results\n        $context = $this->buildContext($searchResults);",
        "newString": "    \/**\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\n     *\n     * @example\n     * $chat = new ChatService($ollama, $qdrant, $claude);\n     * \/\/ With RAG (multiple collections)\n     * $result = $chat->chat('Was ist systemisches Coaching?', 'claude-opus-4-5-20251101', ['documents', 'mail'], 5);\n     * \/\/ Without RAG (no collections)\n     * $result = $chat->chat('Erkläre mir Python', 'claude-opus-4-5-20251101', [], 5);\n     *\/\n    public function chat(\n        string $question,\n        string $model = 'claude-opus-4-5-20251101',\n        array $collections = [],\n        int $limit = 5,\n        ?string $stylePrompt = null,\n        ?string $customSystemPrompt = null,\n        float $temperature = 0.7,\n        int $maxTokens = 4096\n    ): array {\n        $searchResults = [];\n        $context = '';\n\n        \/\/ Only perform RAG if collections are selected\n        if ($collections !== []) {\n            \/\/ Step 1: Generate embedding for the question\n            try {\n                $queryEmbedding = $this->ollama->getEmbedding($question);\n            } catch (RuntimeException $e) {\n                throw new RuntimeException(\n                    'Embedding generation failed: ' . $e->getMessage(),\n                    0,\n                    $e\n                );\n            }\n\n            if ($queryEmbedding === []) {\n                throw new RuntimeException('Embedding generation returned empty vector');\n            }\n\n            \/\/ Step 2: Search across all selected collections\n            try {\n                $searchResults = $this->searchMultipleCollections($queryEmbedding, $collections, $limit);\n            } catch (RuntimeException $e) {\n                throw new RuntimeException(\n                    'Vector search failed: ' . $e->getMessage(),\n                    0,\n                    $e\n                );\n            }\n\n            \/\/ Step 3: Build context from search results (if any found)\n            if ($searchResults !== []) {\n                $context = $this->buildContext($searchResults);\n            }\n        }",
        "originalFile": "<?php\n\ndeclare(strict_types=1);\n\nnamespace Infrastructure\\AI;\n\nuse RuntimeException;\n\n\/**\n * RAG (Retrieval-Augmented Generation) Chat Service.\n *\n * Provides a complete RAG pipeline that:\n * 1. Converts questions to embeddings using Ollama\n * 2. Searches for relevant document chunks in Qdrant\n * 3. Builds context from search results\n * 4. Generates answers using Claude or Ollama\n * 5. Returns structured responses with sources and metadata\n *\n * This service orchestrates the interaction between OllamaService,\n * QdrantService, and ClaudeService to implement a production-ready\n * RAG system for document-based question answering.\n *\n * @package Infrastructure\\AI\n * @author  System Generated\n * @version 1.0.0\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\n     * 2. Searches for similar documents in the vector database\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     * @param string      $question           The user's question to answer\n     * @param string      $model              The LLM model (claude-* or ollama:*)\n     * @param string      $collection         The Qdrant collection to search in (default: documents)\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 no relevant documents are found\n     * @throws RuntimeException If LLM request fails\n     *\n     * @example\n     * $chat = new ChatService($ollama, $qdrant, $claude);\n     * $result = $chat->chat('Was ist systemisches Coaching?', 'claude-opus-4-5-20251101', 'documents', 5, null, null, 0.7, 4096);\n     * \/\/ Returns: [\n     * \/\/   'question' => 'Was ist systemisches Coaching?',\n     * \/\/   'answer' => 'Systemisches Coaching ist...',\n     * \/\/   'sources' => [\n     * \/\/     ['title' => 'Coaching Grundlagen', 'score' => 0.89],\n     * \/\/     ['title' => 'Systemische Methoden', 'score' => 0.76]\n     * \/\/   ],\n     * \/\/   'model' => 'claude-opus-4-5-20251101',\n     * \/\/   'usage' => ['input_tokens' => 234, 'output_tokens' => 567],\n     * \/\/   'chunks_used' => 5\n     * \/\/ ]\n     *\/\n    public function chat(\n        string $question,\n        string $model = 'claude-opus-4-5-20251101',\n        string $collection = 'documents',\n        int $limit = 5,\n        ?string $stylePrompt = null,\n        ?string $customSystemPrompt = null,\n        float $temperature = 0.7,\n        int $maxTokens = 4096\n    ): array {\n        \/\/ Step 1: Generate embedding for the question\n        try {\n            $queryEmbedding = $this->ollama->getEmbedding($question);\n        } catch (RuntimeException $e) {\n            throw new RuntimeException(\n                'Embedding generation failed: ' . $e->getMessage(),\n                0,\n                $e\n            );\n        }\n\n        if ($queryEmbedding === []) {\n            throw new RuntimeException('Embedding generation returned empty vector');\n        }\n\n        \/\/ Step 2: Search for relevant document chunks\n        try {\n            $searchResults = $this->qdrant->search($queryEmbedding, $collection, $limit);\n        } catch (RuntimeException $e) {\n            throw new RuntimeException(\n                'Vector search failed: ' . $e->getMessage(),\n                0,\n                $e\n            );\n        }\n\n        if ($searchResults === []) {\n            throw new RuntimeException('No relevant documents found for the question');\n        }\n\n        \/\/ Step 3: Build context from search results\n        $context = $this->buildContext($searchResults);\n\n        \/\/ Step 4: Parse model string and generate answer\n        $isOllama = str_starts_with($model, 'ollama:');\n        $isClaude = str_starts_with($model, 'claude-');\n\n        if ($isClaude) {\n            try {\n                $ragPrompt = $this->claude->buildRagPrompt($question, $context);\n\n                \/\/ Build system prompt hierarchy: Default -> Custom -> Style\n                if ($customSystemPrompt !== null && $customSystemPrompt !== '') {\n                    $systemPrompt = $customSystemPrompt;\n                } else {\n                    $systemPrompt = $this->claude->getDefaultSystemPrompt();\n                }\n\n                \/\/ Append style prompt from author profile if provided\n                if ($stylePrompt !== null && $stylePrompt !== '') {\n                    $systemPrompt .= \"\\n\\n\" . $stylePrompt;\n                }\n\n                $llmResponse = $this->claude->ask($ragPrompt, $systemPrompt, $model, $maxTokens, $temperature);\n\n                $answer = $llmResponse['text'];\n                $usage = $llmResponse['usage'];\n            } catch (RuntimeException $e) {\n                throw new RuntimeException(\n                    'Claude API request failed: ' . $e->getMessage(),\n                    0,\n                    $e\n                );\n            }\n        } elseif ($isOllama) {\n            try {\n                \/\/ Extract actual model name (remove \"ollama:\" prefix)\n                $ollamaModel = substr($model, 7);\n\n                \/\/ Build instruction from custom prompt and style\n                $instructions = [];\n                if ($customSystemPrompt !== null && $customSystemPrompt !== '') {\n                    $instructions[] = $customSystemPrompt;\n                }\n                if ($stylePrompt !== null && $stylePrompt !== '') {\n                    $instructions[] = $stylePrompt;\n                }\n                $instructionBlock = $instructions !== [] ? implode(\"\\n\\n\", $instructions) . \"\\n\\n\" : '';\n\n                $ragPrompt = sprintf(\n                    \"%sKontext aus den Dokumenten:\\n\\n%s\\n\\n---\\n\\nFrage: %s\",\n                    $instructionBlock,\n                    $context,\n                    $question\n                );\n                $answer = $this->ollama->generate($ragPrompt, $ollamaModel, $temperature);\n                $usage = null;\n            } catch (RuntimeException $e) {\n                throw new RuntimeException(\n                    'Ollama generation failed: ' . $e->getMessage(),\n                    0,\n                    $e\n                );\n            }\n        } else {\n            throw new RuntimeException(\n                sprintf('Unknown model \"%s\". Use claude-* or ollama:* format.', $model)\n            );\n        }\n\n        \/\/ Step 5: Extract source information\n        $sources = $this->extractSources($searchResults);\n\n        \/\/ Step 6: Assemble response\n        $response = [\n            'question' => $question,\n            'answer' => $answer,\n            'sources' => $sources,\n            'model' => $model,\n            'chunks_used' => count($searchResults),\n        ];\n\n        if ($usage !== null) {\n            $response['usage'] = $usage;\n        }\n\n        return $response;\n    }\n\n    \/**\n     * Builds a context string from search results.\n     *\n     * Concatenates the content from multiple search results into a single\n     * context string, respecting a maximum character limit. Each chunk is\n     * labeled with its source document title.\n     *\n     * @param array<int, array{id: int|string, score: float, payload: array<string, mixed>}> $searchResults Vector search results\n     * @param int                                                                             $maxTokens     Maximum tokens to include (default: 3000)\n     *\n     * @return string The built context string\n     *\/\n    private function buildContext(array $searchResults, int $maxTokens = 3000): string\n    {\n        $contextParts = [];\n        $totalChars = 0;\n        $maxChars = $maxTokens * 4; \/\/ Approximate: 1 token ~ 4 characters\n\n        foreach ($searchResults as $index => $result) {\n            $payload = $result['payload'];\n            $content = (string) ($payload['content'] ?? '');\n            $docTitle = (string) ($payload['document_title'] ?? 'Unbekannt');\n\n            \/\/ Check if adding this chunk would exceed the limit\n            if ($totalChars + strlen($content) > $maxChars) {\n                break;\n            }\n\n            $contextParts[] = sprintf('[Quelle %d: %s]%s%s', $index + 1, $docTitle, \"\\n\", $content);\n            $totalChars += strlen($content);\n        }\n\n        return implode(\"\\n\\n---\\n\\n\", $contextParts);\n    }\n\n    \/**\n     * Extracts unique source information from search results.\n     *\n     * Collects document titles and scores from the search results,\n     * deduplicating by title to provide a clean list of sources.\n     * O... [TRUNCATED-e21b9600a444ad20]",
        "structuredPatch": [
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                "lines": [
                    "      * Executes a complete RAG chat pipeline.",
                    "      *",
                    "      * Performs the following steps:",
                    "-     * 1. Generates an embedding vector for the question",
                    "-     * 2. Searches for similar documents in the vector database",
                    "+     * 1. Generates an embedding vector for the question (if collections selected)",
                    "+     * 2. Searches for similar documents in the vector database(s)",
                    "      * 3. Builds context from the most relevant chunks",
                    "      * 4. Generates an answer using the specified LLM model",
                    "      * 5. Extracts source information",
                    "      * 6. Assembles a structured response",
                    "      *",
                    "-     * @param string      $question           The user's question to answer",
                    "-     * @param string      $model              The LLM model (claude-* or ollama:*)",
                    "-     * @param string      $collection         The Qdrant collection to search in (default: documents)",
                    "-     * @param int         $limit              Maximum number of document chunks to retrieve (default: 5)",
                    "-     * @param string|null $stylePrompt        Optional style prompt from author profile",
                    "-     * @param string|null $customSystemPrompt Optional custom system prompt (replaces default if set)",
                    "-     * @param float       $temperature        Sampling temperature 0.0-1.0 (default: 0.7)",
                    "-     * @param int         $maxTokens          Maximum tokens in response (default: 4096)",
                    "+     * If no collections are selected, steps 1-3 and 5 are skipped (no RAG).",
                    "      *",
                    "+     * @param string        $question           The user's question to answer",
                    "+     * @param string        $model              The LLM model (claude-* or ollama:*)",
                    "+     * @param array<string> $collections        Qdrant collections to search (empty = no RAG)",
                    "+     * @param int           $limit              Maximum number of document chunks to retrieve (default: 5)",
                    "+     * @param string|null   $stylePrompt        Optional style prompt from author profile",
                    "+     * @param string|null   $customSystemPrompt Optional custom system prompt (replaces default if set)",
                    "+     * @param float         $temperature        Sampling temperature 0.0-1.0 (default: 0.7)",
                    "+     * @param int           $maxTokens          Maximum tokens in response (default: 4096)",
                    "+     *",
                    "      * @return array{",
                    "      *     question: string,",
                    "      *     answer: string,"
                ]
            },
            {
                "oldStart": 71,
                "oldLines": 67,
                "newStart": 73,
                "newLines": 62,
                "lines": [
                    "      *",
                    "      * @throws RuntimeException If embedding generation fails",
                    "      * @throws RuntimeException If vector search fails",
                    "-     * @throws RuntimeException If no relevant documents are found",
                    "      * @throws RuntimeException If LLM request fails",
                    "      *",
                    "      * @example",
                    "      * $chat = new ChatService($ollama, $qdrant, $claude);",
                    "-     * $result = $chat->chat('Was ist systemisches Coaching?', 'claude-opus-4-5-20251101', 'documents', 5, null, null, 0.7, 4096);",
                    "-     * \/\/ Returns: [",
                    "-     * \/\/   'question' => 'Was ist systemisches Coaching?',",
                    "-     * \/\/   'answer' => 'Systemisches Coaching ist...',",
                    "-     * \/\/   'sources' => [",
                    "-     * \/\/     ['title' => 'Coaching Grundlagen', 'score' => 0.89],",
                    "-     * \/\/     ['title' => 'Systemische Methoden', 'score' => 0.76]",
                    "-     * \/\/   ],",
                    "-     * \/\/   'model' => 'claude-opus-4-5-20251101',",
                    "-     * \/\/   'usage' => ['input_tokens' => 234, 'output_tokens' => 567],",
                    "-     * \/\/   'chunks_used' => 5",
                    "-     * \/\/ ]",
                    "+     * \/\/ With RAG (multiple collections)",
                    "+     * $result = $chat->chat('Was ist systemisches Coaching?', 'claude-opus-4-5-20251101', ['documents', 'mail'], 5);",
                    "+     * \/\/ Without RAG (no collections)",
                    "+     * $result = $chat->chat('Erkläre mir Python', 'claude-opus-4-5-20251101', [], 5);",
                    "      *\/",
                    "     public function chat(",
                    "         string $question,",
                    "         string $model = 'claude-opus-4-5-20251101',",
                    "-        string $collection = 'documents',",
                    "+        array $collections = [],",
                    "         int $limit = 5,",
                    "         ?string $stylePrompt = null,",
                    "         ?string $customSystemPrompt = null,",
                    "         float $temperature = 0.7,",
                    "         int $maxTokens = 4096",
                    "     ): array {",
                    "-        \/\/ Step 1: Generate embedding for the question",
                    "-        try {",
                    "-            $queryEmbedding = $this->ollama->getEmbedding($question);",
                    "-        } catch (RuntimeException $e) {",
                    "-            throw new RuntimeException(",
                    "-                'Embedding generation failed: ' . $e->getMessage(),",
                    "-                0,",
                    "-                $e",
                    "-            );",
                    "-        }",
                    "+        $searchResults = [];",
                    "+        $context = '';",
                    " ",
                    "-        if ($queryEmbedding === []) {",
                    "-            throw new RuntimeException('Embedding generation returned empty vector');",
                    "-        }",
                    "+        \/\/ Only perform RAG if collections are selected",
                    "+        if ($collections !== []) {",
                    "+            \/\/ Step 1: Generate embedding for the question",
                    "+            try {",
                    "+                $queryEmbedding = $this->ollama->getEmbedding($question);",
                    "+            } catch (RuntimeException $e) {",
                    "+                throw new RuntimeException(",
                    "+                    'Embedding generation failed: ' . $e->getMessage(),",
                    "+                    0,",
                    "+                    $e",
                    "+                );",
                    "+            }",
                    " ",
                    "-        \/\/ Step 2: Search for relevant document chunks",
                    "-        try {",
                    "-            $searchResults = $this->qdrant->search($queryEmbedding, $collection, $limit);",
                    "-        } catch (RuntimeException $e) {",
                    "-            throw new RuntimeException(",
                    "-                'Vector search failed: ' . $e->getMessage(),",
                    "-                0,",
                    "-                $e",
                    "-            );",
                    "-        }",
                    "+            if ($queryEmbedding === []) {",
                    "+                throw new RuntimeException('Embedding generation returned empty vector');",
                    "+            }",
                    " ",
                    "-        if ($searchResults === []) {",
                    "-            throw new RuntimeException('No relevant documents found for the question');",
                    "+            \/\/ Step 2: Search across all selected collections",
                    "+            try {",
                    "+                $searchResults = $this->searchMultipleCollections($queryEmbedding, $collections, $limit);",
                    "+            } catch (RuntimeException $e) {",
                    "+                throw new RuntimeException(",
                    "+                    'Vector search failed: ' . $e->getMessage(),",
                    "+                    0,",
                    "+                    $e",
                    "+                );",
                    "+            }",
                    "+",
                    "+            \/\/ Step 3: Build context from search results (if any found)",
                    "+            if ($searchResults !== []) {",
                    "+                $context = $this->buildContext($searchResults);",
                    "+            }",
                    "         }",
                    " ",
                    "-        \/\/ Step 3: Build context from search results",
                    "-        $context = $this->buildContext($searchResults);",
                    "-",
                    "         \/\/ Step 4: Parse model string and generate answer",
                    "         $isOllama = str_starts_with($model, 'ollama:');",
                    "         $isClaude = str_starts_with($model, 'claude-');"
                ]
            }
        ],
        "userModified": false,
        "replaceAll": false
    }
}