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"originalFile": "# Content Pipeline - Validation Report\n\n## Status: READY FOR EXTERNAL USE\n\nAll critical issues have been resolved. This package can be deployed by an external AI.\n\n---\n\n## Fixes Applied\n\n### 1. Configuration Abstraction\n- **src\/config.py** - Replaced with environment-based configuration\n- All 38 settings now loaded via `os.environ.get()`\n- Fallback to `.env` file in project root\n\n### 2. Model Registry Independence\n- **model_registry.py** - No longer depends on database\n- Models configured via static defaults + environment overrides\n- No `ki_dev.ai_models` table required\n\n### 3. Path Portability\n- **25 files fixed** - All `\/var\/www\/scripts\/pipeline` paths replaced\n- Now use `os.path.dirname(os.path.abspath(__file__))` for relative paths\n- Added `import os` where missing\n\n### 4. Database Schema\n- **32 tables** defined in `sql\/schema.sql`\n- Added missing tables:\n - `pipeline_log` - Pipeline execution logs\n - `protokoll` - LLM call logging\n - `document_sections` - Document structure\n - `content_config` - Content generation config\n - `content_orders` - Content generation orders\n - `content_versions` - Content versions\n - `content_critiques` - Content critiques\n\n### 5. Removed Problematic Files\n- `run_demo.py` - Had hardcoded demo paths\n- `quality_test.py` - Had hardcoded test paths\n- `generate_semantics.py` - Had hardcoded credentials path\n\n### 6. SOLID Principles Implementation\nArchitecture refactored for better testability and maintainability:\n\n#### New Files Created\n| File | Purpose | SOLID Principle |\n|------|---------|-----------------|\n| `src\/enums.py` | Status enums (DocumentStatus, QueueStatus, ContentOrderStatus, etc.) | Type Safety |\n| `src\/llm_interface.py` | Abstract LLM provider with OllamaProvider, AnthropicProvider | **I**, **D** |\n| `src\/db_interface.py` | Database protocol interfaces (DocumentRepository, ChunkRepository, etc.) | **I**, **D** |\n| `src\/container.py` | Dependency Injection container | **D** |\n\n#### DRY Improvements (json_utils.py)\n- `parse_llm_response()` - Generic LLM JSON parsing with defaults\n- `parse_critic_response()` - Critic-specific response parsing\n- `parse_semantic_response()` - Semantic analysis response parsing\n- `parse_entity_response()` - Entity extraction response parsing\n\n#### Updated Files\n| File | Change |\n|------|--------|\n| `db.py` | DI documentation, interface compliance |\n| `db_documents.py` | DocumentStatus enum support |\n| `step_embed.py` | Type hints with DatabaseInterface |\n| `generators\/critic.py` | ContentOrderStatus enum, parse_critic_response() |\n| `generators\/persistence.py` | ContentOrderStatus enum support |\n| `analyzers\/semantic_analyzer.py` | parse_semantic_response() |\n\n#### SOLID Compliance\n| Principle | Status |\n|-----------|--------|\n| **S**ingle Responsibility | Mixins split by concern |\n| **O**pen\/Closed | LLMFactory.register() for new providers |\n| **L**iskov Substitution | Protocols define contracts |\n| **I**nterface Segregation | Separate protocols (DocumentRepository, ChunkRepository, etc.) |\n| **D**ependency Inversion | Container.get_db(), get_llm() instead of globals |\n\n---\n\n## Validation Results\n\n| Check | Result |\n|-------|--------|\n| Hardcoded `\/var\/www` paths | 0 |\n| Hardcoded database overrides | 0 |\n| CREATE TABLE statements | 32 |\n| Python files | 82 |\n| Environment variables in config | 38 |\n\n---\n\n## Deployment Checklist\n\n1. **Environment Setup**\n ```bash\n cp config\/settings.env.example .env\n nano .env # Configure all settings\n ```\n\n2. **Database**\n ```bash\n mariadb -e \"CREATE DATABASE content_pipeline CHARACTER SET utf8mb4\"\n mariadb content_pipeline < sql\/schema.sql\n ```\n\n3. **Python Environment**\n ```bash\n python3 -m venv venv\n source venv\/bin\/activate\n pip install -r src\/requirements.txt\n ```\n\n4. **Qdrant Collections**\n ```python\n from qdrant_client import QdrantClient\n from qdrant_client.models import VectorParams, Distance\n\n client = QdrantClient(host=\"localhost\", port=6333)\n client.create_collection(\"documents\", VectorParams(size=1024, distance=Distance.COSINE))\n client.create_collection(\"entities\", VectorParams(size=1024, distance=Distance.COSINE))\n ```\n\n5. **Ollama Models**\n ```bash\n ollama pull mxbai-embed-large\n ollama pull llama3.2:3b\n ```\n\n6. **Run Pipeline**\n ```bash\n source venv\/bin\/activate\n export $(cat .env | xargs)\n python src\/pipeline.py\n ```\n\n---\n\n## Environment Variables Required\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| `PIPELINE_DOCUMENT_PATH` | `~\/documents` | Source documents directory |\n| `DB_HOST` | `localhost` | Database host |\n| `DB_NAME` | `content_pipeline` | Database name |\n| `DB_USER` | `pipeline` | Database user |\n| `DB_PASSWORD` | (required) | Database password |\n| `QDRANT_HOST` | `localhost` | Qdrant host |\n| `QDRANT_PORT` | `6333` | Qdrant port |\n| `OLLAMA_HOST` | `http:\/\/localhost:11434` | Ollama API URL |\n| `OLLAMA_EMBEDDING_MODEL` | `mxbai-embed-large` | Embedding model |\n| `OLLAMA_CHAT_MODEL` | `llama3.2:3b` | Chat model |\n\nSee `config\/settings.env.example` for all options.\n",
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