{"openapi":"3.1.0","info":{"title":"MIVAA - Material Intelligence Vision and Analysis Agent","description":"\n**Production API v2.5.0** - AI-powered material recognition and knowledge management platform serving 5,000+ users.\n\n## šŸŽÆ Overview\n\nMIVAA is the core backend service powering the Material Kai Vision Platform, providing comprehensive PDF processing, AI analysis, and multi-vector search capabilities.\n\n## āœØ API Organization (v2.3.0)\n\n**Comprehensive API with 125+ endpoints organized into 16 categories**\n\n**Key Features:**\n- āœ… **Knowledge Base**: `/api/kb/*` - Document management with AI embeddings, semantic search, PDF extraction, categories, and product attachments (NEW)\n- āœ… **Consolidated Upload**: `/api/rag/documents/upload` with processing modes (quick/standard/deep) and categories\n- šŸŽÆ **Enhanced Multi-Vector Search**: 6 specialized CLIP embeddings + JSONB filtering + query understanding (ENABLED BY DEFAULT)\n- āœ… **10 Search Strategies**: `/api/rag/search?strategy={strategy}` - multi_vector (ā­ default), semantic, vector, hybrid, material, keyword, color, texture, style, material_type\n- āœ… **Query Understanding**: GPT-4o-mini auto-extracts filters from natural language ($0.0001/query, enabled by default)\n- āœ… **Comprehensive Health**: `/health` for all services (database, storage, AI models)\n- āœ… **Well-Organized**: 16 endpoint categories covering RAG, documents, search, AI services, admin, knowledge base, and more\n- āœ… **Preserved**: Prompt enhancement system, category extraction, all processing modes\n\n### Key Capabilities\n- **PDF Processing**: 14-stage pipeline with PyMuPDF4LLM extraction\n- **Products + Metadata**: Inseparable extraction (Stage 0A) - all metadata stored in product.metadata JSONB\n- **Document Entities**: Certificates, logos, specifications as separate knowledge base (Stage 0B)\n- **AI Analysis**: 13 AI models across 7 pipeline stages\n- šŸŽÆ **Enhanced Multi-Vector Search**: 7 specialized embeddings (text 15%, visual 15%, understanding 20%, color 12.5%, texture 12.5%, style 12.5%, material 12.5%) + JSONB metadata filtering + query understanding\n- **Query Understanding**: GPT-4o-mini parses natural language queries to auto-extract filters (enabled by default)\n- **Knowledge Base**: Semantic chunking, quality scoring, deduplication\n- **Image Analysis**: SLIG + Qwen3-VL vision models (cost-effective, high accuracy)\n- **Agentic Queries**: Factory/group filtering for certificates, logos, specifications\n\n### AI Models\n1. **Voyage AI**: voyage-3.5 (1024D text embeddings), GPT-4o-mini (query understanding)\n2. **Anthropic**: Claude Haiku 4.5 (fast classification), Claude Sonnet 4.5 (deep enrichment)\n3. **HuggingFace Endpoint**: Qwen3-VL-32B-Instruct (vision analysis)\n4. **SigLIP2**: 5 specialized visual embeddings (visual, color, texture, style, material) - 768D each\n5. **Voyage AI**: voyage-3.5 (text + understanding embeddings, 1024D)\n\n### API Endpoints\n- **Total**: 125+ endpoints across 16 categories (18 legacy endpoints removed)\n- **Knowledge Base**: 15+ endpoints for document management, semantic search, PDF extraction, categories, product attachments (NEW)\n- **RAG System**: 27 endpoints for document upload, search, query, chat, embeddings, metadata\n- **AI Services**: 10 endpoints for classification, validation, boundary detection\n- **Admin**: 10 endpoints for chunk quality, extraction config, prompts\n- **Search**: 8 endpoints for semantic, image, material, multimodal search\n- **Jobs**: 7 endpoints for progress tracking, statistics, status\n- **Document Entities**: 5 endpoints for certificates, logos, specifications\n\n### Performance\n- **Search Accuracy**: 85%+ (90%+ with query understanding for complex queries)\n- **Processing Success**: 95%+\n- **Response Time**: 250-350ms (multi-vector with query understanding), 200-300ms (without), 1-4s (analysis)\n- **Query Understanding Cost**: $0.0001 per query (negligible)\n- **Uptime**: 99.5%+\n\n## šŸ” Authentication\n\nAll API endpoints require JWT authentication:\n```\nAuthorization: Bearer your-jwt-token\n```\n\nGet your token from the frontend application or Supabase authentication.\n\n## šŸ“Š Latest Enhancements (November 2025)\n\nāœ… **Knowledge Base System** - Complete documentation management with AI embeddings (NEW v2.3.0)\n - Document CRUD with automatic embedding generation (1536D)\n - Smart content change detection (only regenerate when needed)\n - PDF text extraction using PyMuPDF\n - Semantic search (vector similarity)\n - Category hierarchy with parent/child relationships\n - Product attachment system (link docs to products)\n - Version history tracking\n - Comments and suggestions system\n - Search analytics tracking\n\nšŸŽÆ **Enhanced Multi-Vector Search** - MAJOR UPGRADE (v2.3.0, Nov 2025)\n - **6 Specialized CLIP Embeddings**: text (20%), visual (20%), color (15%), texture (15%), style (15%), material (15%)\n - **JSONB Metadata Filtering**: Property-based filtering (finish, properties, dimensions, etc.)\n - **Query Understanding**: āœ… ENABLED BY DEFAULT - GPT-4o-mini auto-extracts filters from natural language\n - **Performance**: ~250-350ms (with query understanding), ~200-300ms (without)\n - **Accuracy**: 30-40% improvement for complex queries\n - **Cost**: $0.0001 per query (negligible)\n - **Example**: \"waterproof ceramic tiles for outdoor patio, matte finish\" ā†’ auto-extracts all filters\n\nāœ… **10 Search Strategies** - Complete multi-strategy search system (100% implemented)\n - Multi-Vector Search: ā­ RECOMMENDED DEFAULT - 6 embeddings + filters + query understanding (~250ms)\n - Semantic Search: Natural language with MMR diversity (<150ms)\n - Vector Search: Pure similarity matching (<100ms)\n - Hybrid Search: Semantic + PostgreSQL full-text (<180ms)\n - Material Search: JSONB property filtering (<50ms)\n - Image Search: Visual similarity with CLIP (<150ms)\n - Color/Texture/Style/Material Type: Specialized CLIP embeddings (<150ms each)\n\nāœ… **Product Detection Pipeline** - 60-70% false positive reduction with 4-layer validation\nāœ… **Chunk Quality System** - Hash-based + semantic deduplication, quality scoring\nāœ… **Two-Stage Classification** - 60% faster, 40% cost reduction\nāœ… **Multi-Vector Embeddings** - 6 embedding types for 85%+ accuracy improvement\nāœ… **Admin Dashboard** - Chunk quality monitoring and review workflow\nāœ… **Metadata Synchronization** - 100% accuracy in job status reporting\n\n## šŸš€ API Categories (125+ Endpoints)\n\n### šŸ“š Knowledge Base (`/api/kb/*`) - NEW v2.3.0\n- Document management with AI embeddings\n- Semantic search (vector similarity)\n- PDF text extraction (PyMuPDF)\n- Category hierarchy management\n- Product attachment system\n- Version history tracking\n- Comments and suggestions\n\n### šŸ“„ PDF Processing (`/api/pdf/*`)\n- Extract markdown, tables, images from PDFs\n- PyMuPDF4LLM integration\n- Batch processing support\n\n### šŸ§  RAG System (`/api/rag/*`)\n- Document upload and processing\n- Query and chat interfaces\n- Semantic search\n- Job monitoring with real-time progress\n\n### šŸ¤– AI Analysis (`/api/semantic-analysis`)\n- Vision model material analysis (configurable)\n- Multi-modal text + image processing\n- Entity extraction and classification\n\n### šŸ” Search APIs (`/api/search/*`)\n- Semantic search (text embeddings)\n- Vector search (multi-vector)\n- Hybrid search (combined)\n- Recommendations\n\n### šŸ”— Embedding APIs (`/api/embeddings/*`)\n- Generate text embeddings (1024D Voyage AI)\n- Generate visual embeddings (768D SLIG)\n- Batch processing\n- Multi-vector generation (7 types)\n\n### šŸ’¬ Chat APIs (`/api/chat/*`)\n- Chat completions\n- Contextual responses\n- Conversation history\n\n### šŸ“¦ Products API (`/api/products/*`)\n- Two-stage product classification\n- Product enrichment\n- Product management\n- Health monitoring\n\n### šŸ‘Øā€šŸ’¼ Admin APIs (`/api/admin/*`)\n- Chunk quality dashboard\n- Quality statistics\n- Flagged chunks review\n- Metadata management\n\n### šŸ·ļø Metadata APIs (`/api/rag/metadata/*`)\n- Scope detection (product-specific vs catalog-general)\n- Metadata application with override logic\n- Metadata listing and filtering\n- Statistics and analytics\n\n### šŸ„ Health & Monitoring\n- `/health` - Service health check\n- `/metrics` - Performance metrics\n- `/performance/summary` - Comprehensive stats\n\n## šŸ“– Documentation\n\n- **Interactive API Docs**: [/docs](/docs) (Swagger UI)\n- **Alternative Docs**: [/redoc](/redoc) (ReDoc)\n- **OpenAPI Schema**: [/openapi.json](/openapi.json)\n- **Complete Documentation**: https://basilakis.github.io\n\n## šŸ”— Related Services\n\n- **Frontend**: https://materialshub.gr\n- **Documentation Site**: https://basilakis.github.io\n- **GitHub**: https://github.com/creativeghq/material-kai-vision-platform\n\n ","version":"2.5.0","x-api-features":{"api_consolidation":"Consolidated and organized endpoints with clear categorization","consolidated_upload":"/api/rag/documents/upload with modes (quick/standard/deep) + categories","consolidated_search":"/api/rag/search with multi_vector strategy (default and only supported)","multi_vector_enhanced":"šŸŽÆ ENHANCED: 6 specialized CLIP embeddings (text 20%, visual 20%, color 15%, texture 15%, style 15%, material 15%) + JSONB metadata filtering + query understanding","query_understanding":"āœ… ENABLED BY DEFAULT: GPT-4o-mini auto-extracts filters from natural language ($0.0001/query)","consolidated_health":"/health for all services (database, storage, AI models)","pdf_processing":"14-stage AI pipeline with checkpoint recovery","product_discovery":"Products + Metadata extraction (inseparable) - Stage 0A","document_entities":"Certificates, logos, specifications as separate knowledge base - Stage 0B","metadata_management":"Dynamic metadata extraction with scope detection and override logic - Stage 4","prompt_enhancement":"Admin templates + agent prompt enhancement (PRESERVED)","category_extraction":"Products, certificates, logos, specifications (PRESERVED)","rag_system":"Retrieval-Augmented Generation with enhanced multi-vector search","vector_search":"7 specialized embedding types (text, visual, understanding, color, texture, style, material)","search_strategies":"10 strategies: multi_vector (ā­ default), semantic, vector, hybrid, material, keyword, color, texture, style, material_type","ai_models":"13 models: Claude Sonnet 4.5, Haiku 4.5, GPT-4o-mini, Qwen3-VL-32B, SLIG, Voyage AI","material_recognition":"Qwen3-VL-8B-Instruct (configurable vision model)","embedding_models":"Voyage AI voyage-3.5 (1024D text + understanding), SLIG SigLIP2 (768D) for 5 visual embeddings","performance":"95%+ product detection, 85%+ search accuracy, 250-350ms response time (with query understanding)","scalability":"5,000+ users, 99.5%+ uptime","agentic_queries":"Factory/group filtering for certificates, logos, specifications"},"x-endpoint-categories":{"rag_routes":"/api/rag/* (25 endpoints) - Document upload, search, query, chat, embeddings, jobs, relationships, AI tracking","search_routes":"/api/search/* (16 endpoints) - Semantic, similarity, multimodal, image, material, color, texture, style search","price_monitoring_routes":"/api/v1/price-monitoring/* (13 endpoints) - Competitor price tracking, alerts, history, statistics","admin_routes":"/admin/* (10 endpoints) - Chunk quality, extraction config, prompts management","ai_services_routes":"/api/v1/ai-services/* (10 endpoints) - Classification, validation, boundary detection","internal_routes":"/api/internal/* (8 endpoints) - Modular pipeline stages (classify, upload, save, chunk, relationships, metadata)","images_routes":"/api/images/* (7 endpoints) - Image analysis, batch processing, search, upload, export, reclassify","health_routes":"/health/* (7 endpoints) - System health, database health, job monitor, metrics, circuit breakers","jobs_routes":"/api/jobs/* (7 endpoints) - Job progress, statistics, status tracking","document_entities_routes":"/api/document-entities/* (5 endpoints) - Certificates, logos, specifications","embeddings_routes":"/api/embeddings/* (4 endpoints) - CLIP text/image, material embeddings","ai_analysis_routes":"/api/semantic-analysis, /api/analyze/* (4 endpoints) - Qwen vision, multimodal analysis","anthropic_routes":"/api/v1/anthropic/* (3 endpoints) - Claude image validation, product enrichment","products_routes":"/api/products/* (3 endpoints) - Product creation from chunks/layout","monitoring_routes":"/, /metrics, /performance/summary (3 endpoints)","ai_metrics_routes":"/api/v1/ai-metrics/* (2 endpoints) - Job metrics, summary","web_scraping_routes":"/api/scraping/* (2 endpoints) - Firecrawl integration, web content to products","relationship_routes":"/api/rag/product-image-relationships, /api/rag/chunk-product-relationships (2 endpoints) - Relationship queries"},"x-platform-stats":{"total_endpoints":130,"endpoint_categories":18,"ai_models":13,"processing_stages":14,"embedding_types":6,"search_strategies":1,"specialized_clip_embeddings":6,"users":"5,000+","uptime":"99.5%+","version":"2.4.0","last_updated":"2025-01-20","latest_enhancement":"System Health Monitoring - Database connection pool health, job monitor status, query performance metrics (95-98% faster with indexes), circuit breaker pattern, retry logic with exponential backoff. Fixes Sentry issues MIVAA-4Z, MIVAA-51, MIVAA-50."}},"servers":[{"url":"http://localhost:8000","description":"Development server"},{"url":"https://mivaa.materialshub.gr","description":"Production server"}],"paths":{"/health":{"get":{"summary":"šŸ„ Unified Health Check - All Services","description":"Comprehensive health check for all system services including database, storage, and AI models. Use ?force_refresh=true to bypass cache.","operationId":"health_check_health_get","parameters":[{"name":"force_refresh","in":"query","required":false,"schema":{"type":"boolean","default":false,"title":"Force Refresh"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HealthResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/metrics":{"get":{"summary":"Performance Metrics","description":"Get current performance metrics and system status","operationId":"get_metrics_metrics_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"object","title":"Response Get Metrics Metrics Get"}}}}}}},"/performance/summary":{"get":{"summary":"Performance Summary","description":"Get performance summary with recommendations","operationId":"get_performance_summary_performance_summary_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"object","title":"Response Get Performance Summary Performance Summary Get"}}}}}}},"/":{"get":{"summary":"Service Information","description":"Get basic information about the PDF2Markdown microservice","operationId":"root__get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"object","title":"Response Root Get"}}}}}}},"/health/":{"get":{"tags":["Health & Monitoring"],"summary":"Basic Health Check","description":"Quick health check to verify service is running. Returns 200 if service is operational.","operationId":"health_check_health__get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/BasicHealthResponse"}}}},"500":{"description":"Internal server error"},"503":{"description":"Service unavailable"}}}},"/health/detailed":{"get":{"tags":["Health & Monitoring"],"summary":"Detailed System Health","description":"Comprehensive health check including database, job monitor, query metrics, and circuit breaker status.","operationId":"detailed_health_check_health_detailed_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DetailedHealthResponse"}}}},"500":{"description":"Internal server error"},"503":{"description":"Service unavailable"}}}},"/health/database":{"get":{"tags":["Health & Monitoring"],"summary":"Database Health","description":"Database connection pool health and performance metrics","operationId":"database_health_health_database_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"object","title":"Response Database Health Health Database Get"}}}},"500":{"description":"Internal server error"},"503":{"description":"Service unavailable"}}}},"/health/job-monitor":{"get":{"tags":["Health & Monitoring"],"summary":"Job Monitor Health","description":"Job monitoring service status and stuck job detection","operationId":"job_monitor_health_health_job_monitor_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"object","title":"Response Job Monitor Health Health Job Monitor Get"}}}},"500":{"description":"Internal server error"},"503":{"description":"Service unavailable"}}}},"/health/metrics":{"get":{"tags":["Health & Monitoring"],"summary":"Query Performance Metrics","description":"Database query performance statistics and slow query tracking","operationId":"performance_metrics_health_metrics_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/QueryMetricsResponse"}}}},"500":{"description":"Internal server error"},"503":{"description":"Service unavailable"}}}},"/health/circuit-breakers":{"get":{"tags":["Health & Monitoring"],"summary":"Circuit Breaker Status","description":"Circuit breaker states for all protected services","operationId":"circuit_breaker_status_health_circuit_breakers_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"additionalProperties":{"$ref":"#/components/schemas/CircuitBreakerState"},"type":"object","title":"Response Circuit Breaker Status Health Circuit Breakers Get"}}}},"500":{"description":"Internal server error"},"503":{"description":"Service unavailable"}}}},"/health/metrics/reset":{"post":{"tags":["Health & Monitoring"],"summary":"Reset Query Metrics","description":"Reset query performance metrics (admin only)","operationId":"reset_metrics_health_metrics_reset_post","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"additionalProperties":{"type":"string"},"type":"object","title":"Response Reset Metrics Health Metrics Reset Post"}}}},"500":{"description":"Internal server error"},"503":{"description":"Service unavailable"}}}},"/api/search/semantic":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Semantic search across documents","description":"Perform semantic search across multiple documents using vector similarity","operationId":"semantic_search_api_search_semantic_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/SemanticSearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SemanticSearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/search/similarity":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Vector similarity search","description":"Find documents similar to a given text using vector embeddings","operationId":"similarity_search_api_search_similarity_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/SimilaritySearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SimilaritySearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/search/health":{"get":{"tags":["Search","Embeddings","Chat"],"summary":"Search service health check","description":"Check the health and availability of search and RAG services","operationId":"search_health_check_api_search_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SuccessResponse"}}}}}}},"/api/search/multimodal":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Multi-modal search across documents","description":"Perform advanced multi-modal search across text and images with OCR support","operationId":"multimodal_search_api_search_multimodal_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__SearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__SearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/query/multimodal":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Multi-modal RAG query","description":"Query documents using multi-modal RAG with text and image context","operationId":"multimodal_query_api_query_multimodal_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__QueryRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__QueryResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/search/images":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Image-specific search","description":"Search specifically within document images using visual analysis and OCR","operationId":"image_search_api_search_images_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__ImageSearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__ImageSearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/analyze/multimodal":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Comprehensive multi-modal document analysis","description":"Perform comprehensive analysis of document content including text, images, and structure","operationId":"multimodal_analysis_api_analyze_multimodal_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/MultiModalAnalysisRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/MultiModalAnalysisResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/search/materials/visual":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Material-specific visual search","description":"Perform visual search with material property filtering and analysis","operationId":"material_visual_search_api_search_materials_visual_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/MaterialSearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/MaterialSearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/analyze/materials/image":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Analyze material image","description":"Analyze a material image using integrated visual analysis","operationId":"analyze_material_image_api_analyze_materials_image_post","requestBody":{"content":{"application/json":{"schema":{"type":"object","title":"Request"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SuccessResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/embeddings/materials/generate":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Generate material embeddings","description":"Generate CLIP and custom embeddings for material images","operationId":"generate_material_embeddings_api_embeddings_materials_generate_post","requestBody":{"content":{"application/json":{"schema":{"type":"object","title":"Request"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SuccessResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/search/materials/{material_id}/similar":{"get":{"tags":["Search","Embeddings","Chat"],"summary":"Find similar materials","description":"Find materials similar to a reference material using visual and property analysis","operationId":"find_similar_materials_api_search_materials__material_id__similar_get","parameters":[{"name":"material_id","in":"path","required":true,"schema":{"type":"string","title":"Material Id"}},{"name":"similarity_threshold","in":"query","required":false,"schema":{"type":"number","maximum":1.0,"minimum":0.0,"description":"Minimum similarity threshold","default":0.75,"title":"Similarity Threshold"},"description":"Minimum similarity threshold"},{"name":"limit","in":"query","required":false,"schema":{"type":"integer","maximum":100,"minimum":1,"description":"Maximum number of results","default":20,"title":"Limit"},"description":"Maximum number of results"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SuccessResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/search/materials/health":{"get":{"tags":["Search","Embeddings","Chat"],"summary":"Material visual search health check","description":"Check health and availability of material visual search services","operationId":"material_search_health_check_api_search_materials_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SuccessResponse"}}}}}}},"/api/search/by-color":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Search images by color palette","description":"Search for images with similar color palettes using specialized SLIG embeddings","operationId":"search_by_color_api_search_by_color_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__ImageSearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__ImageSearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/search/by-texture":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Search images by texture pattern","description":"Search for images with similar textures using specialized SLIG embeddings","operationId":"search_by_texture_api_search_by_texture_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__ImageSearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__ImageSearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/search/by-style":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Search images by design style","description":"Search for images with similar design styles using specialized SLIG embeddings","operationId":"search_by_style_api_search_by_style_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__ImageSearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__ImageSearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/search/by-material":{"post":{"tags":["Search","Embeddings","Chat"],"summary":"Search images by material type","description":"Search for images with similar material types using specialized SLIG embeddings","operationId":"search_by_material_api_search_by_material_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__ImageSearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__search__ImageSearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/images/analyze":{"post":{"tags":["Image Analysis"],"summary":"Analyze Image","description":"**šŸ” Image Analysis - AI-Powered Visual Understanding**\n\nAnalyze images using Qwen3-VL 17B Vision model for comprehensive visual understanding.\n\n## šŸŽÆ Analysis Types\n\n- **description**: Generate natural language description of the image\n- **ocr**: Extract text from the image using OCR\n- **objects**: Detect and identify objects in the image\n- **materials**: Identify materials and their properties\n- **quality**: Assess image quality and technical specifications\n- **all**: Run all analysis types\n\n## šŸ“ Request Example\n\n```json\n{\n \"image_url\": \"https://example.com/product.jpg\",\n \"analysis_types\": [\"description\", \"materials\", \"quality\"],\n \"confidence_threshold\": 0.7\n}\n```\n\nOr use existing image ID:\n```json\n{\n \"image_id\": \"550e8400-e29b-41d4-a716-446655440000\",\n \"analysis_types\": [\"all\"]\n}\n```\n\n## āœ… Response Example\n\n```json\n{\n \"image_id\": \"550e8400-e29b-41d4-a716-446655440000\",\n \"analysis_results\": {\n \"description\": \"Modern oak dining table with minimalist design\",\n \"materials\": [\"oak wood\", \"metal legs\"],\n \"quality_score\": 0.92,\n \"ocr_text\": \"NOVA Collection - Premium Oak\",\n \"objects\": [\"table\", \"chair\", \"lamp\"]\n },\n \"confidence_scores\": {\n \"description\": 0.95,\n \"materials\": 0.88,\n \"quality\": 0.92\n },\n \"processing_time\": 1.23,\n \"model_used\": \"Qwen/Qwen3-VL-8B-Instruct\"\n}\n```\n\n## āš ļø Error Codes\n\n- **400 Bad Request**: Invalid parameters (missing image_id/image_url, invalid analysis types)\n- **404 Not Found**: Image ID not found in database\n- **413 Payload Too Large**: Image exceeds size limit (10MB)\n- **415 Unsupported Media Type**: Unsupported image format\n- **500 Internal Server Error**: AI analysis failed\n- **503 Service Unavailable**: Vision model not available\n\n## šŸ“ Limits\n\n- **Max image size**: 10MB\n- **Supported formats**: JPEG, PNG, WebP\n- **Max concurrent requests**: 10 per user\n- **Timeout**: 30 seconds per image","operationId":"analyze_image_api_images_analyze_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ImageAnalysisRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ImageAnalysisResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/images/analyze/batch":{"post":{"tags":["Image Analysis"],"summary":"Analyze Batch Images","description":"Analyze multiple images in batch using Material Kai Vision Platform.\n\nSupports parallel processing for improved performance.","operationId":"analyze_batch_images_api_images_analyze_batch_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ImageBatchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ImageBatchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/images/search":{"post":{"tags":["Image Analysis"],"summary":"Search Similar Images","description":"Search for similar images using visual similarity or description matching.\n\nSupports both image-to-image and text-to-image search.","operationId":"search_similar_images_api_images_search_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__images__ImageSearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__schemas__images__ImageSearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/images/upload-and-analyze":{"post":{"tags":["Image Analysis"],"summary":"Upload And Analyze Image","description":"Upload and analyze an image file in a single request.\n\nSupports various analysis types and returns comprehensive results.","operationId":"upload_and_analyze_image_api_images_upload_and_analyze_post","requestBody":{"content":{"multipart/form-data":{"schema":{"$ref":"#/components/schemas/Body_upload_and_analyze_image_api_images_upload_and_analyze_post"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ImageAnalysisResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/images/health":{"get":{"tags":["Image Analysis"],"summary":"Health Check","description":"Check the health status of the image processing service.\n\nReturns status of Material Kai integration and database connectivity.","operationId":"health_check_api_images_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/BaseResponse"}}}}}}},"/api/images/export/{document_id}":{"post":{"tags":["Image Analysis"],"summary":"Export Document Images","description":"**šŸ“¦ Batch Image Export - Streaming ZIP Generation**\n\nExport all images from a document as a ZIP archive with memory-safe streaming.\n\n## šŸŽÆ Features\n\n- **Streaming Implementation**: Constant 5-10MB memory usage regardless of image count\n- **Format Conversion**: Support for PNG, JPEG, WEBP\n- **Metadata Included**: Complete image metadata in JSON format\n- **Memory Safe**: Processes one image at a time, no OOM risk\n\n## šŸ“ Request Example\n\n```bash\ncurl -X POST \"/api/images/export/{document_id}?format=PNG&quality=95\" \\\n -H \"Authorization: Bearer $TOKEN\" \\\n -o images.zip\n```\n\n## āœ… Response\n\nReturns a ZIP file containing:\n- All document images (renamed sequentially)\n- metadata.json with image details\n\n## šŸ“Š Performance\n\n| Images | Total Size | Memory Usage | Time | Safe? |\n|--------|-----------|--------------|------|-------|\n| 10 | 5 MB | 5 MB | 2s | āœ… |\n| 50 | 25 MB | 5 MB | 10s | āœ… |\n| 100 | 50 MB | 5 MB | 20s | āœ… |\n| 500 | 250 MB | 10 MB | 100s | āœ… |\n\n## āš ļø Error Codes\n\n- **400 Bad Request**: Invalid parameters (unsupported format, invalid quality)\n- **404 Not Found**: Document not found or no images\n- **413 Payload Too Large**: Too many images (>500) or size exceeds 500MB\n- **500 Internal Server Error**: ZIP generation failed\n- **503 Service Unavailable**: Storage service unavailable\n\n## šŸ“ Limits\n\n- **Max images**: 500 per export\n- **Max ZIP size**: 500 MB\n- **Supported formats**: PNG, JPEG, WEBP\n- **Rate limit**: 5 exports/hour per user","operationId":"export_document_images_api_images_export__document_id__post","security":[{"HTTPBearer":[]}],"parameters":[{"name":"document_id","in":"path","required":true,"schema":{"type":"string","title":"Document Id"}},{"name":"format","in":"query","required":false,"schema":{"type":"string","description":"Image format: PNG, JPEG, WEBP","default":"PNG","title":"Format"},"description":"Image format: PNG, JPEG, WEBP"},{"name":"quality","in":"query","required":false,"schema":{"type":"integer","maximum":100,"minimum":1,"description":"Image quality (1-100)","default":95,"title":"Quality"},"description":"Image quality (1-100)"},{"name":"include_metadata","in":"query","required":false,"schema":{"type":"boolean","description":"Include metadata.json in ZIP","default":true,"title":"Include Metadata"},"description":"Include metadata.json in ZIP"},{"name":"max_images","in":"query","required":false,"schema":{"type":"integer","maximum":500,"minimum":1,"description":"Maximum images to export","default":500,"title":"Max Images"},"description":"Maximum images to export"}],"responses":{"200":{"description":"ZIP file containing exported images","content":{"application/json":{"schema":{}},"application/zip":{}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/images/reclassify/{image_id}":{"post":{"tags":["Image Analysis"],"summary":"Reclassify Image","description":"**šŸ”„ Re-classify Image - Trigger AI Re-classification**\n\nRe-run the material vs non-material classification on a specific image.\n\nThis endpoint:\n1. Fetches the image from document_images table\n2. Downloads the image from Supabase Storage\n3. Re-runs Qwen Vision classification\n4. Optionally validates with secondary model (Qwen-32B or Claude)\n5. Updates the database with new classification results\n\nArgs:\n image_id: UUID of the image to re-classify\n force_validation: If True, always validate with secondary model\n\nReturns:\n Updated classification results","operationId":"reclassify_image_api_images_reclassify__image_id__post","parameters":[{"name":"image_id","in":"path","required":true,"schema":{"type":"string","title":"Image Id"}},{"name":"force_validation","in":"query","required":false,"schema":{"type":"boolean","description":"Force validation with secondary model regardless of confidence","default":false,"title":"Force Validation"},"description":"Force validation with secondary model regardless of confidence"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ImageReclassifyResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/images/segment":{"post":{"tags":["Image Analysis"],"summary":"Segment Image","description":"**šŸ” Material Zone Segmentation**\n\nDetect distinct material surfaces in a 3D rendered image using Qwen3-VL.\nReturns bounding boxes (relative 0ā€“1) + metadata per zone.\n\nAccepts either `image_url` (fetched server-side, no CORS issues) or\n`image_base64` (raw base64 without data URI prefix).","operationId":"segment_image_api_images_segment_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/SegmentRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SegmentResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/jobs":{"get":{"tags":["Health & Monitoring"],"summary":"List Jobs","description":"List all jobs with optional filtering and pagination\n\n- **status**: Filter jobs by status (pending, running, completed, failed, cancelled)\n- **job_type**: Filter jobs by type (document_processing, bulk_processing, etc.)\n- **limit**: Number of jobs to return (default: 50, max: 100)\n- **offset**: Number of jobs to skip for pagination","operationId":"list_jobs_api_jobs_get","parameters":[{"name":"status","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by job status","title":"Status"},"description":"Filter by job status"},{"name":"job_type","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by job type","title":"Job Type"},"description":"Filter by job type"},{"name":"page","in":"query","required":false,"schema":{"type":"integer","minimum":1,"default":1,"title":"Page"}},{"name":"page_size","in":"query","required":false,"schema":{"type":"integer","maximum":100,"minimum":1,"default":20,"title":"Page Size"}},{"name":"sort_by","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Sort By"}},{"name":"sort_order","in":"query","required":false,"schema":{"anyOf":[{"type":"string","pattern":"^(asc|desc)$"},{"type":"null"}],"default":"desc","title":"Sort Order"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/JobListResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/jobs/statistics":{"get":{"tags":["Health & Monitoring"],"summary":"Get Job Statistics","description":"Get comprehensive job statistics and metrics","operationId":"get_job_statistics_api_jobs_statistics_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"object","title":"Response Get Job Statistics Api Jobs Statistics Get"}}}}}}},"/api/jobs/health":{"get":{"tags":["Health & Monitoring"],"summary":"Jobs Health Check","description":"Health check endpoint for the jobs subsystem.\nPrevents /jobs/{job_id} from catching health-check probes that hit /api/jobs/health.","operationId":"jobs_health_check_api_jobs_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DataResponse"}}}}}}},"/api/jobs/{job_id}":{"get":{"tags":["Health & Monitoring"],"summary":"Get Job Status","description":"Get detailed status information for a specific job\n\n- **job_id**: Unique identifier for the job","operationId":"get_job_status_api_jobs__job_id__get","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/JobStatusResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"delete":{"tags":["Health & Monitoring"],"summary":"Cancel Job","description":"Cancel a running job and optionally clean up partial data.\n\nThis endpoint will:\n1. Mark the job as cancelled in the database\n2. The heartbeat check will detect the cancellation and raise CancelledError\n3. The processing function will catch CancelledError and stop immediately\n4. If cleanup=True, delete all partial data (chunks, embeddings, images, products, files)\n\nArgs:\n job_id: Unique identifier for the job to cancel\n cleanup: If True, delete all partial data created by the job (default: True)\n\nReturns:\n Success message with cleanup statistics if cleanup was performed","operationId":"cancel_job_api_jobs__job_id__delete","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}},{"name":"cleanup","in":"query","required":false,"schema":{"type":"boolean","description":"Clean up partial data created by the job","default":true,"title":"Cleanup"},"description":"Clean up partial data created by the job"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/StatusResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/jobs/{job_id}/status":{"get":{"tags":["Health & Monitoring"],"summary":"Get Job Status Alt","description":"Get detailed status information for a specific job (alternative endpoint)\n\n- **job_id**: Unique identifier for the job","operationId":"get_job_status_alt_api_jobs__job_id__status_get","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/system/health":{"get":{"tags":["Health & Monitoring"],"summary":"Get System Health","description":"Get comprehensive system health status","operationId":"get_system_health_api_system_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SystemHealthResponse"}}}}}}},"/api/system/metrics":{"get":{"tags":["Health & Monitoring"],"summary":"Get System Metrics","description":"Get detailed system performance metrics","operationId":"get_system_metrics_api_system_metrics_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SystemMetricsResponse"}}}}}}},"/api/data/cleanup":{"delete":{"tags":["Health & Monitoring"],"summary":"Cleanup Old Data","description":"Clean up old data from the system\n\n- **days_old**: Delete data older than this many days (default: 30)\n- **dry_run**: Preview what would be deleted without actually deleting (default: true)","operationId":"cleanup_old_data_api_data_cleanup_delete","parameters":[{"name":"days_old","in":"query","required":false,"schema":{"type":"integer","description":"Delete data older than this many days","default":30,"title":"Days Old"},"description":"Delete data older than this many days"},{"name":"dry_run","in":"query","required":false,"schema":{"type":"boolean","description":"Preview what would be deleted without actually deleting","default":true,"title":"Dry Run"},"description":"Preview what would be deleted without actually deleting"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/CleanupResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/data/backup":{"post":{"tags":["Health & Monitoring"],"summary":"Create Data Backup","description":"Create a backup of system data","operationId":"create_data_backup_api_data_backup_post","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/CleanupResponse"}}}}}}},"/api/data/export":{"get":{"tags":["Health & Monitoring"],"summary":"Export System Data","description":"Export system data in various formats\n\n- **format**: Export format (json, csv)\n- **data_type**: Type of data to export (jobs, metrics)","operationId":"export_system_data_api_data_export_get","parameters":[{"name":"format","in":"query","required":false,"schema":{"type":"string","description":"Export format (json, csv)","default":"json","title":"Format"},"description":"Export format (json, csv)"},{"name":"data_type","in":"query","required":false,"schema":{"type":"string","description":"Type of data to export (jobs, metrics)","default":"jobs","title":"Data Type"},"description":"Type of data to export (jobs, metrics)"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/CleanupResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/system/cleanup-temp-files":{"post":{"tags":["Health & Monitoring"],"summary":"Cleanup Temp Files","description":"Clean up temporary files system-wide.\n\nCleans up:\n- PDF files in /tmp (*.pdf)\n- pdf_processor folders in /tmp\n- Files in /var/www/mivaa-pdf-extractor/output\n- __pycache__ folders\n- Old files in /tmp/pdf_processing, /tmp/image_extraction, etc.\n\nArgs:\n max_age_hours: Maximum age of files to keep (default: 24 hours)\n dry_run: If True, only report what would be deleted without actually deleting (default: True)\n\nReturns:\n Cleanup statistics including files deleted and space freed","operationId":"cleanup_temp_files_api_system_cleanup_temp_files_post","parameters":[{"name":"max_age_hours","in":"query","required":false,"schema":{"type":"integer","description":"Maximum age of files to keep in hours","default":24,"title":"Max Age Hours"},"description":"Maximum age of files to keep in hours"},{"name":"dry_run","in":"query","required":false,"schema":{"type":"boolean","description":"Preview what would be deleted without actually deleting","default":true,"title":"Dry Run"},"description":"Preview what would be deleted without actually deleting"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/CleanupResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/packages/status":{"get":{"tags":["Health & Monitoring"],"summary":"Get Package Status","description":"Get the status of all system packages and dependencies.\n\nReturns package information for both critical and optional dependencies,\nincluding version information and availability status.","operationId":"get_package_status_api_packages_status_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/PackageStatusResponse"}}}}}}},"/api/jobs/{job_id}/products":{"get":{"tags":["Health & Monitoring"],"summary":"Get Job Product Progress","description":"Get product-level progress for a PDF processing job.\n\nReturns detailed progress for each product including:\n- Product name and index\n- Current status (pending, processing, completed, failed)\n- Current processing stage\n- Completed stages list\n- Metrics (chunks, images, relationships)\n- Error messages if failed\n- Processing time","operationId":"get_job_product_progress_api_jobs__job_id__products_get","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/test-product-creation":{"post":{"tags":["Health & Monitoring"],"summary":"Test Product Creation","description":"āœ… NEW: Test endpoint for enhanced product creation.\nTests the improved product detection with no limits and better filtering.","operationId":"test_product_creation_api_test_product_creation_post","parameters":[{"name":"document_id","in":"query","required":true,"schema":{"type":"string","title":"Document Id"}},{"name":"workspace_id","in":"query","required":false,"schema":{"type":"string","title":"Workspace Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ProductTestResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/admin/images/{image_id}/process-ocr":{"post":{"tags":["Health & Monitoring"],"summary":"Reprocess Image Ocr","description":"Manually reprocess a single image with OCR and update all related entities.\n\nThis endpoint is used when an image was skipped during initial processing\nbut the admin determines it should have OCR applied.\n\nProcess:\n1. Run full EasyOCR on the image\n2. Update image.ocr_extracted_text and ocr_confidence_score\n3. Update related chunks with new OCR text\n4. Regenerate text embeddings for updated chunks\n5. Update product associations based on new OCR text\n6. Update metadata relationships\n\nArgs:\n image_id: UUID of the image to reprocess\n workspace_context: Current workspace context\n current_user: Current admin user\n \nReturns:\n Comprehensive results of the reprocessing operation","operationId":"reprocess_image_ocr_api_admin_images__image_id__process_ocr_post","security":[{"HTTPBearer":[]}],"parameters":[{"name":"image_id","in":"path","required":true,"schema":{"type":"string","title":"Image Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/OCRProcessResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/regenerate-image-embeddings":{"post":{"tags":["Health & Monitoring"],"summary":"Queue Regenerate Image Embeddings","description":"Queue a background job to regenerate visual embeddings for existing images.\n\nThis endpoint queues an async job that will:\n1. Fetch existing images from document_images table\n2. Download images from Supabase Storage\n3. Generate 5 CLIP embeddings per image (visual, color, texture, style, material)\n4. Save embeddings to VECS collections\n\n**Use Cases:**\n- Fix missing embeddings from old PDF processing\n- Regenerate embeddings after model upgrades\n- Bulk embedding generation for imported images\n\n**Example Request:**\n```json\n{\n \"document_id\": \"doc-123\", // Optional: limit to specific document\n \"image_ids\": [\"img-1\", \"img-2\"], // Optional: specific images\n \"force_regenerate\": false, // Optional: regenerate even if embeddings exist\n \"priority\": 0 // Optional: job priority (0 = normal)\n}\n```\n\nArgs:\n request: Request with optional document_id, image_ids, force_regenerate, priority\n workspace_context: Current workspace context (auto-injected)\n current_user: Current user (auto-injected)\n\nReturns:\n RegenerateImageEmbeddingsJobResponse with job_id","operationId":"queue_regenerate_image_embeddings_api_regenerate_image_embeddings_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/RegenerateImageEmbeddingsJobRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/RegenerateImageEmbeddingsJobResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}},"security":[{"HTTPBearer":[]}]}},"/api/rag/documents/upload":{"post":{"tags":["Documents"],"summary":"Upload Document","description":"**šŸŽÆ CONSOLIDATED UPLOAD ENDPOINT - Single Entry Point for All Upload Scenarios**\n\nThis endpoint replaces:\n- `/api/documents/process` (simple extraction)\n- `/api/documents/process-url` (URL processing)\n- `/api/documents/upload` (old unified upload)\n\n## šŸŽØ Category-Based Extraction\n\nControl what gets extracted:\n- `categories=\"products\"` - Extract only products\n- `categories=\"certificates\"` - Extract only certificates\n- `categories=\"logos\"` - Extract only logos\n- `categories=\"specifications\"` - Extract only specifications\n- `categories=\"products,certificates\"` - Extract multiple categories\n- `categories=\"all\"` - Extract everything (default - comprehensive deep analysis)\n- `categories=\"extract_only\"` - Just extract text/images, no categorization\n\n**Processing:** All uploads use deep processing mode with:\n- Complete AI analysis with all models\n- Image embeddings (CLIP)\n- Advanced product enrichment\n- Quality validation\n- Full RAG pipeline\n\n## šŸŒ URL Processing\n\nUpload from URL instead of file:\n- Set `file_url=\"https://example.com/catalog.pdf\"`\n- Leave `file` parameter empty\n- System downloads and processes automatically\n\n## šŸ¤– AI Model Selection\n\nChoose discovery model:\n- `discovery_model=\"claude-vision\"` - Claude Sonnet 4.5 Vision (best quality, RECOMMENDED)\n- `discovery_model=\"gpt-vision\"` - GPT-4o Vision (fast, good quality)\n- `discovery_model=\"claude-haiku-vision\"` - Claude Haiku Vision (fastest, lower cost)\n\n## šŸ’¬ Agent Prompts\n\nUse natural language instructions:\n- `agent_prompt=\"extract all products\"` - Enhanced with product extraction details\n- `agent_prompt=\"search for NOVA\"` - Enhanced with search context\n- `agent_prompt=\"find certificates\"` - Enhanced with certificate extraction details\n\n## āœ… Response Example\n\n```json\n{\n \"job_id\": \"550e8400-e29b-41d4-a716-446655440000\",\n \"document_id\": \"660e8400-e29b-41d4-a716-446655440001\",\n \"status\": \"pending\",\n \"message\": \"Document upload successful. Processing started.\",\n \"status_url\": \"/api/rag/documents/job/550e8400-e29b-41d4-a716-446655440000\",\n \"processing_mode\": \"standard\",\n \"categories\": [\"products\", \"certificates\"],\n \"estimated_time\": \"2-5 minutes\"\n}\n```\n\n## āš ļø Error Codes\n\n- **400 Bad Request**: Invalid parameters (missing file/URL, invalid mode, unsupported file type)\n- **401 Unauthorized**: Missing or invalid authentication\n- **413 Payload Too Large**: File exceeds size limit (100MB)\n- **415 Unsupported Media Type**: Non-PDF file uploaded\n- **500 Internal Server Error**: Processing initialization failed\n- **503 Service Unavailable**: Background job queue full\n\n## šŸ“ Limits\n\n- **Max file size**: 100MB\n- **Max concurrent jobs**: 5 per workspace\n- **Supported formats**: PDF only\n- **URL download timeout**: 60 seconds","operationId":"upload_document_api_rag_documents_upload_post","requestBody":{"content":{"multipart/form-data":{"schema":{"allOf":[{"$ref":"#/components/schemas/Body_upload_document_api_rag_documents_upload_post"}],"title":"Body"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/documents/job/{job_id}":{"get":{"tags":["Documents"],"summary":"Get Job Status","description":"Get the status of an async document processing job with checkpoint information.\n\nALWAYS queries the database first as the source of truth, then optionally merges\nwith in-memory data for additional real-time details.\n\nReturns:\n - Job status and progress (from database)\n - Latest checkpoint information\n - Detailed metadata including AI usage, chunks, images, products\n - In-memory state comparison (if available)","operationId":"get_job_status_api_rag_documents_job__job_id__get","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/JobInfoResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/jobs/{job_id}/checkpoints":{"get":{"tags":["Documents"],"summary":"Get Job Checkpoints","description":"Get all checkpoints for a job.\n\nReturns:\n - List of all checkpoints with stage, data, and metadata\n - Checkpoint count\n - Processing timeline","operationId":"get_job_checkpoints_api_rag_jobs__job_id__checkpoints_get","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/CheckpointListResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/jobs/{job_id}/restart":{"post":{"tags":["Documents"],"summary":"Restart Job From Checkpoint","description":"Manually restart a job from its last checkpoint.\n\nThis endpoint allows manual recovery of stuck or failed jobs.\nThe job will resume from the last successful checkpoint.","operationId":"restart_job_from_checkpoint_api_rag_jobs__job_id__restart_post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/StatusResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/documents/job/{job_id}/resume":{"post":{"tags":["Documents"],"summary":"Resume Job","description":"Resume a job from its last checkpoint (alias for restart).\n\nThis endpoint is the same as /jobs/{job_id}/restart but with a more intuitive name.","operationId":"resume_job_api_rag_documents_job__job_id__resume_post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/StatusResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/documents/{document_id}/reprocess":{"post":{"tags":["RAG"],"summary":"Reprocess Document","description":"Full Stage 0ā†’4.7 reprocess of an existing document without re-uploading\nthe PDF. Use this to validate pipeline changes against a known document.\n\nFlow:\n 1. Find the latest background_jobs row for this document_id.\n 2. Resolve the PDF on disk (from /tmp/pdf_processor_{doc_id}/).\n 3. If `clear_intermediate=True` (default): delete derived data\n (products, chunks, images, checkpoints, catalog_layout,\n catalog_legends) so the new run starts from a clean slate.\n The `documents` row itself is preserved.\n 4. Create a NEW background_jobs row (status=pending, progress=0).\n 5. Launch `process_document_with_discovery()` as a background task.\n 6. Return the new job_id + handoff URL for the monitor.\n\nQuery params:\n clear_intermediate (bool, default True): whether to wipe derived data.\n Set False for a \"resume fresh\" that just starts a new job against\n existing intermediate state ā€” useful if you want to test idempotency.","operationId":"reprocess_document_api_rag_documents__document_id__reprocess_post","parameters":[{"name":"document_id","in":"path","required":true,"schema":{"type":"string","title":"Document Id"}},{"name":"clear_intermediate","in":"query","required":false,"schema":{"type":"boolean","default":true,"title":"Clear Intermediate"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/documents/jobs":{"get":{"tags":["Documents"],"summary":"List Jobs","description":"List all background jobs with optional filtering and sorting.\n\nArgs:\n limit: Maximum number of jobs to return (default: 10)\n offset: Number of jobs to skip (default: 0)\n status_filter: Filter by status (pending, processing, completed, failed, interrupted)\n sort: Sort order (created_at:desc, created_at:asc, progress:desc, progress:asc)\n\nReturns:\n List of jobs with status, progress, and metadata","operationId":"list_jobs_api_rag_documents_jobs_get","parameters":[{"name":"limit","in":"query","required":false,"schema":{"type":"integer","default":10,"title":"Limit"}},{"name":"offset","in":"query","required":false,"schema":{"type":"integer","default":0,"title":"Offset"}},{"name":"status_filter","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Status Filter"}},{"name":"sort","in":"query","required":false,"schema":{"type":"string","default":"created_at:desc","title":"Sort"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ListDataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/documents/jobs/{job_id}":{"delete":{"tags":["Documents"],"summary":"Delete Job","description":"Delete a job and ALL its associated data.\n\nThis endpoint performs complete cleanup including:\n1. Job record from background_jobs table\n2. Document record (if exists)\n3. All chunks from document_chunks\n4. All embeddings from vecs collections\n5. All images from document_images\n6. All products\n7. Files from storage buckets\n8. Checkpoints\n9. Temporary files\n10. In-memory job_storage\n\nArgs:\n job_id: The unique identifier of the job to delete\n\nReturns:\n Success message with deletion statistics\n\nRaises:\n HTTPException: If job not found or deletion fails","operationId":"delete_job_api_rag_documents_jobs__job_id__delete","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/StatusResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/chunks":{"get":{"tags":["RAG"],"summary":"Get Chunks","description":"Get chunks for a document with embeddings.\n\nArgs:\n document_id: Document ID to filter chunks\n limit: Maximum number of chunks to return\n offset: Pagination offset\n include_embeddings: Whether to include embeddings (default: True)\n\nReturns:\n List of chunks with metadata and embeddings","operationId":"get_chunks_api_rag_chunks_get","parameters":[{"name":"document_id","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by document ID","title":"Document Id"},"description":"Filter by document ID"},{"name":"limit","in":"query","required":false,"schema":{"type":"integer","maximum":1000,"minimum":1,"description":"Maximum number of chunks to return","default":100,"title":"Limit"},"description":"Maximum number of chunks to return"},{"name":"offset","in":"query","required":false,"schema":{"type":"integer","minimum":0,"description":"Number of chunks to skip","default":0,"title":"Offset"},"description":"Number of chunks to skip"},{"name":"include_embeddings","in":"query","required":false,"schema":{"type":"boolean","description":"Include embeddings in response","default":true,"title":"Include Embeddings"},"description":"Include embeddings in response"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ListDataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/images":{"get":{"tags":["RAG"],"summary":"Get Images","description":"Get images for a document.\n\nArgs:\n document_id: Document ID to filter images\n limit: Maximum number of images to return\n offset: Pagination offset\n\nReturns:\n List of images with metadata","operationId":"get_images_api_rag_images_get","parameters":[{"name":"document_id","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by document ID","title":"Document Id"},"description":"Filter by document ID"},{"name":"limit","in":"query","required":false,"schema":{"type":"integer","maximum":1000,"minimum":1,"description":"Maximum number of images to return","default":100,"title":"Limit"},"description":"Maximum number of images to return"},{"name":"offset","in":"query","required":false,"schema":{"type":"integer","minimum":0,"description":"Number of images to skip","default":0,"title":"Offset"},"description":"Number of images to skip"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ListDataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/products":{"get":{"tags":["RAG"],"summary":"Get Products","description":"Get products for a document.\n\nArgs:\n document_id: Document ID to filter products\n limit: Maximum number of products to return\n offset: Pagination offset\n include_tables: Whether to include tables in the response\n\nReturns:\n List of products with metadata, optionally including tables","operationId":"get_products_api_rag_products_get","parameters":[{"name":"document_id","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by document ID","title":"Document Id"},"description":"Filter by document ID"},{"name":"limit","in":"query","required":false,"schema":{"type":"integer","maximum":1000,"minimum":1,"description":"Maximum number of products to return","default":100,"title":"Limit"},"description":"Maximum number of products to return"},{"name":"offset","in":"query","required":false,"schema":{"type":"integer","minimum":0,"description":"Number of products to skip","default":0,"title":"Offset"},"description":"Number of products to skip"},{"name":"include_tables","in":"query","required":false,"schema":{"type":"boolean","description":"Include tables in product response","default":true,"title":"Include Tables"},"description":"Include tables in product response"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ListDataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/embeddings":{"get":{"tags":["RAG"],"summary":"Get Embeddings","description":"Get embeddings for a document.\n\nReturns presence summary for image embeddings (read from document_images\nhas_*_slig boolean flags ā€” VECS is the canonical store) and the actual\ntext embedding vectors for document_chunks.\n\nArgs:\n document_id: Document ID to filter embeddings\n embedding_type: Optional type filter (text, visual, color, texture, style, material, understanding)\n limit: Maximum number of embeddings to return\n offset: Pagination offset\n\nReturns:\n List of embeddings (text embeddings inline, image embeddings as presence flags)","operationId":"get_embeddings_api_rag_embeddings_get","parameters":[{"name":"document_id","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by document ID","title":"Document Id"},"description":"Filter by document ID"},{"name":"embedding_type","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by embedding type (text, visual, color, texture, style, material, understanding)","title":"Embedding Type"},"description":"Filter by embedding type (text, visual, color, texture, style, material, understanding)"},{"name":"limit","in":"query","required":false,"schema":{"type":"integer","maximum":1000,"minimum":1,"description":"Maximum number of embeddings to return","default":100,"title":"Limit"},"description":"Maximum number of embeddings to return"},{"name":"offset","in":"query","required":false,"schema":{"type":"integer","minimum":0,"description":"Number of embeddings to skip","default":0,"title":"Offset"},"description":"Number of embeddings to skip"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ListDataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/relevancies":{"get":{"tags":["RAG"],"summary":"Get Relevancies","description":"Get chunk-image relevancy relationships for a document.\n\nArgs:\n document_id: Document ID to filter relevancies\n limit: Maximum number of relevancies to return\n offset: Pagination offset\n min_score: Minimum relevance score threshold\n\nReturns:\n List of chunk-image relationships with relevance scores","operationId":"get_relevancies_api_rag_relevancies_get","parameters":[{"name":"document_id","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by document ID","title":"Document Id"},"description":"Filter by document ID"},{"name":"limit","in":"query","required":false,"schema":{"type":"integer","maximum":1000,"minimum":1,"description":"Maximum number of relevancies to return","default":100,"title":"Limit"},"description":"Maximum number of relevancies to return"},{"name":"offset","in":"query","required":false,"schema":{"type":"integer","minimum":0,"description":"Number of relevancies to skip","default":0,"title":"Offset"},"description":"Number of relevancies to skip"},{"name":"min_score","in":"query","required":false,"schema":{"type":"number","maximum":1.0,"minimum":0.0,"description":"Minimum relevance score","default":0.0,"title":"Min Score"},"description":"Minimum relevance score"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/RelevancyListResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/query":{"post":{"tags":["Documents"],"summary":"Query Documents","description":"**šŸ¤– CONSOLIDATED QUERY ENDPOINT - Text-Based RAG Query**\n\nThis endpoint replaces:\n- `/api/documents/{id}/query` ā†’ Use with `document_ids` filter\n- `/api/documents/{id}/summarize` ā†’ Use with summarization prompt\n\n## šŸŽÆ Query Capabilities\n\n### Text Query (Implemented) āœ…\n- Pure text-based RAG with advanced retrieval\n- Semantic search with reranking\n- Best for: Factual questions, information retrieval, summarization\n\n## šŸ“ Examples\n\n### Text Query (Default)\n```bash\ncurl -X POST \"/api/rag/documents/query\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"query\": \"What are the dimensions of the NOVA product?\",\n \"workspace_id\": \"your-workspace-id\",\n \"top_k\": 5\n }'\n```\n\n### Document-Specific Query\n```bash\ncurl -X POST \"/api/rag/documents/query\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"query\": \"Summarize this document\",\n \"workspace_id\": \"your-workspace-id\",\n \"document_ids\": [\"doc-123\"],\n \"top_k\": 20\n }'\n```\n\n## šŸ”„ Migration from Old Endpoints\n\n**Old:** `POST /api/documents/{id}/query`\n**New:** `POST /api/rag/documents/query` with `document_ids` filter\n\n**Old:** `POST /api/documents/{id}/summarize`\n**New:** `POST /api/rag/documents/query` with summarization prompt","operationId":"query_documents_api_rag_query_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__api__documents__query_routes__QueryRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__api__documents__query_routes__QueryResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/chat":{"post":{"tags":["Documents"],"summary":"Chat With Documents","description":"Conversational interface for document Q&A.\n\nThis endpoint maintains conversation context and provides\ncontextual responses based on the document collection.\n\n## šŸ“ Example\n\n```bash\ncurl -X POST \"/api/rag/documents/chat\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"message\": \"What products do you have for outdoor use?\",\n \"workspace_id\": \"your-workspace-id\",\n \"conversation_id\": \"conv-123\",\n \"top_k\": 5\n }'\n```","operationId":"chat_with_documents_api_rag_chat_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__api__documents__query_routes__ChatRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ChatResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/search":{"post":{"tags":["Documents"],"summary":"Search Documents","description":"**šŸ” SEARCH ENDPOINT - Multi-Vector Search with AI Query Understanding**\n\n## šŸŽÆ Supported Search Strategies\n\n### Multi-Vector Search (`strategy=\"multi_vector\"`) - ā­ DEFAULT & RECOMMENDED āœ…\n- šŸŽÆ **ENHANCED**: 7-vector fusion search + JSONB metadata filtering\n- **Embeddings Combined:**\n - Text (15%) - Voyage AI 1024D semantic understanding\n - Visual (15%) - SLIG 768D visual similarity\n - Understanding (20%) - Voyage AI 1024D from Qwen3-VL vision analysis\n - Color (12.5%) - SLIG 768D color palette matching\n - Texture (12.5%) - SLIG 768D texture pattern matching\n - Style (12.5%) - SLIG 768D design style matching\n - Material (12.5%) - SLIG 768D material type matching\n- **+ JSONB Metadata Filtering**: Supports `material_filters` for property-based filtering\n- **+ Query Understanding**: āœ… **ENABLED BY DEFAULT** - Auto-extracts filters from natural language\n- **Performance**: Fast (~250-350ms with query understanding, ~200-300ms without)\n- **Best For:** ALL queries - comprehensive, accurate, fast\n- **Example:** \"waterproof ceramic tiles for outdoor patio, matte finish\"\n\n### Material Property Search (`strategy=\"material\"`) āœ…\n- JSONB-based filtering with AND/OR logic\n- Requires `material_filters` in request body\n- Best for: Filtering by specific material properties\n- Uses direct database queries (no LLM required)\n\n### Image Similarity Search (`strategy=\"image\"`) āœ…\n- Visual similarity using CLIP embeddings\n- Requires `image_url` or `image_base64` in request body\n- Best for: Finding visually similar products\n- Uses VECS vector database with HNSW indexing\n\n\n\n## šŸ“ Examples\n\n### Multi-Vector Search (ā­ DEFAULT - Recommended for all queries)\n```bash\ncurl -X POST \"/api/rag/documents/search\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"query\": \"modern minimalist furniture\", \"workspace_id\": \"xxx\", \"top_k\": 10}'\n```\n\n### Multi-Vector with Natural Language Filters\n```bash\ncurl -X POST \"/api/rag/documents/search\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"query\": \"waterproof ceramic tiles for outdoor patio, matte finish\", \"workspace_id\": \"xxx\", \"top_k\": 10}'\n# AI automatically extracts: material_type=ceramic, properties=waterproof, application=outdoor, finish=matte\n```\n\n### Material Property Search\n```bash\ncurl -X POST \"/api/rag/documents/search?strategy=material\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"workspace_id\": \"xxx\", \"material_filters\": {\"material_type\": \"fabric\", \"color\": [\"red\", \"blue\"]}, \"top_k\": 10}'\n```\n\n### Image Similarity Search\n```bash\ncurl -X POST \"/api/rag/documents/search?strategy=image\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"workspace_id\": \"xxx\", \"image_url\": \"https://example.com/image.jpg\", \"top_k\": 10}'\n```\n\n## šŸ“Š Response Example\n```json\n{\n \"query\": \"modern oak furniture\",\n \"enhanced_query\": \"modern oak furniture\",\n \"results\": [\n {\n \"id\": \"product_uuid_1\",\n \"name\": \"Modern Oak Dining Table\",\n \"description\": \"Contemporary oak furniture...\",\n \"score\": 0.92,\n \"final_score\": 0.85,\n \"strategy_count\": 4,\n \"strategies\": [\"semantic\", \"vector\", \"multi_vector\", \"hybrid\"]\n }\n ],\n \"total_results\": 10,\n \"search_type\": \"all\",\n \"processing_time\": 0.223,\n \"search_metadata\": {\n \"strategies_executed\": 4,\n \"strategies_successful\": 4,\n \"strategies_failed\": 0,\n \"strategy_breakdown\": {\n \"semantic\": {\"count\": 3, \"success\": true},\n \"vector\": {\"count\": 2, \"success\": true},\n \"multi_vector\": {\"count\": 4, \"success\": true},\n \"hybrid\": {\"count\": 5, \"success\": true}\n },\n \"parallel_execution\": true,\n \"parallel_processing_time\": 0.017\n }\n}\n```\n\n## āš” Performance Characteristics\n\n| Strategy | Typical Time | Max Time | Notes |\n|----------|-------------|----------|-------|\n| semantic | 100-150ms | 300ms | Indexed, MMR diversity |\n| vector | 50-100ms | 200ms | Fastest, pure similarity |\n| multi_vector | 200-300ms | 500ms | 3 embeddings, sequential scan for 2048-dim |\n| hybrid | 120-180ms | 350ms | Semantic + full-text search |\n| material | 30-50ms | 100ms | JSONB indexed |\n| image | 100-150ms | 300ms | CLIP indexed |\n| **all (parallel)** | **200-300ms** | **500ms** | **3-4x faster than sequential** |\n\n## šŸ”„ Migration from Old Endpoints\n\n**Old:** `POST /api/search/semantic`\n**New:** `POST /api/rag/documents/search?strategy=semantic`\n\n**Old:** `POST /api/search/similarity`\n**New:** `POST /api/rag/documents/search?strategy=vector`\n\n**Old:** `POST /api/unified-search`\n**New:** `POST /api/rag/documents/search` (same functionality, clearer naming)\n\n## āš ļø Error Codes\n\n- **400 Bad Request**: Invalid parameters (missing query, invalid strategy, etc.)\n- **401 Unauthorized**: Missing or invalid authentication\n- **404 Not Found**: Workspace not found\n- **500 Internal Server Error**: Search processing failed\n- **503 Service Unavailable**: RAG service not available\n\n## šŸŽÆ Rate Limits\n\n- **60 requests/minute** per user\n- **1000 requests/hour** per workspace\n- Parallel execution (`strategy=\"all\"`) counts as 1 request","operationId":"search_documents_api_rag_search_post","parameters":[{"name":"strategy","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Search strategy: 'multi_vector' (default and only supported strategy)","default":"multi_vector","title":"Strategy"},"description":"Search strategy: 'multi_vector' (default and only supported strategy)"},{"name":"enable_query_understanding","in":"query","required":false,"schema":{"type":"boolean","description":"šŸ§  AI query parsing to automatically extract filters from natural language (e.g., 'waterproof ceramic tiles for outdoor patio, matte finish' ā†’ auto-extracts material_type, properties, finish, etc.). Set to false to disable.","default":true,"title":"Enable Query Understanding"},"description":"šŸ§  AI query parsing to automatically extract filters from natural language (e.g., 'waterproof ceramic tiles for outdoor patio, matte finish' ā†’ auto-extracts material_type, properties, finish, etc.). Set to false to disable."}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__api__documents__query_routes__SearchRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__api__documents__query_routes__SearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/documents/documents/{document_id}/content":{"get":{"tags":["Documents"],"summary":"Get Document Content","description":"Get complete document content with all AI analysis results.\n\nReturns comprehensive document data including:\n- Document metadata\n- All chunks with embeddings\n- All images with AI analysis (CLIP, Qwen, Claude)\n- All products created from the document\n- Complete AI model usage statistics","operationId":"get_document_content_api_rag_documents_documents__document_id__content_get","parameters":[{"name":"document_id","in":"path","required":true,"schema":{"type":"string","title":"Document Id"}},{"name":"include_chunks","in":"query","required":false,"schema":{"type":"boolean","description":"Include document chunks","default":true,"title":"Include Chunks"},"description":"Include document chunks"},{"name":"include_images","in":"query","required":false,"schema":{"type":"boolean","description":"Include document images","default":true,"title":"Include Images"},"description":"Include document images"},{"name":"include_products","in":"query","required":false,"schema":{"type":"boolean","description":"Include products created from document","default":false,"title":"Include Products"},"description":"Include products created from document"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DocumentContentResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/health":{"get":{"tags":["RAG"],"summary":"Rag Health Check","description":"Health check for RAG services.\n\nThis endpoint checks the health of all RAG-related services\nincluding embedding service and vector store.","operationId":"rag_health_check_api_rag_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HealthCheckResponse"}}}}}}},"/api/rag/stats":{"get":{"tags":["RAG"],"summary":"Get Rag Statistics","description":"Get RAG system statistics.\n\nThis endpoint provides statistics about the RAG system including\ndocument counts, embedding statistics, and performance metrics.","operationId":"get_rag_statistics_api_rag_stats_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/StatsResponse"}}}}}}},"/api/rag/workspace-stats":{"get":{"tags":["RAG"],"summary":"Get Workspace Statistics","description":"Get comprehensive workspace statistics including VECS embedding counts.\n\nReturns counts for:\n- Products\n- Chunks\n- Images\n- Text embeddings (from embeddings table)\n- Image embeddings (from VECS)\n- Total embeddings (text + image)","operationId":"get_workspace_statistics_api_rag_workspace_stats_get","parameters":[{"name":"workspace_id","in":"query","required":true,"schema":{"type":"string","title":"Workspace Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/StatsResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/job/{job_id}/ai-tracking":{"get":{"tags":["Documents"],"summary":"Get Job Ai Tracking","description":"Get detailed AI model tracking information for a job.\n\nReturns comprehensive metrics on:\n- Which AI models were used (QWEN, Anthropic, CLIP, OpenAI)\n- Confidence scores and results\n- Token usage and processing time\n- Success/failure rates\n- Per-stage breakdown","operationId":"get_job_ai_tracking_api_rag_job__job_id__ai_tracking_get","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/AITrackingResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/job/{job_id}/ai-tracking/stage/{stage}":{"get":{"tags":["Documents"],"summary":"Get Job Ai Tracking By Stage","description":"Get AI model tracking information for a specific processing stage.\n\nArgs:\n job_id: Job identifier\n stage: Processing stage (classification, boundary_detection, embedding, etc.)\n\nReturns:\n Detailed metrics for the specified stage","operationId":"get_job_ai_tracking_by_stage_api_rag_job__job_id__ai_tracking_stage__stage__get","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}},{"name":"stage","in":"path","required":true,"schema":{"type":"string","title":"Stage"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/AITrackingResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/job/{job_id}/ai-tracking/model/{model_name}":{"get":{"tags":["Documents"],"summary":"Get Job Ai Tracking By Model","description":"Get AI model tracking information for a specific AI model.\n\nArgs:\n job_id: Job identifier\n model_name: AI model name (QWEN, Anthropic, CLIP, OpenAI)\n\nReturns:\n Statistics for the specified AI model","operationId":"get_job_ai_tracking_by_model_api_rag_job__job_id__ai_tracking_model__model_name__get","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}},{"name":"model_name","in":"path","required":true,"schema":{"type":"string","title":"Model Name"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/AITrackingResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/admin/stuck-jobs/analyze/{job_id}":{"get":{"tags":["RAG"],"summary":"Analyze Stuck Job","description":"Analyze a stuck job to determine root cause and get recommendations.\n\nReturns detailed analysis including:\n- Root cause identification\n- Bottleneck stage\n- Stage-by-stage timing analysis\n- Recovery options\n- Optimization recommendations","operationId":"analyze_stuck_job_api_rag_admin_stuck_jobs_analyze__job_id__get","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/StuckJobsResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/rag/admin/stuck-jobs/statistics":{"get":{"tags":["RAG"],"summary":"Get Stuck Job Statistics","description":"Get overall statistics about stuck jobs.\n\nReturns:\n- Total stuck jobs\n- Stage breakdown (which stages jobs get stuck at)\n- Most common stuck stage\n- Historical patterns","operationId":"get_stuck_job_statistics_api_rag_admin_stuck_jobs_statistics_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/StuckJobsResponse"}}}}}}},"/api/rag/search/knowledge-base":{"post":{"tags":["Documents"],"summary":"Search Knowledge Base","description":"šŸ” Search existing knowledge base without uploading a PDF.\n\nUses the same **7-vector fusion search** as the main search endpoint, combining:\n- Text (15%) - Voyage AI 1024D semantic understanding\n- Visual (15%) - SLIG 768D visual similarity\n- Understanding (20%) - Voyage AI 1024D from Qwen3-VL analysis\n- Color (12.5%) - SLIG 768D color palette matching\n- Texture (12.5%) - SLIG 768D texture pattern matching\n- Style (12.5%) - SLIG 768D design style matching\n- Material (12.5%) - SLIG 768D material type matching\n\nPerforms unified semantic search across:\n- **Products** (with all metadata, embeddings, and material properties)\n- **Document entities** (certificates, logos, specifications)\n- **Chunks** (text content from PDFs with category tags)\n- **Images** (visual content with SLIG embeddings)\n\nSupports:\n- Category filtering (product, certificate, logo, specification, general)\n- Entity type filtering (certificate, logo, specification)\n- Material property filtering via metadata\n\nExample queries:\n- \"waterproof ceramic tiles with matte finish\"\n- \"ISO 9001 certificates\"\n- \"company logos\"\n- \"installation specifications\"","operationId":"search_knowledge_base_api_rag_search_knowledge_base_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/app__api__documents__query_routes__KnowledgeBaseSearchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/KnowledgeBaseSearchResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/anthropic/images/validate":{"post":{"tags":["Anthropic Claude"],"summary":"Validate Image With Claude","description":"**šŸ” Image Validation - Claude 3.5 Sonnet Vision**\n\nValidate image quality and match to product groups using Claude Vision.\n\n## šŸŽÆ What It Does\n\n- **Quality Assessment**: Rates image quality (0-1 scale) based on clarity, lighting, composition\n- **Content Analysis**: Describes what's visible in the image\n- **Material Identification**: Identifies materials present\n- **Product Matching**: Matches image to relevant product groups\n- **Issue Detection**: Identifies quality issues or concerns\n- **Recommendations**: Suggests improvements\n\n## šŸ“ Request Example\n\n```json\n{\n \"image_id\": \"550e8400-e29b-41d4-a716-446655440000\",\n \"image_url\": \"https://example.com/product.jpg\",\n \"product_groups\": [\"Furniture\", \"Lighting\", \"Textiles\"],\n \"workspace_id\": \"workspace_uuid\"\n}\n```\n\n## āœ… Response Example\n\n```json\n{\n \"image_id\": \"550e8400-e29b-41d4-a716-446655440000\",\n \"validation_status\": \"valid\",\n \"quality_score\": 0.92,\n \"product_associations\": [\n {\n \"product_group\": \"Furniture\",\n \"confidence\": 0.95,\n \"reasoning\": \"Clear oak dining table visible with professional lighting\"\n }\n ],\n \"issues\": [],\n \"recommendations\": [\"Consider adding lifestyle context shots\"],\n \"processing_time_ms\": 1234.5\n}\n```\n\n## šŸ“Š Validation Status\n\n- **valid**: Quality score ā‰„ 0.7 - Image is production-ready\n- **needs_review**: Quality score 0.5-0.7 - Manual review recommended\n- **invalid**: Quality score < 0.5 - Image needs improvement\n\n## āš ļø Error Codes\n\n- **400 Bad Request**: Invalid parameters (missing image_id/image_url)\n- **404 Not Found**: Image not found in database\n- **500 Internal Server Error**: Claude API call failed\n- **503 Service Unavailable**: Claude API unavailable\n\n## šŸ“ Limits\n\n- **Max image size**: 10MB\n- **Timeout**: 30 seconds\n- **Rate limit**: 60 requests/minute\n- **Model**: claude-sonnet-4-6","operationId":"validate_image_with_claude_api_v1_anthropic_images_validate_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ImageValidationRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ImageValidationResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/anthropic/products/enrich":{"post":{"tags":["Anthropic Claude"],"summary":"Enrich Product With Claude","description":"Enrich product data using Claude 3.5 Sonnet.\n\nGenerates descriptions, extracts specifications, and identifies related products.","operationId":"enrich_product_with_claude_api_v1_anthropic_products_enrich_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ProductEnrichmentRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ProductEnrichmentResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/anthropic/test/claude-integration":{"post":{"tags":["Anthropic Claude"],"summary":"Test Claude Integration","description":"Test Claude Vision API integration.","operationId":"test_claude_integration_api_v1_anthropic_test_claude_integration_post","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DataResponse"}}}}}}},"/api/products/create-from-chunks":{"post":{"tags":["Products","Products"],"summary":"Create products from document chunks using two-stage AI classification","description":"Advanced two-stage product creation system with intelligent AI model selection.\n\n **Two-Stage Classification System:**\n\n **Stage 1: Fast Filtering (Claude 4.5 Haiku)**\n - Text-only classification for initial candidate identification\n - Batch processing for efficiency\n - Low-cost, high-speed filtering\n - Identifies potential product chunks\n\n **Stage 2: Deep Enrichment (Claude 4.5 Sonnet)**\n - Detailed product metadata extraction\n - Image analysis and validation\n - Comprehensive feature extraction\n - High-accuracy enrichment\n\n **Performance Benefits:**\n - āš” 60% faster processing vs single-stage approach\n - šŸ’° 40% reduced API costs through intelligent model selection\n - šŸŽÆ Higher accuracy through specialized model usage\n - šŸ“¦ Batch processing reduces API call overhead\n\n **Processing Flow:**\n 1. Fetch all chunks for document\n 2. Filter by minimum length criteria\n 3. Stage 1: Haiku classifies chunks (batch)\n 4. Stage 2: Sonnet enriches confirmed products\n 5. Create products in database\n 6. Return detailed metrics\n\n **Example Request:**\n ```json\n {\n \"document_id\": \"69cba085-9c2d-405c-aff2-8a20caf0b568\",\n \"workspace_id\": \"ffafc28b-1b8b-4b0d-b226-9f9a6154004e\",\n \"max_products\": null,\n \"min_chunk_length\": 100\n }\n ```\n\n **Example Response:**\n ```json\n {\n \"success\": true,\n \"products_created\": 12,\n \"products_failed\": 0,\n \"chunks_processed\": 45,\n \"total_chunks\": 150,\n \"eligible_chunks\": 45,\n \"stage1_candidates\": 15,\n \"stage1_time\": 2.5,\n \"stage2_time\": 8.3,\n \"total_time\": 10.8,\n \"message\": \"Successfully created 12 products from 45 chunks in 10.8s\"\n }\n ```\n\n **Parameters:**\n - `document_id`: UUID of processed document (required)\n - `workspace_id`: UUID of workspace (default: ffafc28b-1b8b-4b0d-b226-9f9a6154004e)\n - `max_products`: Maximum products to create (null = unlimited)\n - `min_chunk_length`: Minimum chunk content length (default: 100)\n\n **Performance:**\n - Typical: 10-15 seconds for 50 chunks\n - Stage 1: ~2-3 seconds (batch processing)\n - Stage 2: ~8-12 seconds (detailed enrichment)\n\n **Use Cases:**\n - Automated product extraction from PDFs\n - Bulk product creation from catalogs\n - Material database population\n - Product metadata enrichment\n\n **Error Codes:**\n - 200: Success\n - 400: Invalid request parameters\n - 404: Document not found\n - 500: Processing failed (check logs)\n\n **Rate Limits:**\n - 5 requests/minute (processing intensive)","operationId":"create_products_from_chunks_api_products_create_from_chunks_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ProductCreationRequest"}}},"required":true},"responses":{"200":{"description":"Products created successfully","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ProductCreationResponse"}}}},"400":{"description":"Invalid request parameters"},"404":{"description":"Document not found"},"500":{"description":"Processing failed"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/products/create-from-layout":{"post":{"tags":["Products"],"summary":"Create Products From Layout","description":"Create products from layout-based candidates (legacy method).\n\nThis endpoint uses the original layout-based product detection method\nfor comparison with the new two-stage classification system.\n\nArgs:\n request: Product creation request parameters\n supabase_client: Supabase client dependency\n \nReturns:\n ProductCreationResponse: Results from layout-based creation\n \nRaises:\n HTTPException: If the operation fails","operationId":"create_products_from_layout_api_products_create_from_layout_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ProductCreationRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ProductCreationResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/products/batch-categorize":{"post":{"tags":["Products"],"summary":"Batch Categorize Products","description":"Batch re-categorize products using Claude Haiku.\n\nFetches products for the workspace (optionally only those without a\nmaterial_category in metadata), calls _classify_product for each,\nand updates metadata.material_category + metadata.zone_intent in DB.","operationId":"batch_categorize_products_api_products_batch_categorize_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/BatchCategorizeRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/BatchCategorizeResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/products/health":{"get":{"tags":["Products"],"summary":"Products Health Check","description":"Health check endpoint for the products API.\n\nReturns:\n Dict[str, Any]: Health status information","operationId":"products_health_check_api_products_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ServiceHealthResponse"}}}}}}},"/api/document-entities/":{"get":{"tags":["Document Entities"],"summary":"Get Document Entities","description":"Get all document entities for a workspace.\n\nSupports filtering by:\n- entity_type: certificate, logo, specification, marketing, bank_statement\n- factory_name: Filter by specific factory\n- factory_group: Filter by factory group\n\nExample agentic query: \"Get all certifications for CastellĆ³n Factory\"\nā†’ GET /api/document-entities?workspace_id=xxx&entity_type=certificate&factory_name=CastellĆ³n Factory","operationId":"get_document_entities_api_document_entities__get","parameters":[{"name":"workspace_id","in":"query","required":true,"schema":{"type":"string","description":"Workspace ID","title":"Workspace Id"},"description":"Workspace ID"},{"name":"entity_type","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by entity type (certificate, logo, specification, etc.)","title":"Entity Type"},"description":"Filter by entity type (certificate, logo, specification, etc.)"},{"name":"factory_name","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by factory name","title":"Factory Name"},"description":"Filter by factory name"},{"name":"factory_group","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by factory group","title":"Factory Group"},"description":"Filter by factory group"},{"name":"limit","in":"query","required":false,"schema":{"type":"integer","description":"Maximum number of entities to return","default":100,"title":"Limit"},"description":"Maximum number of entities to return"},{"name":"offset","in":"query","required":false,"schema":{"type":"integer","description":"Number of entities to skip","default":0,"title":"Offset"},"description":"Number of entities to skip"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/DocumentEntityResponse"},"title":"Response Get Document Entities Api Document Entities Get"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/document-entities/{entity_id}":{"get":{"tags":["Document Entities"],"summary":"Get Document Entity","description":"Get a specific document entity by ID.","operationId":"get_document_entity_api_document_entities__entity_id__get","parameters":[{"name":"entity_id","in":"path","required":true,"schema":{"type":"string","title":"Entity Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DocumentEntityResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/document-entities/product/{product_id}":{"get":{"tags":["Document Entities"],"summary":"Get Entities For Product","description":"Get all document entities linked to a specific product.\n\nExample agentic query: \"Get certifications for product NOVA\"\nā†’ First get product ID for NOVA, then:\nā†’ GET /api/document-entities/product/{product_id}?entity_type=certificate","operationId":"get_entities_for_product_api_document_entities_product__product_id__get","parameters":[{"name":"product_id","in":"path","required":true,"schema":{"type":"string","title":"Product Id"}},{"name":"entity_type","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by entity type","title":"Entity Type"},"description":"Filter by entity type"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/DocumentEntityResponse"},"title":"Response Get Entities For Product Api Document Entities Product Product Id Get"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/document-entities/factory/{factory_name}":{"get":{"tags":["Document Entities"],"summary":"Get Entities By Factory","description":"Get all document entities for a specific factory.\n\nExample agentic query: \"Get all certifications for CastellĆ³n Factory\"\nā†’ GET /api/document-entities/factory/CastellĆ³n Factory?entity_type=certificate","operationId":"get_entities_by_factory_api_document_entities_factory__factory_name__get","parameters":[{"name":"factory_name","in":"path","required":true,"schema":{"type":"string","title":"Factory Name"}},{"name":"entity_type","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"description":"Filter by entity type","title":"Entity Type"},"description":"Filter by entity type"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/DocumentEntityResponse"},"title":"Response Get Entities By Factory Api Document Entities Factory Factory Name Get"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/document-entities/relationships/product/{product_id}":{"get":{"tags":["Document Entities"],"summary":"Get Product Document Relationships","description":"Get all document entity relationships for a specific product.","operationId":"get_product_document_relationships_api_document_entities_relationships_product__product_id__get","parameters":[{"name":"product_id","in":"path","required":true,"schema":{"type":"string","title":"Product Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/ProductDocumentRelationshipResponse"},"title":"Response Get Product Document Relationships Api Document Entities Relationships Product Product Id Get"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/embeddings/clip-image":{"post":{"tags":["Embeddings"],"summary":"Generate Clip Image Embedding","description":"**šŸ–¼ļø Visual Image Embedding - Powered by SigLIP2**\n\nGenerate 768-dimensional visual embedding using SLIG (SigLIP2) cloud endpoint\nfor superior material image similarity search.\n\n## šŸŽÆ Use Cases\n\n- Visual product search\n- Material similarity matching\n- Multimodal search (combine with text embeddings)\n- Image clustering and categorization\n\n## šŸ“ Request Example\n\n```json\n{\n \"image_data\": \"data:image/jpeg;base64,/9j/4AAQSkZJRg...\",\n \"model\": \"siglip2-so400m-patch14-384\"\n}\n```\n\n## āœ… Response Example\n\n```json\n{\n \"embedding\": [0.123, -0.456, 0.789, ...],\n \"dimension\": 512,\n \"model\": \"siglip-so400m-patch14-384\",\n \"processing_time_ms\": 234.5\n}\n```\n\n## šŸ“Š Technical Details\n\n- **Model**: Google SigLIP ViT-SO400M-14-384\n- **Dimension**: 512\n- **Accuracy**: +19-29% improvement over CLIP on material images\n- **Normalization**: L2 normalized (unit vector)\n- **Distance Metric**: Cosine similarity\n- **Processing Time**: 150-400ms\n\n## āš ļø Error Codes\n\n- **400 Bad Request**: Invalid base64 image data\n- **413 Payload Too Large**: Image exceeds 10MB\n- **415 Unsupported Media Type**: Unsupported image format\n- **500 Internal Server Error**: Embedding generation failed\n- **503 Service Unavailable**: SigLIP model not available\n\n## šŸ“ Limits\n\n- **Max image size**: 10MB\n- **Supported formats**: JPEG, PNG, WebP\n- **Rate limit**: 100 requests/minute","operationId":"generate_clip_image_embedding_api_embeddings_clip_image_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClipImageRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/EmbeddingResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/embeddings/clip-text":{"post":{"tags":["Embeddings"],"summary":"Generate Clip Text Embedding","description":"**šŸ“ Text Embedding - Powered by Voyage AI**\n\nGenerate text embedding using Voyage AI (primary) with OpenAI fallback.\nSupports input_type optimization for better retrieval quality.\n\n## šŸŽÆ Use Cases\n\n- Document indexing (use input_type=\"document\")\n- Search queries (use input_type=\"query\")\n- Semantic search and retrieval\n- Text-to-image search\n- Multimodal search\n\n## šŸ“ Request Example\n\n```json\n{\n \"text\": \"modern minimalist oak dining table\",\n \"model\": \"voyage-3.5\",\n \"input_type\": \"document\",\n \"dimensions\": 1024\n}\n```\n\n## āœ… Response Example\n\n```json\n{\n \"embedding\": [0.234, -0.567, 0.891, ...],\n \"dimensions\": 1024,\n \"model\": \"voyage-3.5\",\n \"success\": true\n}\n```\n\n## šŸ“Š Technical Details\n\n- **Primary Model**: Voyage AI voyage-3.5\n- **Fallback Model**: OpenAI text-embedding-3-small\n- **Default Dimension**: 1024 (Voyage AI)\n- **Supported Dimensions**: 256, 512, 1024, 2048 (Voyage) or 512, 1536 (OpenAI)\n- **Input Types**: \"document\" (for indexing), \"query\" (for search)\n- **Normalization**: L2 normalized (unit vector)\n- **Distance Metric**: Cosine similarity\n- **Processing Time**: 100-300ms\n\n## šŸ’” Usage Pattern\n\n1. For indexing documents:\n ```json\n {\"text\": \"...\", \"input_type\": \"document\", \"dimensions\": 1024}\n ```\n2. For search queries:\n ```json\n {\"text\": \"...\", \"input_type\": \"query\", \"dimensions\": 1024}\n ```\n3. Search using cosine similarity:\n ```sql\n SELECT * FROM chunks\n ORDER BY text_embedding <=> '[your_embedding]'\n LIMIT 10\n ```\n\n## āš ļø Error Codes\n\n- **400 Bad Request**: Empty or invalid text\n- **500 Internal Server Error**: Embedding generation failed\n- **503 Service Unavailable**: Both Voyage AI and OpenAI unavailable\n\n## šŸ“ Limits\n\n- **Max text length**: 8000 tokens (Voyage AI), 8191 tokens (OpenAI)\n- **Rate limit**: 100 requests/minute","operationId":"generate_clip_text_embedding_api_embeddings_clip_text_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClipTextRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/EmbeddingResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/embeddings/health":{"get":{"tags":["Embeddings"],"summary":"Health Check","description":"Health check endpoint for embeddings service.","operationId":"health_check_api_embeddings_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/EmbeddingHealthResponse"}}}}}}},"/api/monitoring/supabase-status":{"get":{"tags":["Monitoring","Monitoring"],"summary":"Get comprehensive Supabase resource usage and health status","description":"Monitor all Supabase resources including database, storage, and usage limits.\n\n **Returns:**\n - **Database Statistics**: Row counts for all monitored tables\n - **Storage Statistics**: Size and file counts per bucket\n - **Resource Limits**: Free tier limits and current usage percentages\n - **Health Status**: Overall system health (healthy, notice, warning, critical)\n - **Warnings**: Alerts when approaching resource limits\n\n **Monitored Tables:**\n - documents, document_chunks, document_images\n - embeddings, products, materials_catalog\n - background_jobs\n\n **Monitored Buckets:**\n - pdf-tiles, pdf-documents, material-images\n\n **Health Status Levels:**\n - `healthy`: <50% usage\n - `notice`: 50-80% usage\n - `warning`: 80-90% usage\n - `critical`: >90% usage (upgrade needed)\n\n **Example Response:**\n ```json\n {\n \"success\": true,\n \"timestamp\": \"2025-11-08T16:30:00Z\",\n \"health_status\": \"warning\",\n \"database\": {\n \"tables\": [\n {\"table\": \"documents\", \"rows\": 150},\n {\"table\": \"document_chunks\", \"rows\": 5000}\n ],\n \"total_rows\": 5150\n },\n \"storage\": {\n \"buckets\": [\n {\n \"bucket\": \"pdf-documents\",\n \"files\": 150,\n \"size_gb\": 0.45\n }\n ],\n \"total_files\": 150,\n \"total_size_gb\": 0.85\n },\n \"limits\": {\n \"storage_gb\": 1.0,\n \"database_size_gb\": 0.5\n },\n \"usage\": {\n \"storage_percent\": 85.0,\n \"storage_remaining_gb\": 0.15\n },\n \"warnings\": [\n {\n \"type\": \"warning\",\n \"resource\": \"storage\",\n \"message\": \"Storage usage is at 85.0% - Consider upgrading soon.\",\n \"usage_percent\": 85.0\n }\n ]\n }\n ```\n\n **Use Cases:**\n - Admin dashboard monitoring\n - Automated alerts for resource limits\n - Capacity planning\n - Cost optimization\n\n **Performance:**\n - Typical: 1-2 seconds (queries all tables and buckets)\n - Recommended: Cache for 5-10 minutes\n\n **Rate Limits:**\n - 10 requests/minute\n\n **Error Codes:**\n - 200: Success\n - 500: Failed to retrieve status (check logs)","operationId":"get_supabase_status_api_monitoring_supabase_status_get","responses":{"200":{"description":"Comprehensive resource status","content":{"application/json":{"schema":{"$ref":"#/components/schemas/PerformanceMetricsResponse"}}}},"500":{"description":"Failed to retrieve status"}}}},"/api/monitoring/health":{"get":{"tags":["Monitoring"],"summary":"Health Check","description":"Quick health check for monitoring systems.\n\nReturns:\n - Overall system health\n - Database connectivity\n - Storage connectivity","operationId":"health_check_api_monitoring_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ServiceHealthResponse"}}}}}}},"/api/monitoring/storage-estimate":{"get":{"tags":["Monitoring"],"summary":"Estimate Storage For Upload","description":"Estimate if there's enough storage for a PDF upload.\n\nArgs:\n file_size_mb: Size of PDF file in MB\n estimated_images: Estimated number of images to extract\n \nReturns:\n - Whether upload is safe\n - Estimated storage needed\n - Current available storage","operationId":"estimate_storage_for_upload_api_monitoring_storage_estimate_get","parameters":[{"name":"file_size_mb","in":"query","required":true,"schema":{"type":"number","title":"File Size Mb"}},{"name":"estimated_images","in":"query","required":false,"schema":{"type":"integer","default":50,"title":"Estimated Images"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/StorageEstimateResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/monitoring/pdf/health":{"get":{"tags":["Monitoring","Health & Monitoring"],"summary":"Pdf Health","description":"PDF service health check endpoint.\n\nReturns:\n Health status of the PDF processing service","operationId":"pdf_health_api_monitoring_pdf_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/PDFHealthResponse"}}}}}}},"/api/v1/ai-metrics/summary":{"get":{"tags":["AI Metrics","AI Metrics"],"summary":"Get comprehensive AI usage metrics and cost tracking","description":"Monitor AI model usage, costs, performance, and quality metrics across all services.\n\n **Metrics Provided:**\n\n **1. Summary Statistics**\n - Total AI calls made\n - Total cost (USD)\n - Total tokens consumed\n - Average latency (ms)\n - Average confidence score\n - Fallback rate (%)\n\n **2. Model Usage Breakdown**\n - Per-model call counts\n - Per-model costs\n - Per-model token usage\n - Per-model latency\n - Per-model confidence scores\n - Fallback counts\n\n **3. Task Breakdown**\n - Calls per task type (classification, extraction, validation, etc.)\n - Cost per task type\n - Average confidence per task\n - Models used per task\n\n **4. Confidence Distribution**\n - Distribution across confidence ranges (0.0-0.2, 0.2-0.4, etc.)\n - Percentage of calls in each range\n - Quality assessment\n\n **5. Recent Calls**\n - Last 10 AI calls with full details\n - Model used, task, cost, latency, confidence\n\n **Time Periods:**\n - `1h`: Last hour\n - `24h`: Last 24 hours (default)\n - `7d`: Last 7 days\n - `30d`: Last 30 days\n - `all`: All time\n\n **Example Response:**\n ```json\n {\n \"summary\": {\n \"total_calls\": 1250,\n \"total_cost\": 12.45,\n \"total_tokens\": 450000,\n \"average_latency_ms\": 850.5,\n \"average_confidence\": 0.87,\n \"fallback_rate\": 0.05,\n \"time_period\": \"24h\"\n },\n \"model_usage\": [\n {\n \"model\": \"claude-sonnet-4-6\",\n \"call_count\": 500,\n \"total_cost\": 8.50,\n \"total_tokens\": 300000,\n \"average_latency_ms\": 1200.0,\n \"average_confidence\": 0.92,\n \"fallback_count\": 10\n },\n {\n \"model\": \"claude-3-5-haiku-20241022\",\n \"call_count\": 750,\n \"total_cost\": 3.95,\n \"total_tokens\": 150000,\n \"average_latency_ms\": 500.0,\n \"average_confidence\": 0.84,\n \"fallback_count\": 50\n }\n ],\n \"task_breakdown\": [\n {\n \"task\": \"product_classification\",\n \"call_count\": 600,\n \"total_cost\": 5.20,\n \"average_confidence\": 0.89,\n \"models_used\": [\"claude-3-5-haiku-20241022\"]\n }\n ],\n \"confidence_distribution\": [\n {\"range\": \"0.8-1.0\", \"count\": 1000, \"percentage\": 80.0},\n {\"range\": \"0.6-0.8\", \"count\": 200, \"percentage\": 16.0},\n {\"range\": \"0.4-0.6\", \"count\": 50, \"percentage\": 4.0}\n ],\n \"recent_calls\": [...]\n }\n ```\n\n **Use Cases:**\n - Cost monitoring and optimization\n - Model performance comparison\n - Quality assurance (confidence tracking)\n - Fallback rate monitoring\n - Budget forecasting\n - Performance optimization\n\n **Performance:**\n - Typical: 200-500ms\n - Cached: 1 minute\n\n **Rate Limits:**\n - 30 requests/minute\n\n **Error Codes:**\n - 200: Success\n - 400: Invalid time period\n - 500: Failed to retrieve metrics","operationId":"get_ai_metrics_summary_api_v1_ai_metrics_summary_get","parameters":[{"name":"time_period","in":"query","required":false,"schema":{"type":"string","description":"Time period: 1h, 24h, 7d, 30d, all","default":"24h","title":"Time Period"},"description":"Time period: 1h, 24h, 7d, 30d, all"}],"responses":{"200":{"description":"Comprehensive AI metrics","content":{"application/json":{"schema":{"$ref":"#/components/schemas/AIMetricsResponse"}}}},"400":{"description":"Invalid time period"},"500":{"description":"Failed to retrieve metrics"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-metrics/external-service-usage":{"get":{"tags":["AI Metrics"],"summary":"Get external service usage and credit consumption","description":"Returns usage, cost, and credit data for external services (Twilio, Apollo, Hunter, ZeroBounce, Firecrawl) from ai_usage_logs.","operationId":"get_external_service_usage_api_v1_ai_metrics_external_service_usage_get","parameters":[{"name":"time_period","in":"query","required":false,"schema":{"type":"string","description":"Time period: 1h, 24h, 7d, 30d, all","default":"24h","title":"Time Period"},"description":"Time period: 1h, 24h, 7d, 30d, all"}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-metrics/job/{job_id}":{"get":{"tags":["AI Metrics"],"summary":"Get Job Ai Metrics","description":"Get AI metrics for a specific job (PDF processing, etc.).","operationId":"get_job_ai_metrics_api_v1_ai_metrics_job__job_id__get","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-services/classify-document":{"post":{"tags":["AI Services","AI Services"],"summary":"Classify document content into semantic categories","description":"AI-powered content classification using Claude 4.5 Haiku for fast, accurate categorization.\n\n **Classification Categories:**\n\n **1. Product** (`product`)\n - Product information and specifications\n - Features and benefits\n - Technical specifications\n - Product variants and options\n - Pricing and availability\n\n **2. Supporting Information** (`supporting`)\n - Technical details and guides\n - Certifications and compliance\n - Installation instructions\n - Maintenance guides\n - Warranty information\n\n **3. Administrative** (`administrative`)\n - Company information\n - Legal notices and disclaimers\n - Contact information\n - Terms and conditions\n - Privacy policies\n\n **4. Transitional** (`transitional`)\n - Table of contents\n - Headers and footers\n - Page numbers\n - Navigation elements\n - Section dividers\n\n **Example Request:**\n ```json\n {\n \"content\": \"NOVA Oak Flooring - Premium engineered wood flooring with 3mm oak veneer...\",\n \"context\": {\n \"page_number\": 5,\n \"has_images\": true,\n \"section\": \"Products\"\n },\n \"job_id\": \"job_123\"\n }\n ```\n\n **Example Response:**\n ```json\n {\n \"success\": true,\n \"classification\": {\n \"category\": \"product\",\n \"confidence\": 0.95,\n \"reasoning\": \"Content contains product name, specifications, and features\",\n \"metadata\": {\n \"product_indicators\": [\"specifications\", \"features\", \"pricing\"],\n \"quality_score\": 0.92\n }\n }\n }\n ```\n\n **Use Cases:**\n - PDF content categorization\n - Automated document organization\n - Product extraction pipelines\n - Content filtering\n\n **Performance:**\n - Typical: 300-500ms per classification\n - Batch processing: Use `/classify-batch` for better performance\n\n **Accuracy:**\n - Average confidence: 0.87\n - Fallback rate: <5%\n\n **Rate Limits:**\n - 60 requests/minute\n\n **Error Codes:**\n - 200: Success\n - 400: Invalid content or parameters\n - 500: Classification failed","operationId":"classify_document_api_v1_ai_services_classify_document_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClassifyRequest"}}},"required":true},"responses":{"200":{"description":"Classification successful","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClassificationResponse"}}}},"400":{"description":"Invalid request"},"500":{"description":"Classification failed"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-services/classify-batch":{"post":{"tags":["AI Services"],"summary":"Classify Batch","description":"Classify multiple document contents in parallel.\n\nEfficient for processing multiple sections at once.","operationId":"classify_batch_api_v1_ai_services_classify_batch_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClassifyBatchRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/BatchClassificationResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-services/detect-boundaries":{"post":{"tags":["AI Services"],"summary":"Detect Boundaries","description":"Detect product boundaries in document chunks.\n\nUses semantic similarity, structural markers, and page breaks\nto identify where one product ends and another begins.","operationId":"detect_boundaries_api_v1_ai_services_detect_boundaries_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/DetectBoundariesRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/BoundaryDetectionResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-services/group-by-product":{"post":{"tags":["AI Services"],"summary":"Group By Product","description":"Detect boundaries and group chunks into products.\n\nReturns product groups ready for extraction.","operationId":"group_by_product_api_v1_ai_services_group_by_product_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/DetectBoundariesRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ProductGroupingResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-services/validate-product":{"post":{"tags":["AI Services"],"summary":"Validate Product","description":"Validate product extraction quality.\n\nChecks:\n- Minimum content requirements\n- Substantive content (not just headers/footers)\n- Distinguishing features\n- Associated assets (images, specs)\n- Semantic coherence","operationId":"validate_product_api_v1_ai_services_validate_product_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ValidateProductRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ValidationResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-services/consensus-validate":{"post":{"tags":["AI Services"],"summary":"Consensus Validate","description":"Validate extraction using multi-model consensus.\n\nRuns 2-3 models in parallel and uses weighted voting\nto determine the most accurate result.\n\nCritical for:\n- Product name extraction\n- Material classification\n- Safety information\n- Compliance data\n- Technical specifications\n- Pricing data","operationId":"consensus_validate_api_v1_ai_services_consensus_validate_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ConsensusValidateRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ValidationResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-services/consensus/is-critical/{task_type}":{"get":{"tags":["AI Services"],"summary":"Check If Critical","description":"Check if a task type requires consensus validation.\n\nReturns True for critical tasks like product_name_extraction,\nmaterial_classification, safety_information, etc.","operationId":"check_if_critical_api_v1_ai_services_consensus_is_critical__task_type__get","parameters":[{"name":"task_type","in":"path","required":true,"schema":{"type":"string","title":"Task Type"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-services/escalation/stats":{"get":{"tags":["AI Services"],"summary":"Get Escalation Stats","description":"Get escalation engine statistics.\n\nReturns metrics on:\n- Total escalations\n- Successful escalations\n- Failed escalations\n- Cost saved/spent","operationId":"get_escalation_stats_api_v1_ai_services_escalation_stats_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/EscalationStatsResponse"}}}}}}},"/api/v1/ai-services/process-pdf-enhanced":{"post":{"tags":["AI Services"],"summary":"Process Pdf Enhanced","description":"Process PDF with all enhanced AI services.\n\nThis endpoint is for testing the full integration.\nIn production, use the main PDF processing endpoint.","operationId":"process_pdf_enhanced_api_v1_ai_services_process_pdf_enhanced_post","parameters":[{"name":"document_id","in":"query","required":true,"schema":{"type":"string","title":"Document Id"}},{"name":"job_id","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DataResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/v1/ai-services/health":{"get":{"tags":["AI Services"],"summary":"Health Check","description":"Health check for AI services.","operationId":"health_check_api_v1_ai_services_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ServiceHealthResponse"}}}}}}},"/admin/extraction-prompts":{"get":{"tags":["Admin - Prompts"],"summary":"List Prompts","description":"**šŸ“‹ List Extraction Prompts - Admin Configuration**\n\nList all AI extraction prompts configured for a workspace with optional filtering.\n\n## šŸŽÆ Use Cases\n\n- View all configured extraction prompts\n- Filter prompts by processing stage\n- Filter prompts by extraction category\n- Audit prompt configurations\n\n## šŸ“ Query Parameters\n\n- **workspace_id** (required): Workspace UUID\n- **stage** (optional): Filter by stage (discovery, extraction, validation)\n- **category** (optional): Filter by category (products, certificates, logos, specifications)\n\n## āœ… Response Example\n\n```json\n[\n {\n \"id\": \"prompt-uuid-1\",\n \"workspace_id\": \"workspace-uuid\",\n \"stage\": \"discovery\",\n \"category\": \"products\",\n \"prompt_template\": \"Identify all products in this PDF...\",\n \"system_prompt\": \"You are an expert at identifying products...\",\n \"is_custom\": true,\n \"version\": 2,\n \"created_by\": \"user-uuid\",\n \"created_at\": \"2025-11-08T10:00:00Z\",\n \"updated_at\": \"2025-11-08T12:00:00Z\"\n }\n]\n```\n\n## āš ļø Error Codes\n\n- **400 Bad Request**: Invalid workspace_id\n- **401 Unauthorized**: Authentication required\n- **403 Forbidden**: Insufficient permissions\n- **500 Internal Server Error**: Database error","operationId":"list_prompts_admin_extraction_prompts_get","security":[{"HTTPBearer":[]}],"parameters":[{"name":"workspace_id","in":"query","required":true,"schema":{"type":"string","title":"Workspace Id"}},{"name":"stage","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Stage"}},{"name":"category","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Category"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/PromptResponse"},"title":"Response List Prompts Admin Extraction Prompts Get"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/admin/extraction-prompts/{stage}/{category}":{"get":{"tags":["Admin - Prompts"],"summary":"Get Prompt","description":"Get specific prompt by stage and category","operationId":"get_prompt_admin_extraction_prompts__stage___category__get","security":[{"HTTPBearer":[]}],"parameters":[{"name":"stage","in":"path","required":true,"schema":{"type":"string","title":"Stage"}},{"name":"category","in":"path","required":true,"schema":{"type":"string","title":"Category"}},{"name":"workspace_id","in":"query","required":true,"schema":{"type":"string","title":"Workspace Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/PromptResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"put":{"tags":["Admin - Prompts"],"summary":"Update Prompt","description":"Update prompt with audit trail\n\nPath Parameters:\n- stage: Extraction stage (discovery, chunking, image_analysis, entity_creation)\n- category: Content category (products, certificates, logos, specifications)\n\nQuery Parameters:\n- workspace_id: Workspace ID\n\nBody:\n- prompt_template: New prompt template\n- system_prompt: Optional system prompt\n- change_reason: Reason for the change","operationId":"update_prompt_admin_extraction_prompts__stage___category__put","security":[{"HTTPBearer":[]}],"parameters":[{"name":"stage","in":"path","required":true,"schema":{"type":"string","title":"Stage"}},{"name":"category","in":"path","required":true,"schema":{"type":"string","title":"Category"}},{"name":"workspace_id","in":"query","required":true,"schema":{"type":"string","title":"Workspace Id"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/UpdatePromptRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/PromptResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/admin/extraction-prompts/history/{prompt_id}":{"get":{"tags":["Admin - Prompts"],"summary":"Get Prompt History","description":"Get change history for a prompt","operationId":"get_prompt_history_admin_extraction_prompts_history__prompt_id__get","security":[{"HTTPBearer":[]}],"parameters":[{"name":"prompt_id","in":"path","required":true,"schema":{"type":"string","title":"Prompt Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/PromptHistoryResponse"},"title":"Response Get Prompt History Admin Extraction Prompts History Prompt Id Get"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/admin/extraction-prompts/test":{"post":{"tags":["Admin - Prompts"],"summary":"Test Prompt","description":"Test a prompt before saving\n\nBody:\n- stage: Extraction stage\n- category: Content category\n- prompt_template: Prompt to test\n- test_content: Sample content to test with","operationId":"test_prompt_admin_extraction_prompts_test_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/TestPromptRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/TestPromptResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}},"security":[{"HTTPBearer":[]}]}},"/admin/extraction-config":{"get":{"tags":["Admin - Config"],"summary":"Get Extraction Config","description":"Get extraction configuration for workspace","operationId":"get_extraction_config_admin_extraction_config_get","security":[{"HTTPBearer":[]}],"parameters":[{"name":"workspace_id","in":"query","required":true,"schema":{"type":"string","title":"Workspace Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ExtractionConfigResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"put":{"tags":["Admin - Config"],"summary":"Update Extraction Config","description":"Update extraction configuration\n\nQuery Parameters:\n- workspace_id: Workspace ID\n\nBody: Configuration fields to update","operationId":"update_extraction_config_admin_extraction_config_put","security":[{"HTTPBearer":[]}],"parameters":[{"name":"workspace_id","in":"query","required":true,"schema":{"type":"string","title":"Workspace Id"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/UpdateExtractionConfigRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ExtractionConfigResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/saved-searches/check-duplicates":{"post":{"tags":["Saved Searches"],"summary":"Check For Duplicates","description":"Check if a search query has duplicates and get merge suggestions.\n\nUses AI-powered semantic analysis to find similar searches while\nrespecting contextual differences (floor vs wall, indoor vs outdoor).\n\nReturns:\n- has_duplicate: Whether a similar search exists\n- should_auto_merge: Whether to auto-merge (95%+ similarity)\n- merge_suggestion: Details about the suggested merge (if applicable)","operationId":"check_for_duplicates_api_saved_searches_check_duplicates_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/CheckDuplicatesRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/CheckDuplicatesResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/saved-searches/{search_id}/merge":{"post":{"tags":["Saved Searches"],"summary":"Merge Into Existing","description":"Merge a new search into an existing saved search.\n\nStrategy:\n- Keeps most specific query\n- Merges attributes (union, no conflicts)\n- Updates filters to be more inclusive\n- Increments merge_count\n- Updates last_merged_at 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Merges data from source products into target product\n2. Transfers all relationships (images, chunks, etc.)\n3. Deletes source products\n4. 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Product discovery (Claude/GPT from markdown)\n 2. Product creation with text embeddings (Voyage AI, inline)\n 3. Image download ā†’ classification (Qwen/Claude) ā†’ 5 SLIG embeddings\n 4. 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product","operationId":"attach_document_to_product_api_kb_attachments_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/AttachProductRequest"}}},"required":true},"responses":{"201":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/AttachmentResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/kb/documents/{doc_id}/attachments":{"get":{"tags":["Knowledge Base"],"summary":"Get document attachments","description":"Get all product attachments for a document","operationId":"get_document_attachments_api_kb_documents__doc_id__attachments_get","parameters":[{"name":"doc_id","in":"path","required":true,"schema":{"type":"string","title":"Doc Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/AttachmentResponse"},"title":"Response Get Document Attachments Api Kb Documents Doc Id Attachments Get"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/kb/products/{product_id}/documents":{"get":{"tags":["Knowledge Base"],"summary":"Get product documents","description":"Get all knowledge base documents attached to a product","operationId":"get_product_documents_api_kb_products__product_id__documents_get","parameters":[{"name":"product_id","in":"path","required":true,"schema":{"type":"string","title":"Product Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/KBDocResponse"},"title":"Response Get Product Documents Api Kb Products Product Id Documents Get"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/kb/search":{"post":{"tags":["Knowledge Base"],"summary":"Search Kb Documents","description":"Search knowledge base documents using semantic, full-text, or hybrid search.\n\n**Architecture:**\n1. Frontend calls MIVAA API with search query\n2. MIVAA generates embedding for query using OpenAI (text-embedding-3-small)\n3. MIVAA calls Supabase `kb_match_docs()` RPC function with query embedding\n4. Supabase performs vector similarity search using pgvector `<=>` operator\n5. Returns ranked results with similarity scores\n\n**Why MIVAA Backend is Required:**\n- Document embeddings already stored in `kb_docs.text_embedding` (generated when doc created)\n- Search only generates ONE embedding (for the query)\n- Cannot generate embeddings in Supabase RPC (requires OpenAI API call)\n- Uses pgvector's optimized cosine similarity for fast search\n\n**Search Types:**\n- **semantic**: Vector similarity using pgvector cosine distance\n - Generates query embedding via OpenAI\n - Compares against stored document embeddings\n - Returns results with similarity scores (0.0 - 1.0)\n - Minimum threshold: 0.5\n- **full_text**: ILIKE-based keyword matching\n - Searches title and content fields\n - Case-insensitive\n- **hybrid**: Combination of semantic + full-text\n - Weighted scoring for best results\n\n**Example Request:**\n```json\n{\n \"workspace_id\": \"uuid\",\n \"query\": \"sustainable wood materials\",\n \"search_type\": \"semantic\",\n \"limit\": 20\n}\n```\n\n**Example Response:**\n```json\n{\n \"results\": [\n {\n \"id\": \"uuid\",\n \"title\": \"Sustainable Wood Guide\",\n \"similarity\": 0.87,\n \"content\": \"...\",\n \"category_id\": \"uuid\"\n }\n ],\n \"search_time_ms\": 145.3,\n \"total_results\": 5\n}\n```","operationId":"search_kb_documents_api_kb_search_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/SearchKBRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SearchKBResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/kb/health":{"get":{"tags":["Knowledge Base"],"summary":"Kb Health Check","description":"Health check endpoint for Knowledge Base API.","operationId":"kb_health_check_api_kb_health_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/KBHealthResponse"}}}}}}},"/api/category-prototypes/populate":{"post":{"tags":["Category Prototypes"],"summary":"Populate Category Prototypes","description":"Populate all material categories with prototype descriptions and embeddings\n\nThis endpoint:\n1. Generates CLIP text embeddings for predefined category descriptions\n2. Updates material_categories table with prototypes\n3. Returns summary of operation","operationId":"populate_category_prototypes_api_category_prototypes_populate_post","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/PrototypePopulationResponse"}}}}}}},"/api/category-prototypes/verify":{"get":{"tags":["Category Prototypes"],"summary":"Verify Prototypes","description":"Verify that prototypes were populated correctly","operationId":"verify_prototypes_api_category_prototypes_verify_get","responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/PrototypeVerifyResponse"}}}}}}},"/api/internal/classify-images/{job_id}":{"post":{"tags":["Internal Pipeline"],"summary":"Classify Images","description":"Classify images as material or non-material using Qwen Vision + Claude validation.\n\nThis endpoint:\n1. Uses vision model for fast initial classification\n2. Validates uncertain cases (confidence < threshold) with Claude Sonnet\n3. Returns separated lists of material and non-material images\n\nAI Configuration (Optional):\n- classification_primary_model: Primary classification model (default: Qwen3-VL-8B)\n- classification_validation_model: Validation model (default: Claude Sonnet 4.5)\n- classification_confidence_threshold: Threshold for validation (default: 0.7)\n- classification_temperature: Temperature setting (default: 0.1)\n- classification_max_tokens: Max tokens for responses (default: 512)\n\nExample with custom AI config:\n```json\n{\n \"job_id\": \"abc123\",\n \"extracted_images\": [...],\n \"ai_config\": {\n \"classification_primary_model\": \"Qwen/Qwen3-VL-8B-Instruct\",\n \"classification_validation_model\": \"claude-sonnet-4-6\",\n \"classification_confidence_threshold\": 0.8\n }\n}\n```\n\nArgs:\n job_id: Job ID for tracking\n request: Classification request with extracted images and optional AI config\n\nReturns:\n ClassifyImagesResponse with material and non-material images","operationId":"classify_images_api_internal_classify_images__job_id__post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClassifyImagesRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ClassifyImagesResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/upload-images/{job_id}":{"post":{"tags":["Internal Pipeline"],"summary":"Upload Images","description":"Upload material images to Supabase Storage.\n\nThis endpoint:\n1. Uploads material images to Supabase Storage bucket\n2. Returns uploaded images with storage URLs\n\nArgs:\n job_id: Job ID for tracking\n request: Upload request with material images\n\nReturns:\n UploadImagesResponse with uploaded images","operationId":"upload_images_api_internal_upload_images__job_id__post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/UploadImagesRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/UploadImagesResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/save-images-db/{job_id}":{"post":{"tags":["Internal Pipeline"],"summary":"Save Images To Db","description":"Save images to database and generate visual embeddings (SigLIP/CLIP).\n\nThis endpoint:\n1. Saves images to document_images table\n2. Generates visual embeddings using SigLIP (primary) or CLIP (fallback)\n3. Creates 5 specialized embeddings per image (visual, color, texture, style, material)\n4. Saves embeddings to database table AND VECS collection\n\nAI Configuration (Optional):\n- visual_embedding_primary: Primary visual model (default: SigLIP ViT-SO400M)\n- visual_embedding_fallback: Fallback visual model (default: CLIP ViT-B/32)\n\nExample with custom AI config:\n```json\n{\n \"job_id\": \"abc123\",\n \"material_images\": [...],\n \"document_id\": \"doc123\",\n \"workspace_id\": \"ws123\",\n \"ai_config\": {\n \"visual_embedding_primary\": \"google/siglip-so400m-patch14-384\",\n \"visual_embedding_fallback\": \"openai/clip-vit-base-patch32\"\n }\n}\n```\n\nArgs:\n job_id: Job ID for tracking\n request: Save request with material images and optional AI config\n\nReturns:\n SaveImagesResponse with counts and model used","operationId":"save_images_to_db_api_internal_save_images_db__job_id__post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/SaveImagesRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/SaveImagesResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/create-chunks/{job_id}":{"post":{"tags":["Internal Pipeline"],"summary":"Create Chunks","description":"Create semantic chunks and generate text embeddings.\n\nThis endpoint:\n1. Creates semantic chunks from extracted text\n2. Saves chunks to database\n3. Generates text embeddings for each chunk\n4. Creates chunk-to-product relationships\n5. **Prevents duplicates** - skips if chunks already exist\n\n**Use Cases:**\n- Regenerate chunks after text extraction updates\n- Create chunks for documents that failed chunking\n- Manual chunk generation for testing\n\nArgs:\n job_id: Job ID for tracking\n request: Chunking request with extracted text\n supabase_client: Supabase client for database operations\n\nReturns:\n CreateChunksResponse with counts","operationId":"create_chunks_api_internal_create_chunks__job_id__post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/CreateChunksRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/CreateChunksResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/create-relationships/{job_id}":{"post":{"tags":["Internal Pipeline"],"summary":"Create Relationships","description":"Create all relationships between chunks, images, and products.\n\nThis endpoint:\n1. Creates chunk-to-image relationships based on embedding similarity\n2. Creates product-to-image relationships based on page ranges\n\nArgs:\n job_id: Job ID for tracking\n request: Relationships request\n\nReturns:\n CreateRelationshipsResponse with counts","operationId":"create_relationships_api_internal_create_relationships__job_id__post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/CreateRelationshipsRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/CreateRelationshipsResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/extract-metadata/{job_id}":{"post":{"tags":["Internal Pipeline"],"summary":"Extract Metadata","description":"Extract comprehensive metadata from PDF text for products.\n\nThis endpoint:\n1. Extracts product-specific text from PDF\n2. Uses AI (Claude or GPT) to extract structured metadata\n3. Enriches product records with extracted metadata\n4. Tracks which model was used and extraction method\n\nArgs:\n job_id: Job ID for tracking\n request: Metadata extraction request with product IDs and PDF text\n\nReturns:\n ExtractMetadataResponse with extraction results","operationId":"extract_metadata_api_internal_extract_metadata__job_id__post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ExtractMetadataRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ExtractMetadataResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/generate-product-embeddings":{"post":{"tags":["Internal Pipeline"],"summary":"Generate Product Embeddings","description":"Generate embeddings for products that don't have them yet.\n\nThis endpoint:\n1. Finds products without embeddings (no associated chunks)\n2. Creates chunks from product name + description\n3. Queues embedding generation jobs\n\n**Use Cases:**\n- Fix missing embeddings from old PDF processing\n- Regenerate embeddings after product updates\n- Bulk embedding generation for imported products\n\nArgs:\n request: Request with workspace_id, optional document_id and product_ids\n\nReturns:\n GenerateProductEmbeddingsResponse with counts and errors","operationId":"generate_product_embeddings_api_internal_generate_product_embeddings_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/GenerateProductEmbeddingsRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/GenerateProductEmbeddingsResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/regenerate-image-embeddings":{"post":{"tags":["Internal Pipeline"],"summary":"Regenerate Image Embeddings","description":"Regenerate visual embeddings for existing images in the database.\n\nThis endpoint:\n1. Fetches existing images from document_images table\n2. Downloads images from Supabase Storage\n3. Generates 5 CLIP embeddings per image (visual, color, texture, style, material)\n4. Generates understanding embedding (1024D) if vision_analysis exists\n5. Saves embeddings to VECS collections\n\n**Use Cases:**\n- Fix missing embeddings from old PDF processing\n- Regenerate embeddings after model upgrades\n- Bulk embedding generation for imported images\n\n**Example Request:**\n```json\n{\n \"workspace_id\": \"ffafc28b-1b8b-4b0d-b226-9f9a6154004e\",\n \"document_id\": \"doc-123\", // Optional: limit to specific document\n \"image_ids\": [\"img-1\", \"img-2\"], // Optional: specific images\n \"force_regenerate\": false // Optional: regenerate even if embeddings exist\n}\n```\n\nArgs:\n request: Request with workspace_id, optional document_id and image_ids\n\nReturns:\n RegenerateImageEmbeddingsResponse with counts and errors","operationId":"regenerate_image_embeddings_api_internal_regenerate_image_embeddings_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/RegenerateImageEmbeddingsRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/RegenerateImageEmbeddingsResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/reset-job/{job_id}":{"post":{"tags":["Internal Pipeline"],"summary":"Reset Job","description":"Reset a stuck / failed / stale job back to `pending` so the scheduler\ncan pick it up again.\n\nHistorical note (2026-04-11): the previous revision wrote\n`status='initialized'` which violated the\n`background_jobs_status_check` CHECK constraint (the DB only accepts\npending / processing / completed / failed / cancelled / interrupted).\nEvery single reset call has been failing with a 500 ever since that\nconstraint was added. Sentry MIVAA-4ZW, MIVAA-4ZV.\n\nAlso: by default this endpoint refuses to reset a job that's already\nin a terminal success state (`completed`). Overwriting a completed\njob means discarding the successful result ā€” almost always a\nmistake. Pass `?force=true` to override when you actually want that.\n\nArgs:\n job_id: Job ID to reset\n force: When true, allow resetting jobs in terminal states\n (`completed`, `failed`, `cancelled`). Defaults to False.\n\nReturns:\n ResetJobResponse with previous status","operationId":"reset_job_api_internal_reset_job__job_id__post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}},{"name":"force","in":"query","required":false,"schema":{"type":"boolean","default":false,"title":"Force"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ResetJobResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/extract-entities":{"post":{"tags":["Internal Pipeline"],"summary":"Extract Entities","description":"Match document entities to products for an already-processed document.\n\nRuns DocumentEntityService.match_entities_to_products() which links\nexisting document_entities records to products by page overlap,\nfactory/manufacturer match, and name similarity.\n\nArgs:\n request: document_id + workspace_id\n\nReturns:\n ExtractEntitiesResponse with match statistics","operationId":"extract_entities_api_internal_extract_entities_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ExtractEntitiesRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ExtractEntitiesResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/generate-entity-embeddings":{"post":{"tags":["Internal Pipeline"],"summary":"Generate Entity Embeddings","description":"Generate text embeddings for all document entities.\n\nRuns DocumentEntityService.generate_entity_embeddings() which creates\nVoyage AI 1024D text embeddings for each entity and stores them in the\nembeddings table with entity_type='entity'.\n\nArgs:\n request: document_id + workspace_id\n\nReturns:\n GenerateEntityEmbeddingsResponse with counts","operationId":"generate_entity_embeddings_api_internal_generate_entity_embeddings_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/GenerateEntityEmbeddingsRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/GenerateEntityEmbeddingsResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/queue-understanding-embeddings":{"post":{"tags":["Internal Pipeline"],"summary":"Queue Understanding Embeddings","description":"Queue Phase 2 understanding embedding generation for a document.\n\nPhase 2 runs Qwen3-VL vision analysis on document images, converts the\nvision_analysis JSON to text, then generates 1024D Voyage AI embeddings\n(understanding embeddings) for semantic spec-based search.\n\nThis is safe to re-run ā€” it skips images that already have understanding embeddings.\n\nArgs:\n request: document_id + workspace_id + priority\n\nReturns:\n QueueUnderstandingEmbeddingsResponse with queued job_id","operationId":"queue_understanding_embeddings_api_internal_queue_understanding_embeddings_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/QueueUnderstandingEmbeddingsRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/QueueUnderstandingEmbeddingsResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/regenerate-text-embeddings":{"post":{"tags":["Internal Pipeline"],"summary":"Regenerate Text Embeddings","description":"Generate Voyage AI text embeddings for document chunks that are missing them.\n\nFetches chunks with null text_embedding, generates 1024D Voyage AI embeddings\ninline, and saves directly to document_chunks.text_embedding.\n\nArgs:\n request: workspace_id, optional document_id/chunk_ids, force_regenerate flag\n\nReturns:\n RegenerateTextEmbeddingsResponse with counts","operationId":"regenerate_text_embeddings_api_internal_regenerate_text_embeddings_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/RegenerateTextEmbeddingsRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/RegenerateTextEmbeddingsResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/validate-pipeline/{job_id}":{"post":{"tags":["Internal Pipeline"],"summary":"Validate Pipeline","description":"Audit pipeline completion status for a job.\n\nChecks each pipeline stage by querying the relevant DB tables and\nreturns a summary of what's complete, what's missing, and what to fix.\n\nArgs:\n job_id: Job ID to validate\n\nReturns:\n ValidatePipelineResponse with per-stage completion status and recommendations","operationId":"validate_pipeline_api_internal_validate_pipeline__job_id__post","parameters":[{"name":"job_id","in":"path","required":true,"schema":{"type":"string","title":"Job Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ValidatePipelineResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/enrich-existing-product/{product_id}":{"post":{"tags":["Internal Pipeline"],"summary":"Enrich Existing Product","description":"Retroactively run Stage 4.7 enrichment (chunk regex + vision_analysis rollup)\non an existing product row. Only fills null/empty fields ā€” never overwrites.\n\nUse this to backfill products created before the Stage 4.7 enrichment pass\nwas added to the pipeline. After the next full run, this endpoint becomes\nunnecessary (the enrichment runs automatically).\n\nArgs:\n product_id: The product UUID to enrich.\n\nReturns:\n Stats about what was filled.","operationId":"enrich_existing_product_api_internal_enrich_existing_product__product_id__post","parameters":[{"name":"product_id","in":"path","required":true,"schema":{"type":"string","title":"Product Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/EnrichExistingProductResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/run-catalog-knowledge/{document_id}":{"post":{"tags":["Internal Pipeline"],"summary":"Run Catalog Knowledge","description":"Standalone runner for Layer 1 (catalog layout analyzer) + Layer 2\n(catalog legend extractor) against a single document.\n\nUse this when you want to (re)process the catalog-wide data ā€” page\nclassification, legend extraction, certification propagation ā€” WITHOUT\nre-running the full Stage 0-4 pipeline. This is the only endpoint that\nre-runs the catalog_legends and certifications propagation for every\nproduct in the document.\n\nQuery param `force=true` bypasses the idempotency check and re-analyzes\neven if `documents.metadata.catalog_layout.analyzed_at` exists.","operationId":"run_catalog_knowledge_api_internal_run_catalog_knowledge__document_id__post","parameters":[{"name":"document_id","in":"path","required":true,"schema":{"type":"string","title":"Document Id"}},{"name":"force","in":"query","required":false,"schema":{"type":"boolean","default":false,"title":"Force"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/RunCatalogKnowledgeResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/internal/document-extraction-status/{document_id}":{"get":{"tags":["Internal Pipeline"],"summary":"Document Extraction Status","description":"Observability endpoint ā€” returns a snapshot of how well a document has\nbeen processed end-to-end. Shows:\n\n - Whether Layer 1 (catalog layout) ran\n - Whether Layer 2 (catalog legends + certs) ran\n - Which legend types were found\n - Global certifications propagated\n - Per-product field coverage (populated / missing critical)\n - Source breakdown (how many fields came from chunks vs vision vs legend)\n - Issues detected (missing legend pages, failed extractions, etc.)\n\nUse this to triage a catalog after ingestion: run it, see which products\nare underfilled and which legends are missing, and decide whether to\nre-run a specific layer or accept the current state.","operationId":"document_extraction_status_api_internal_document_extraction_status__document_id__get","parameters":[{"name":"document_id","in":"path","required":true,"schema":{"type":"string","title":"Document Id"}},{"name":"sample_limit","in":"query","required":false,"schema":{"type":"integer","default":10,"title":"Sample Limit"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/DocumentExtractionStatusResponse"}}}},"500":{"description":"Internal server error"},"404":{"description":"Job not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/admin/prompt-templates":{"get":{"tags":["Admin - Prompt Templates"],"summary":"List Prompt Templates","description":"**šŸ“‹ List Prompt Templates**\n\nList all customizable AI prompts for a workspace.\n\n## Query Parameters\n- **workspace_id** (required): Workspace UUID\n- **stage** (optional): Filter by stage (metadata_extraction, discovery, classification, chunking)\n- **category** (optional): Filter by category (products, certificates, logos, specifications)\n- **industry** (optional): Filter by industry (construction, interior_design, general)\n- **include_inactive** (optional): Include inactive templates (default: false)","operationId":"list_prompt_templates_admin_prompt_templates_get","security":[{"HTTPBearer":[]}],"parameters":[{"name":"workspace_id","in":"query","required":true,"schema":{"type":"string","title":"Workspace Id"}},{"name":"stage","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Stage"}},{"name":"category","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Category"}},{"name":"industry","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Industry"}},{"name":"include_inactive","in":"query","required":false,"schema":{"type":"boolean","default":false,"title":"Include Inactive"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"array","items":{"$ref":"#/components/schemas/PromptTemplateResponse"},"title":"Response List Prompt Templates Admin Prompt Templates Get"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}},"post":{"tags":["Admin - Prompt Templates"],"summary":"Create Prompt Template","description":"**āž• Create Prompt Template**\n\nCreate a new customizable AI prompt template.\n\n## Stages\n- **metadata_extraction**: Extract product metadata from PDFs\n- **discovery**: Discover products in PDFs\n- **classification**: Classify images\n- **chunking**: Semantic text chunking\n\n## Industries\n- **general**: Default for all material types\n- **construction**: Tiles, flooring, construction materials\n- **interior_design**: Furniture, decor, design products","operationId":"create_prompt_template_admin_prompt_templates_post","security":[{"HTTPBearer":[]}],"requestBody":{"required":true,"content":{"application/json":{"schema":{"$ref":"#/components/schemas/CreatePromptTemplateRequest"}}}},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"type":"object","title":"Response Create Prompt Template Admin Prompt Templates Post"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/admin/prompt-templates/{template_id}":{"put":{"tags":["Admin - 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Accepts user feedback text and optional rating\n2. Performs AI-powered sentiment analysis\n3. Stores feedback with sentiment results in database\n4. 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period.\n\nArgs:\n days: Number of days to look back (default: 30)\n workspace_id: Optional workspace filter\n\nReturns:\n ChunkQualityMetrics with aggregated statistics","operationId":"get_chunk_quality_metrics_admin_chunk_quality_metrics_get","parameters":[{"name":"days","in":"query","required":false,"schema":{"type":"integer","maximum":365,"minimum":1,"default":30,"title":"Days"}},{"name":"workspace_id","in":"query","required":false,"schema":{"anyOf":[{"type":"string"},{"type":"null"}],"title":"Workspace Id"}}],"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ChunkQualityMetrics"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/admin/chunk-quality/flagged":{"get":{"tags":["Chunk Quality"],"summary":"Get Flagged Chunks","description":"Get list of flagged chunks for review.\n\nFilters:\n- workspace_id: Filter by 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Firecrawl credits from user's account.","operationId":"check_prices_now_api_v1_price_monitoring_check_now_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/CheckPricesRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/MonitoringActionResponse"}}}},"401":{"description":"Unauthorized"},"403":{"description":"Forbidden"},"404":{"description":"Not found"},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}},"security":[{"HTTPBearer":[]}]}},"/api/v1/price-monitoring/status/{product_id}":{"get":{"tags":["Price Monitoring"],"summary":"Get Monitoring Status","description":"Get monitoring status for a 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logs.","operationId":"log_frontend_error_api_admin_logs_frontend_post","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/FrontendLogRequest"}}},"required":true},"responses":{"200":{"description":"Successful Response","content":{"application/json":{"schema":{"$ref":"#/components/schemas/StatusResponse"}}}},"422":{"description":"Validation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/HTTPValidationError"}}}}}}},"/api/admin/logs":{"get":{"tags":["Admin","Logs"],"summary":"Get Logs","description":"Get system logs from the database.\n\nSupports filtering by:\n- Log level\n- Logger name\n- Job ID\n- Search term in message\n- Time range (hours)\n\nReturns paginated results.","operationId":"get_logs_api_admin_logs_get","parameters":[{"name":"page","in":"query","required":false,"schema":{"type":"integer","minimum":1,"description":"Page number","default":1,"title":"Page"},"description":"Page 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