- Commented out early 'if not update.message' validation at line 723
- This was preventing photo and voice messages from being processed
- process_photo() and process_voice() functions are already implemented
- Now photo/voice updates can reach multimodal handlers before text validation
- Fixes issue where bots showed placeholder messages instead of processing media
- Telegram API returns 400 Bad Request with invalid Markdown characters
- Switched to plain text mode (no parse_mode) to prevent errors
- Photo and voice processing now work correctly
- Fixes issue where vision-8b responses couldn't be sent to users
- Add auto_complex logic: if needs_complex_reasoning, set metadata["provider"] = "cloud_deepseek"
- Add context caching in MemoryClient with 5s TTL
- Add GREENFOOD agent configuration
- This enables automatic routing of complex queries to DeepSeek via explicit_provider_override rule
- Створено AgentConfig для уніфікації конфігурації агентів
- Додано універсальні функції: process_photo(), process_document(), process_voice()
- Створено handle_telegram_webhook() для будь-якого агента
- Рефакторинг telegram_webhook() та helion_telegram_webhook()
- Оновлено process_voice() для використання STT Service
- Додано реєстр агентів AGENT_REGISTRY
- Оновлено health endpoint для показу всіх агентів
- Додано інструкції для додавання нових агентів
- Modified send_telegram_message() to accept bot_token parameter
- DAARWIZZ webhook now uses DAARWIZZ_TELEGRAM_BOT_TOKEN
- Helion webhook now uses HELION_TELEGRAM_BOT_TOKEN
- Fixes issue where DAARWIZZ messages were sent via Helion bot
- Add Helion system prompt with 9 user modes
- Add /helion/telegram/webhook endpoint
- Add Helion configuration to docker-compose.yml
- Add HELION-QUICKSTART.md documentation
- Update .env.example with Helion variables
Helion provides technical support for EcoMiner/BioMiner, tokenomics (ENERGY, 1T, kWt), and DAO governance with RAG verification and 4-level compliance.
RAG Service Implementation:
- Create rag-service/ with full structure (config, document_store, embedding, pipelines)
- Document Store: PostgreSQL + pgvector via Haystack
- Embedding: BAAI/bge-m3 (multilingual, 1024 dim)
- Ingest Pipeline: Convert ParsedDocument to Haystack Documents, embed, index
- Query Pipeline: Retrieve documents, generate answers via DAGI Router
- FastAPI endpoints: /ingest, /query, /health
Tests:
- Unit tests for ingest and query pipelines
- E2E test with example parsed JSON
- Test fixtures with real PARSER output example
Router Integration:
- Add mode='rag_query' routing rule in router-config.yml
- Priority 7, uses local_qwen3_8b for RAG queries
Docker:
- Add rag-service to docker-compose.yml
- Configure dependencies (router, city-db)
- Add model cache volume
Documentation:
- Complete README with API examples
- Integration guides for PARSER and Router
Router Configuration:
- Add mode='qa_build' routing rule in router-config.yml
- Priority 8, uses local_qwen3_8b for Q&A generation
2-Stage Q&A Pipeline Tests:
- Create test_qa_pipeline.py with comprehensive tests
- Test prompt building, JSON parsing, router integration
- Mock DAGI Router responses for testing
Region Mode (Grounding OCR):
- Add region_bbox and region_page parameters to ParseRequest
- Support region mode in local_runtime with bbox in prompt
- Update endpoints to accept region parameters (x, y, width, height, page)
- Validate region parameters and filter pages for region mode
- Pass region_bbox through inference pipeline
Updates:
- Update local_runtime to support region_bbox in prompts
- Update inference.py to pass region_bbox to local_runtime
- Update endpoints.py to handle region mode parameters
Prompt Modes Integration:
- Create local_runtime.py with DOTS_PROMPT_MAP
- Map OutputMode to native dots.ocr prompt modes (prompt_layout_all_en, prompt_ocr, etc.)
- Support dict_promptmode_to_prompt from dots.ocr with fallback prompts
- Add layout_only and region modes to OutputMode enum
2-Stage Q&A Pipeline:
- Create qa_builder.py for 2-stage qa_pairs generation
- Stage 1: PARSER (dots.ocr) → raw JSON via prompt_layout_all_en
- Stage 2: LLM (DAGI Router) → Q&A pairs via mode=qa_build
- Update endpoints.py to use 2-stage pipeline for qa_pairs mode
- Add ROUTER_BASE_URL and ROUTER_TIMEOUT to config
Updates:
- Update inference.py to use local_runtime with native prompts
- Update ollama_client.py to use same prompt map
- Add PROMPT_MODES.md documentation
Model Loader:
- Update model_loader.py with complete dots.ocr loading code
- Proper device detection (CUDA/CPU/MPS) with fallback
- Memory optimization (low_cpu_mem_usage)
- Better error handling and logging
- Support for local model paths and HF Hub
Docker:
- Multi-stage Dockerfile (CPU/CUDA builds)
- docker-compose.yml for parser-service
- .dockerignore for clean builds
- Model cache volume for persistence
Configuration:
- Support DOTS_OCR_MODEL_ID and DEVICE env vars (backward compatible)
- Better defaults and environment variable handling
Deployment:
- Add DEPLOYMENT.md with detailed instructions
- Local deployment (venv)
- Docker Compose deployment
- Ollama runtime setup
- Troubleshooting guide
Integration:
- Add parser-service to main docker-compose.yml
- Configure volumes and networks
- Health checks and dependencies
Ollama Runtime:
- Add ollama_client.py for Ollama API integration
- Support for dots-ocr model via Ollama
- Add OLLAMA_BASE_URL configuration
- Update inference.py to support Ollama runtime (RUNTIME_TYPE=ollama)
- Update endpoints to handle async Ollama calls
- Alternative to local transformers model
RAG Implementation Plan:
- Create TODO-RAG.md with detailed Haystack integration plan
- Document Store setup (pgvector)
- Embedding model selection
- Ingest pipeline (PARSER → RAG)
- Query pipeline (RAG → LLM)
- Integration with DAGI Router
- Bot commands (/upload_doc, /ask_doc)
- Testing strategy
Now supports three runtime modes:
1. Local transformers (RUNTIME_TYPE=local)
2. Ollama (RUNTIME_TYPE=ollama)
3. Dummy (USE_DUMMY_PARSER=true)
G.2.5 - Tests:
- Add pytest test suite with fixtures
- test_preprocessing.py - PDF/image loading, normalization, validation
- test_postprocessing.py - chunks, QA pairs, markdown generation
- test_inference.py - dummy parser and inference functions
- test_api.py - API endpoint tests
- Add pytest.ini configuration
G.1.3 - dots.ocr Integration:
- Update model_loader.py with real model loading code
- Support for AutoModelForVision2Seq and AutoProcessor
- Device handling (CUDA/CPU/MPS) with fallback
- Error handling with dummy fallback option
- Update inference.py with real model inference
- Process images through model
- Generate and decode outputs
- Parse model output to blocks
- Add model_output_parser.py
- Parse JSON or plain text model output
- Convert to structured blocks
- Layout detection support (placeholder)
Dependencies:
- Add pytest, pytest-asyncio, httpx for testing
