Commit Graph

7 Commits

Author SHA1 Message Date
Apple
4601c6fca8 feat: add Vision Encoder service + Vision RAG implementation
- Vision Encoder Service (OpenCLIP ViT-L/14, GPU-accelerated)
  - FastAPI app with text/image embedding endpoints (768-dim)
  - Docker support with NVIDIA GPU runtime
  - Port 8001, health checks, model info API

- Qdrant Vector Database integration
  - Port 6333/6334 (HTTP/gRPC)
  - Image embeddings storage (768-dim, Cosine distance)
  - Auto collection creation

- Vision RAG implementation
  - VisionEncoderClient (Python client for API)
  - Image Search module (text-to-image, image-to-image)
  - Vision RAG routing in DAGI Router (mode: image_search)
  - VisionEncoderProvider integration

- Documentation (5000+ lines)
  - SYSTEM-INVENTORY.md - Complete system inventory
  - VISION-ENCODER-STATUS.md - Service status
  - VISION-RAG-IMPLEMENTATION.md - Implementation details
  - vision_encoder_deployment_task.md - Deployment checklist
  - services/vision-encoder/README.md - Deployment guide
  - Updated WARP.md, INFRASTRUCTURE.md, Jupyter Notebook

- Testing
  - test-vision-encoder.sh - Smoke tests (6 tests)
  - Unit tests for client, image search, routing

- Services: 17 total (added Vision Encoder + Qdrant)
- AI Models: 3 (qwen3:8b, OpenCLIP ViT-L/14, BAAI/bge-m3)
- GPU Services: 2 (Vision Encoder, Ollama)
- VRAM Usage: ~10 GB (concurrent)

Status: Production Ready 
2025-11-17 05:24:36 -08:00
Apple
382e661f1f feat: complete RAG pipeline integration (ingest + query + Memory)
Parser Service:
- Add /ocr/ingest endpoint (PARSER → RAG in one call)
- Add RAG_BASE_URL and RAG_TIMEOUT to config
- Add OcrIngestResponse schema
- Create file_converter utility for PDF/image → PNG bytes
- Endpoint accepts file, dao_id, doc_id, user_id
- Automatically parses with dots.ocr and sends to RAG Service

Router Integration:
- Add _handle_rag_query() method in RouterApp
- Combines Memory + RAG → LLM pipeline
- Get Memory context (facts, events, summaries)
- Query RAG Service for documents
- Build prompt with Memory + RAG documents
- Call LLM provider with combined context
- Return answer with citations

Clients:
- Create rag_client.py for Router (query RAG Service)
- Create memory_client.py for Router (get Memory context)

E2E Tests:
- Create e2e_rag_pipeline.sh script for full pipeline test
- Test ingest → query → router query flow
- Add E2E_RAG_README.md with usage examples

Docker:
- Add RAG_SERVICE_URL and MEMORY_SERVICE_URL to router environment
2025-11-16 05:02:14 -08:00
Apple
d3c701f3ff feat: add qa_build mode, tests, and region mode support
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
2025-11-16 04:26:35 -08:00
Apple
be22752590 feat: integrate dots.ocr native prompt modes and 2-stage qa_pairs pipeline
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
2025-11-16 04:24:03 -08:00
Apple
00f9102e50 feat: add Ollama runtime support and RAG implementation plan
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)
2025-11-16 02:56:36 -08:00
Apple
4befecc425 feat: implement PDF/image preprocessing, post-processing, and dots.ocr integration prep
G.2.3 - PDF/Image Support:
- Add preprocessing.py with PDF→images conversion (pdf2image)
- Add image loading and normalization
- Add file type detection and validation
- Support for PDF, PNG, JPEG, WebP, TIFF

G.2.4 - Pre/Post-processing:
- Add postprocessing.py with structured output builders
- build_chunks() - semantic chunks for RAG
- build_qa_pairs() - Q&A extraction
- build_markdown() - Markdown conversion
- Text normalization and chunking logic

G.1.3 - dots.ocr Integration Prep:
- Update model_loader.py with proper error handling
- Add USE_DUMMY_PARSER and ALLOW_DUMMY_FALLBACK flags
- Update inference.py to work with images list
- Add parse_document_from_images() function
- Ready for actual model integration

Configuration:
- Add PDF_DPI, IMAGE_MAX_SIZE, PAGE_RANGE settings
- Add parser mode flags (USE_DUMMY_PARSER, ALLOW_DUMMY_FALLBACK)

API Updates:
- Update endpoints to use new preprocessing pipeline
- Integrate post-processing for all output modes
- Remove temp file handling (work directly with bytes)
2025-11-15 13:19:07 -08:00
Apple
5e7cfc019e feat: create PARSER service skeleton with FastAPI
- Create parser-service/ with full structure
- Add FastAPI app with endpoints (/parse, /parse_qa, /parse_markdown, /parse_chunks)
- Add Pydantic schemas (ParsedDocument, ParsedBlock, ParsedChunk, etc.)
- Add runtime module with model_loader and inference (with dummy parser)
- Add configuration, Dockerfile, requirements.txt
- Update TODO-PARSER-RAG.md with completed tasks
- Ready for dots.ocr model integration
2025-11-15 13:15:08 -08:00