Changes_prakhar - #19
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…/WhereTF-backend into Changes_Prakhar
This update completely changes how we handle file uploads by moving the heavy AI work into the background so the user doesn't have to wait. Here is everything that changed: - Added Redis and Celery worker to docker-compose to handle background jobs. - Updated Dockerfile to cache Python packages for faster builds. - Fixed the database migrations and added a 'state' column to track file progress (processing, indexed, failed). - Fixed the circular import crashes in the database models. - Updated the upload route to instantly send jobs to the queue and return a task ID. - Added a new '/status' live stream endpoint to show real-time progress to the frontend. - Added an automatic cleanup step in the worker so temporary files are always deleted. - Brought back the GET /files/ endpoint so the dashboard can list all uploaded documents.
…l security/data bugs Infrastructure & Architecture: - Completely decoupled file uploading from the main API thread using Redis and Celery. - AI (sentence-transformers) now runs safely in background workers. - Implemented Server-Sent Events (SSE) for live frontend status tracking. - Restored unified hybrid search endpoint. Security & Vulnerability Patches: - Patched 'Zip Slip' directory traversal vulnerability in archive extraction. - Secured upload route against path traversal and malicious overwrites using UUID prefixing. - Prevented temp-directory memory leaks on corrupted ZIP uploads. Data Integrity & Database: - Resolved race conditions in Celery workers using IntegrityError try/rollback blocks. - Forced strict failure propagation so Celery correctly reports 'FAILURE' instead of swallowing exceptions. - Added 'state' column to File model to track indexing progress. - Added 'relation_type' to FileRelationship model. - Fixed one-directional relationship bug by implementing symmetric ORM queries. - Cleaned up app/database namespace collisions.
…d migrate vector DB Infrastructure & Build Fixes: - Downgraded Dockerfile to Python 3.11-slim to bypass Rust/Cargo compilation errors for HF tokenizers. - Removed hardcoded DNS from docker-compose.yml to resolve network timeouts when pulling NLTK dictionaries. - Baked NLTK data (punkt, wordnet, omw-1.4) directly into the Docker image to eliminate startup hangs. AI & Database Migration: - Added missing nltk dependency for expansion.py. - Updated FileContent ORM model to Vector(768) to support the new Jina CLIP embedding model. - Executed database migration to stretch the pgvector column and cascade-delete legacy 384-dimension files.
- Updated \ChunkData\ typing and \extract_image\ in extractors to preserve and pass raw PIL Image objects instead of dropping images without OCR text. - Modified \embeddings.py\ to dynamically locate the underlying Jina AutoModel, bypassing the \SentenceTransformer\ text-only modality strictness. - Implemented L2 vector normalization for image tensors to ensure cosine similarity perfectly aligns with text search queries. - Resolved SIGKILL (OOM) crashes and PyTorch tensor conversion errors during the worker indexer pipeline.
…/WhereTF-backend into Changes_Prakhar
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"fix(multimodal): enable native image encoding for jina-clip-v1