Ship personalised RAG, 10m SLA, parallel generate, and AI phase docs for HF pilot.

Prioritize production AI: photo vision fix, HF profile, and inspector on generate.

feat: async pipeline, job queue, generation hardening, and docs

feat: AI interference levels (minimum|medium|maximum) UI, API, prompts, generation

feat: enhance embedding model configuration and retrieval logic

feat: introduce strict uploaded only mode and enhance logging for AI generation

feat: enhance AI involvement and standard paragraph handling

feat: enhance AI involvement handling and transparency metrics

fix: apply structured section blocks to L1/L2 as well

fix: restore L3 structured section blocks and label survey level

fix: allow reference-only RAG guidance in notes-only mode

fix: notes-only generation to prevent RAG content leak

deploy: harden inspector + add RAGAS eval harness

refactor: enhance non-invention guard and tier-aware output handling

chore: update .gitignore and README for improved clarity and functionality

feat: adaptive writing style — user docs first, KB fallback, few-shot examples

- Added support for RICS survey levels (1, 2, 3) in document uploads and reports, allowing for better tier management and retrieval filtering.

- Replaced timezone.utc with UTC from datetime for consistent datetime handling.

- Introduced `ingest_timeout_seconds` setting in config.py to define maximum time for document ingestion.

feat(experiment-lab): AI transparency bar, tenant-scoped RAG, citations, proofread/enhance fixes

- Updated `.env.example` to include new settings for bulk upload limits and batch processing.

feat: enhance document upload and processing capabilities with new personalisation features

fix: comprehensive codebase review fixes (critical + major + minor issues)

fix: apply all bug fixes and improvements (embeddings, cache, postprocess, frontend, tests)

feat: scaffold complete RAG system for RICS-style report generation