- Lower vue_ui_beta gate to "free" so all licensed users can access the
new UI without a paid subscription
- Remove "Paid tier" wording from the Try New UI banner
- Fix Vue SPA navigation in cloud/demo deployments: add VITE_BASE_PATH
build arg so Vite sets the correct subpath base, and pass
import.meta.env.BASE_URL to createWebHistory() so router links
emit /peregrine/... paths that Caddy can match
- Fix feedback button missing on cloud instance by passing
FORGEJO_API_TOKEN through compose.cloud.yml
- Remove vLLM container from compose.yml (vLLM dropped from stack;
cf-research service in cfcore covers the use case)
- Fix cloud config path in Apply page (use get_config_dir() so per-user
cloud data roots resolve correctly for user.yaml and resume YAML)
- Refactor generate_cover_letter._build_system_context and
_build_mission_notes to accept explicit profile arg (enables
per-user cover letter generation in cloud multi-tenant mode)
- Add API proxy block to nginx.conf (Vue web container can now call
/api/ directly without Vite dev proxy)
- Update .env.example: remove vLLM vars, add research model + tuning
vars for external vLLM deployments
- Update llm.yaml: switch vllm base_url to host.docker.internal
(vLLM now runs outside Docker stack)
Closes#63 (feedback button)
Related: #8 (Vue SPA), #50–#62 (parity milestone)
compose.yml was still using build: . which fails because requirements.txt
has -e ../circuitforge-core outside the build context. Now matches
compose.cloud.yml and compose.test-cfcore.yml.
sync_ui_cookie() was calling window.parent.location.reload() on every
render when user.yaml has ui_preference=vue, but no Caddy is in the
traffic path (test instances, bare Docker). This caused an infinite
reload loop because the reload just came back to Streamlit.
Gate the reload on PEREGRINE_CADDY_PROXY=1. Without it, the cookie is
still written silently but no reload is attempted. Add the env var to
compose.yml and compose.cloud.yml (both are behind Caddy); omit from
compose.test-cfcore.yml so test instances stay stable.
- Settings → Search: add-title (＋) and Import buttons crashed with
StreamlitAPIException when writing to _sp_titles_multi after it was
already instantiated. Fix: pending-key pattern (_sp_titles_pending /
_sp_locs_pending) applied before widget renders on next pass.
- Home setup banners: fired for email/notion/keywords even when those
features were already configured. Add 'done' condition callables
(_email_configured, _notion_configured, _keywords_configured) to
suppress banners automatically when config files are present.
- Services tab start/stop buttons: docker CLI was unavailable inside
the container so _docker_available was False and buttons never showed.
Bind-mount host /usr/bin/docker (ro) + /var/run/docker.sock into the
app container so it can control sibling containers via DooD pattern.
- compose.yml: pass STREAMLIT_SERVER_BASE_URL_PATH from .env into container
Streamlit prefixes all asset URLs with the path so Caddy handle_path routing works.
Without this, /static/* requests skip the /peregrine* route → 503 text/plain MIME error.
- config/server.yaml.example: document base_url_path + server_port settings
- .gitignore: ignore config/server.yaml (local gitignored instance of server.yaml.example)
- app/pages/2_Settings.py: add Deployment/Server expander under System tab
Shows active base URL path from env; saves edits to config/server.yaml + .env;
prompts user to run ./manage.sh restart to apply.
Refs: https://docs.streamlit.io/develop/api-reference/configuration/config.toml#server.baseUrlPath
- preflight.py now writes PEREGRINE_GPU_COUNT and PEREGRINE_GPU_NAMES to
.env so the app container gets GPU info without needing nvidia-smi access
- compose.yml passes PEREGRINE_GPU_COUNT, PEREGRINE_GPU_NAMES, and
RECOMMENDED_PROFILE as env vars to the app service
- 0_Setup.py _detect_gpus() reads PEREGRINE_GPU_NAMES env var first;
falls back to nvidia-smi (bare / GPU-passthrough environments)
- 0_Setup.py _suggest_profile() reads RECOMMENDED_PROFILE env var first
- requirements.txt: add pdfplumber (needed for resume PDF parsing)
- preflight: ollama was incorrectly marked docker_owned=False — Docker does
define an ollama service, so external detection now correctly disables it
via compose.override.yml when host Ollama is already running
- compose.yml: finetune moves from [cpu,single-gpu,dual-gpu] profiles to
[finetune] profile so it is never built during 'make start' (pytorch/cuda
base is 3.7GB+ and unnecessary for the UI)
- compose.yml: remove depends_on ollama from finetune — it reaches Ollama
via OLLAMA_URL env var which works whether Ollama is Docker or host
- Makefile: finetune target uses --profile finetune + compose.gpu.yml overlay
preflight.py now detects when a managed service (ollama, vllm, vision,
searxng) is already running on its configured port and adopts it rather
than reassigning or conflicting:
- Generates compose.override.yml disabling Docker containers for adopted
services (profiles: [_external_] — a profile never passed via --profile)
- Rewrites config/llm.yaml base_url entries to host.docker.internal:<port>
so the app container can reach host-side services through Docker's
host-gateway mapping
- compose.yml: adds extra_hosts host.docker.internal:host-gateway to the
app service (required on Linux; no-op on macOS Docker Desktop)
- .gitignore: excludes compose.override.yml (auto-generated, host-specific)
Only streamlit is non-adoptable and continues to reassign on conflict.
- Remove ollama-gpu service (was colliding with ollama on port 11434)
- Strip inline deploy.resources GPU blocks from vision and vllm
- Add compose.gpu.yml: Docker NVIDIA overlay for ollama (GPU 0),
vision (GPU 0), vllm (GPU 1), finetune (GPU 0)
- Fix compose.podman-gpu.yml: rename ollama-gpu → ollama to match
service name after removal of ollama-gpu
- Update Makefile: apply compose.gpu.yml for Docker + GPU profiles
(was only applying podman-gpu.yml for Podman + GPU profiles)
- Dockerfile.finetune: PyTorch 2.3/CUDA 12.1 base + unsloth + training stack
- finetune_local.py: auto-register model via Ollama HTTP API after GGUF
export; path-translate between finetune container mount and Ollama's view;
update config/llm.yaml automatically; DOCS_DIR env override for Docker
- prepare_training_data.py: DOCS_DIR env override so make prepare-training
works correctly inside the app container
- compose.yml: add finetune service (cpu/single-gpu/dual-gpu profiles);
DOCS_DIR=/docs injected into app + finetune containers
- compose.podman-gpu.yml: CDI device override for finetune service
- Makefile: make prepare-training + make finetune targets
scripts/preflight.py (stdlib-only, no psutil):
- Port probing: owned services auto-reassign to next free port; external
services (Ollama) show ✓ reachable / ⚠ not responding
- System resources: CPU cores, RAM (total + available), GPU VRAM via
nvidia-smi; works on Linux + macOS
- Profile recommendation: remote / cpu / single-gpu / dual-gpu
- vLLM KV cache offload: calculates CPU_OFFLOAD_GB when VRAM < 10 GB
free and RAM headroom > 4 GB (uses up to 25% of available headroom)
- Writes resolved values to .env for docker compose; single-service mode
(--service streamlit) for scripted port queries
- Exit 0 unless an owned port genuinely can't be resolved
scripts/manage-ui.sh:
- Calls preflight.py --service streamlit before bind; falls back to
pure-bash port scan if Python/yaml unavailable
compose.yml:
- vllm command: adds --cpu-offload-gb ${CPU_OFFLOAD_GB:-0}
Makefile:
- start / restart depend on preflight target
- PYTHON variable for env portability
- test target uses PYTHON variable
- Add moondream2 vision service to compose.yml (single-gpu + dual-gpu profiles)
- Create scripts/vision_service/Dockerfile for the vision container
- Add VISION_PORT, VISION_MODEL, VISION_REVISION vars to .env.example
- Add Vision Service entry to SERVICES list in Settings (hidden unless gpu profile active)
- Add Fine-Tune Wizard tab (Task 10) to Settings with 3-step upload→preview→train flow
- Tab is always rendered; shows info message when non-GPU profile is active
