Primary parse path is now fully deterministic — no LLM, no token limits,
no JSON generation. Handles two-column experience headers, institution-before-
or-after-degree education layouts, and header bleed prevention via
looks_like_header detection.
LLM path retained as optional career_summary enhancement only (1500 chars,
falls back silently). structure_resume() now returns tuple[dict, str].
Tests updated to match the new API.
- 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)
Three inter-related fixes for the service adoption flow:
- preflight: stub_port field — adopted services get a free port for their
no-op container (avoids binding conflict with external service on real port)
while update_llm_yaml still uses the real external port for host.docker.internal URLs
- preflight: write_env now uses stub_port (not resolved) for adopted services
so SEARXNG_PORT etc point to the stub's harmless port, not the occupied one
- preflight: stub containers use sleep infinity + CMD true healthcheck so
depends_on: service_healthy is satisfied without holding any real port
- Makefile: finetune profile changed from [cpu,single-gpu,dual-gpu] to [finetune]
so the pytorch/cuda base image is not built during make start
- 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.
- setup.sh: replace docker-image-based NVIDIA test with nvidia-ctk validate
(faster, no 100MB pull, no daemon required); add check_docker_running()
to auto-start the Docker service on Linux or warn on macOS
- prepare_training_data.py: also scan training_data/uploads/*.{md,txt}
so web-uploaded letters are included in training data
- task_runner.py: add prepare_training task type (calls build_records +
write_jsonl inline; reports pair count in task result)
- Settings fine-tune tab: Step 1 accepts .md/.txt uploads; Step 2 Extract
button submits prepare_training background task + shows status; Step 3
shows make finetune command + live Ollama model status poller
- 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
- generate() accepts previous_result + feedback; appends both to LLM prompt
- task_runner cover_letter handler parses params JSON, passes fields through
- Apply Workspace: "Refine with Feedback" expander with text area + Regenerate
button; only shown when a draft exists; clears feedback after submitting
- 8 new tests (TestGenerateRefinement + TestTaskRunnerCoverLetterParams)
Add all 13 integration modules (Notion, Google Drive, Google Sheets,
Airtable, Dropbox, OneDrive, MEGA, Nextcloud, Google Calendar, Apple
Calendar/CalDAV, Slack, Discord, Home Assistant) with fields(), connect(),
and test() implementations. Add config/integrations/*.yaml.example files
and gitignore rules for live config files. Add 5 new registry/schema
tests bringing total to 193 passing.
- Add tier, dev_tier_override, wizard_complete, wizard_step, dismissed_banners
fields to UserProfile with defaults and effective_tier property
- Add params TEXT column to background_tasks table (CREATE + migration)
- Update insert_task() to accept params with params-aware dedup logic
- Update submit_task() and _run_task() to thread params through
- Add test_wizard_defaults, test_effective_tier_override,
test_effective_tier_no_override, and test_insert_task_with_params
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
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
scripts/migrate.py:
- dry-run by default; --apply writes files; --copy-db migrates staging.db
- generates config/user.yaml from source repo's resume + cover letter scripts
- copies gitignored configs (notion, email, adzuna, craigslist, search profiles,
resume keywords, blocklist, aihawk resume)
- merges fine-tuned model name from source llm.yaml into dest llm.yaml
scripts/manage-ui.sh:
- STREAMLIT_BIN no longer hardcoded; auto-resolves via conda env or PATH;
override with STREAMLIT_BIN env var
scripts/manage-vllm.sh:
- VLLM_BIN and MODEL_DIR now read from env vars with portable defaults
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LGBTQIA+ inclusion section in research briefs:
- user_profile.py: add candidate_lgbtq_focus bool accessor
- user.yaml.example: add candidate_lgbtq_focus flag (default false)
- company_research.py: gate new LGBTQIA+ section behind flag; section
count now dynamic (7 base + 1 per opt-in section, max 9)
- 2_Settings.py: add "Research Brief Preferences" expander with
checkboxes for both accessibility and LGBTQIA+ focus flags;
mission_preferences now round-trips through save (no silent drop)
Phase 2 fixes:
- manage-vllm.sh: MODEL_DIR and VLLM_BIN now read from env vars
(VLLM_MODELS_DIR, VLLM_BIN) with portable defaults
- search_profiles.yaml: replace personal CS/TAM/Bay Area profiles
with a documented generic starter profile
Phase 3 fix:
- llm.yaml: rename alex-cover-writer:latest → llama3.2:3b with
inline comment for users to substitute their fine-tuned model;
fix model-exclusion comment
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 3_Resume_Editor.py: replace "Alex's" in docstring and caption
- user_profile.py: expose mission_preferences and candidate_accessibility_focus
- user.yaml.example: add mission_preferences section + candidate_accessibility_focus flag
- generate_cover_letter.py: build _MISSION_NOTES from user profile instead of
hardcoded personal passion notes; falls back to generic defaults when not set
- company_research.py: gate "Inclusion & Accessibility" section behind
candidate_accessibility_focus flag; section count adjusts (7 or 8) accordingly
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace hard-coded paths (/Library/Documents/JobSearch), names (Alex Rivera),
NDA sets (_NDA_COMPANIES), and the scraper path with UserProfile-driven lookups.
Update tests to be profile-agnostic (no user.yaml in peregrine config dir).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
