pyr0ball
8602107cc1
architecture.md: updated Docker Compose table (3 compose files), database layer (Postgres platform + SQLite-per-user), cloud session middleware, telemetry system, and cloud design decisions. cloud-deployment.md (new): full operational runbook — env vars, data root layout, GDPR deletion, platform DB queries, telemetry, backup/restore, Caddy routing, demo instance, and onboarding a new app to the cloud.
286 lines
11 KiB
Markdown
286 lines
11 KiB
Markdown
# Architecture
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This page describes Peregrine's system structure, layer boundaries, and key design decisions.
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---
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## System Overview
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### Pipeline
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```mermaid
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flowchart LR
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sources["JobSpy\nCustom Boards"]
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discover["discover.py"]
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db[("staging.db\nSQLite")]
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match["match.py\nScoring"]
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review["Job Review\nApprove / Reject"]
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apply["Apply Workspace\nCover letter + PDF"]
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kanban["Interviews\nphone_screen → hired"]
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sync["sync.py"]
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notion["Notion DB"]
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sources --> discover --> db --> match --> review --> apply --> kanban
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db --> sync --> notion
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```
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### Docker Compose Services
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Three compose files serve different deployment contexts:
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| File | Project name | Port | Purpose |
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|------|-------------|------|---------|
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| `compose.yml` | `peregrine` | 8502 | Local self-hosted install (default) |
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| `compose.demo.yml` | `peregrine-demo` | 8504 | Public demo at `demo.circuitforge.tech/peregrine` — `DEMO_MODE=true`, no LLM |
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| `compose.cloud.yml` | `peregrine-cloud` | 8505 | Cloud managed instance at `menagerie.circuitforge.tech/peregrine` — `CLOUD_MODE=true`, per-user data |
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```mermaid
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flowchart TB
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subgraph local["compose.yml (local)"]
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app_l["**app** :8502\nStreamlit UI"]
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ollama_l["**ollama**\nLocal LLM"]
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vllm_l["**vllm**\nvLLM"]
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vision_l["**vision**\nMoondream2"]
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searxng_l["**searxng**\nWeb Search"]
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db_l[("staging.db\nSQLite")]
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end
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subgraph cloud["compose.cloud.yml (cloud)"]
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app_c["**app** :8505\nStreamlit UI\nCLOUD_MODE=true"]
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searxng_c["**searxng**\nWeb Search"]
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db_c[("menagerie-data/\n<user-id>/staging.db\nSQLCipher")]
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pg[("Postgres\nplatform DB\n:5433")]
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end
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```
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Solid lines = always connected. Dashed lines = optional/profile-dependent backends.
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### Streamlit App Layer
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```mermaid
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flowchart TD
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entry["app/app.py\nEntry point · navigation · sidebar task badge"]
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setup["0_Setup.py\nFirst-run wizard\n⚠️ Gates everything"]
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review["1_Job_Review.py\nApprove / reject queue"]
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settings["2_Settings.py\nAll user configuration"]
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apply["4_Apply.py\nCover letter gen + PDF export"]
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interviews["5_Interviews.py\nKanban: phone_screen → hired"]
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prep["6_Interview_Prep.py\nResearch brief + practice Q&A"]
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survey["7_Survey.py\nCulture-fit survey assistant"]
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wizard["app/wizard/\nstep_hardware.py … step_integrations.py\ntiers.py — feature gate definitions"]
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entry --> setup
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entry --> review
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entry --> settings
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entry --> apply
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entry --> interviews
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entry --> prep
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entry --> survey
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setup <-.->|wizard steps| wizard
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```
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### Scripts Layer
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Framework-independent — no Streamlit imports. Can be called from CLI, FastAPI, or background threads.
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| Script | Purpose |
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|--------|---------|
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| `discover.py` | JobSpy + custom board orchestration |
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| `match.py` | Resume keyword scoring |
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| `db.py` | All SQLite helpers (single source of truth) |
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| `llm_router.py` | LLM fallback chain |
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| `generate_cover_letter.py` | Cover letter generation |
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| `company_research.py` | Pre-interview research brief |
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| `task_runner.py` | Background daemon thread executor |
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| `imap_sync.py` | IMAP email fetch + classify |
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| `sync.py` | Push to external integrations |
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| `user_profile.py` | `UserProfile` wrapper for `user.yaml` |
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| `preflight.py` | Port + resource check |
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| `custom_boards/` | Per-board scrapers |
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| `integrations/` | Per-service integration drivers |
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| `vision_service/` | FastAPI Moondream2 inference server |
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### Config Layer
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Plain YAML files. Gitignored files contain secrets; `.example` files are committed as templates.
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| File | Purpose |
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|------|---------|
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| `config/user.yaml` | Personal data + wizard state |
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| `config/llm.yaml` | LLM backends + fallback chains |
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| `config/search_profiles.yaml` | Job search configuration |
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| `config/resume_keywords.yaml` | Scoring keywords |
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| `config/blocklist.yaml` | Excluded companies/domains |
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| `config/email.yaml` | IMAP credentials |
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| `config/integrations/` | Per-integration credentials |
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### Database Layer
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**Local mode** — `staging.db`: SQLite, single file, gitignored.
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**Cloud mode** — Hybrid:
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- **Postgres (platform layer):** account data, subscriptions, telemetry consent. Shared across all users.
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- **SQLite-per-user (content layer):** each user's job data in an isolated, SQLCipher-encrypted file at `/devl/menagerie-data/<user-id>/peregrine/staging.db`. Schema is identical to local — the app sees no difference.
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#### Local SQLite tables
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| Table | Purpose |
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|-------|---------|
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| `jobs` | Core pipeline — all job data |
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| `job_contacts` | Email thread log per job |
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| `company_research` | LLM-generated research briefs |
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| `background_tasks` | Async task queue state |
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| `survey_responses` | Culture-fit survey Q&A pairs |
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#### Postgres platform tables (cloud only)
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| Table | Purpose |
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|-------|---------|
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| `subscriptions` | User tier, license JWT, product |
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| `usage_events` | Anonymous usage telemetry (consent-gated) |
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| `telemetry_consent` | Per-user telemetry preferences + hard kill switch |
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| `support_access_grants` | Time-limited support session grants |
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---
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### Cloud Session Middleware
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`app/cloud_session.py` handles multi-tenant routing transparently:
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```
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Request → Caddy injects X-CF-Session header (from Directus session cookie)
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→ resolve_session() validates JWT, derives db_path + db_key
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→ all DB calls use get_db_path() instead of DEFAULT_DB
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```
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Key functions:
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| Function | Purpose |
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|----------|---------|
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| `resolve_session(app)` | Called at top of every page — no-op in local mode |
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| `get_db_path()` | Returns per-user `db_path` (cloud) or `DEFAULT_DB` (local) |
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| `derive_db_key(user_id)` | `HMAC(SERVER_SECRET, user_id)` — deterministic per-user SQLCipher key |
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The app code never branches on `CLOUD_MODE` except at the entry points (`resolve_session` and `get_db_path`). Everything downstream is transparent.
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### Telemetry (cloud only)
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`app/telemetry.py` is the **only** path to the `usage_events` table. No feature may write there directly.
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```python
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from app.telemetry import log_usage_event
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log_usage_event(user_id, "peregrine", "cover_letter_generated", {"words": 350})
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```
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- Complete no-op when `CLOUD_MODE=false`
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- Checks `telemetry_consent.all_disabled` first — if set, nothing is written, no exceptions
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- Swallows all exceptions so telemetry never crashes the app
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---
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## Layer Boundaries
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### App layer (app/)
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The Streamlit UI layer. Its only responsibilities are:
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- Reading from `scripts/db.py` helpers
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- Calling `scripts/` functions directly or via `task_runner.submit_task()`
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- Rendering results to the browser
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The app layer does not contain business logic. Database queries, LLM calls, and integrations all live in `scripts/`.
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### Scripts layer (scripts/)
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This is the stable public API of Peregrine. Scripts are designed to be framework-independent — they do not import Streamlit and can be called from a CLI, FastAPI endpoint, or background thread without modification.
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All personal data access goes through `scripts/user_profile.py` (`UserProfile` class). Scripts never read `config/user.yaml` directly.
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All database access goes through `scripts/db.py`. No script does raw SQLite outside of `db.py`.
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### Config layer (config/)
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Plain YAML files. Gitignored files contain secrets; `.example` files are committed as templates.
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---
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## Background Tasks
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`scripts/task_runner.py` provides a simple background thread executor for long-running LLM tasks.
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```python
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from scripts.task_runner import submit_task
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# Queue a cover letter generation task
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submit_task(db_path, task_type="cover_letter", job_id=42)
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# Queue a company research task
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submit_task(db_path, task_type="company_research", job_id=42)
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```
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Tasks are recorded in the `background_tasks` table with the following state machine:
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```mermaid
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stateDiagram-v2
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[*] --> queued : submit_task()
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queued --> running : daemon picks up
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running --> completed
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running --> failed
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queued --> failed : server restart clears stuck tasks
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completed --> [*]
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failed --> [*]
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```
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**Dedup rule:** Only one `queued` or `running` task per `(task_type, job_id)` pair is allowed at a time. Submitting a duplicate is a silent no-op.
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**On startup:** `app/app.py` resets any `running` or `queued` rows to `failed` to clear tasks that were interrupted by a server restart.
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**Sidebar indicator:** `app/app.py` polls the `background_tasks` table every 3 seconds via a Streamlit fragment and displays a badge in the sidebar.
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---
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## LLM Router
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`scripts/llm_router.py` provides a single `complete()` call that tries backends in priority order and falls back transparently. See [LLM Router](../reference/llm-router.md) for full documentation.
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---
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## Key Design Decisions
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### scripts/ is framework-independent
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The scripts layer was deliberately kept free of Streamlit imports. This means the full pipeline can be migrated to a FastAPI or Celery backend without rewriting business logic.
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### All personal data via UserProfile
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`scripts/user_profile.py` is the single source of truth for all user data. This makes it easy to swap the storage backend (e.g. from YAML to a database) without touching every script.
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### SQLite as staging layer
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`staging.db` acts as the staging layer between discovery and external integrations. This lets discovery, matching, and the UI all run independently without network dependencies. External integrations (Notion, Airtable, etc.) are push-only and optional.
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### Tier system in app/wizard/tiers.py
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`FEATURES` is a single dict that maps feature key → minimum tier. `can_use(tier, feature)` is the single gating function. New features are added to `FEATURES` in one place.
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### Vision service is a separate process
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Moondream2 requires `torch` and `transformers`, which are incompatible with the lightweight main conda environment. The vision service runs as a separate FastAPI process in a separate conda environment (`job-seeker-vision`), keeping the main env free of GPU dependencies.
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### Cloud mode is a transparent layer, not a fork
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`CLOUD_MODE=true` activates two entry points (`resolve_session`, `get_db_path`) and the telemetry middleware. Every other line of app code is unchanged. There is no cloud branch, no conditional imports, no schema divergence. The local-first architecture is preserved end-to-end; the cloud layer sits on top of it.
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### SQLite-per-user instead of shared Postgres
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Each cloud user gets their own encrypted SQLite file. This means:
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- No SQL migrations when the schema changes — new users get the latest schema, existing users keep their file as-is
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- Zero risk of cross-user data leakage at the DB layer
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- GDPR deletion is `rm -rf /devl/menagerie-data/<user-id>/` — auditable and complete
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- The app can be tested locally with `CLOUD_MODE=false` without any Postgres dependency
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The Postgres platform DB holds only account metadata (subscriptions, consent, telemetry) — never job search content.
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