175 lines
6.3 KiB
Markdown
175 lines
6.3 KiB
Markdown
<div align="center">
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<img src="docs/avocet-logo.svg" alt="Avocet" height="96" />
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# Avocet
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**Email classifier training tool — label, benchmark, fine-tune.**
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[]()
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[](https://git.opensourcesolarpunk.com/Circuit-Forge/avocet/releases)
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[](LICENSE)
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[]()
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[](https://circuitforge.tech)
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</div>
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---
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## What is Avocet?
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Avocet is the internal data pipeline Circuit Forge uses to build, evaluate, and fine-tune email classifiers. It implements a three-stage workflow: human labelers review emails one at a time in a drag-to-bucket UI and produce a ground-truth dataset; the benchmark harness scores any number of HuggingFace zero-shot models against that dataset and produces a ranked comparison; and the fine-tune harness adapts the best-scoring base model to the labeled distribution. The output feeds directly into Peregrine's email classification layer. No LLM API key required for the label tool or benchmark — all inference runs locally via HuggingFace Transformers.
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---
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## Quick Start
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```bash
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git clone https://git.opensourcesolarpunk.com/Circuit-Forge/avocet.git
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cd avocet
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# Copy config template and fill in your IMAP credentials
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cp config/label_tool.yaml.example config/label_tool.yaml
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# Start the label tool (Vue SPA + FastAPI, port 8503)
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./manage.sh start
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./manage.sh open
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```
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---
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## Features
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- **Drag-to-bucket label UI** — ASMR-style card interface; drag emails into labeled buckets or discard without queuing noise into the training set
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- **Targeted IMAP fetch** — pull emails by date range, sender, or subject filter across multiple accounts without flooding the queue
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- **Email classifier benchmark** — score any HuggingFace zero-shot model against your labeled JSONL; side-by-side comparison on live IMAP emails
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- **Planning benchmark** — evaluate LLMs on structured planning tasks; compare models head-to-head with verbose diff output
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- **Writing style benchmark** — compare Ollama models on writing style coherence; scan local disk for existing outputs
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- **Fine-tune harness** — HuggingFace Transformers fine-tuning from labeled ground truth; classifier adapter interface for swapping backends at runtime
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- **Local inference first** — no API key required; GPU optional; designed to run on developer hardware
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- **Hot-reload dev mode** — uvicorn `--reload` + Vite HMR (hot module replacement) for fast iteration on both API and UI
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---
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## CLI Reference
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All operations go through `manage.sh`.
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### Label Tool
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```bash
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./manage.sh start # Build Vue SPA and start FastAPI on port 8503
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./manage.sh stop # Stop FastAPI server
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./manage.sh restart # Stop, rebuild, and restart
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./manage.sh status # Show running state and port
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./manage.sh logs # Tail the API log
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./manage.sh open # Open http://localhost:8503 in browser
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./manage.sh dev # Hot-reload: uvicorn --reload + Vite HMR
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./manage.sh test # Run pytest suite
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```
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### Email Classifier Benchmark
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```bash
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./manage.sh benchmark [args] # Run benchmark_classifier.py
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./manage.sh list-models # List available zero-shot models
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./manage.sh score # Score models against labeled JSONL
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./manage.sh score --include-slow # Include large/slow models
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./manage.sh compare --limit 30 # Side-by-side comparison on live IMAP emails
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```
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### Planning Benchmark
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```bash
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./manage.sh plans-bench [args] # Run benchmark_plans.py
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./manage.sh plans-list # List available models
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./manage.sh plans-run <model> [args] # Run a single model (verbose)
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./manage.sh plans-compare <m1> <m2> [...] # Compare models side-by-side
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```
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### Writing Style Benchmark
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```bash
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./manage.sh style-bench [args] # Run benchmark_style.py
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./manage.sh style-list # List available Ollama models
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./manage.sh style-run [args] # Run writing style benchmark
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./manage.sh style-last # Print most recent benchmark report
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```
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---
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## Data Flow
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```
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IMAP accounts
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→ fetch (targeted or wide)
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→ email_label_queue.jsonl
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email_label_queue.jsonl
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→ label tool drag-to-bucket UI
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→ email_score.jsonl (ground truth)
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email_score.jsonl
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→ benchmark harness
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→ model rankings
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best model
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→ fine-tune harness
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→ Peregrine classifier adapter
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```
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---
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## Labels
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| Label | Key |
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|-------|-----|
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| `interview_scheduled` | 1 |
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| `offer_received` | 2 |
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| `rejected` | 3 |
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| `positive_response` | 4 |
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| `survey_received` | 5 |
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| `neutral` | 6 |
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| `event_rescheduled` | 7 |
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| `unrelated` | 8 |
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| `digest` | 9 |
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---
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## Stack
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| Layer | Technology |
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|-------|-----------|
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| Label UI | Vue 3 SPA (Vite) |
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| API | FastAPI + uvicorn (port 8503) |
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| Benchmark | Python + HuggingFace Transformers |
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| Email fetch | IMAP (multi-account, targeted date/sender/subject filter) |
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| Data | JSONL (`data/email_label_queue.jsonl`, `data/email_score.jsonl`) |
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| Runtime | SQLite |
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| Config | `config/label_tool.yaml` (gitignored — `.example` committed) |
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---
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## Logo
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The Avocet logo (`avocet_v1_poly.svg`) lives in the shared graphics repo. Copy it to `docs/avocet-logo.svg` to render correctly in this README.
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---
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## About
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Avocet is internal CircuitForge infrastructure, open source as a reference implementation. It is not a user-facing product. The primary consumer is [Peregrine](https://git.opensourcesolarpunk.com/Circuit-Forge/peregrine), CircuitForge's job-search pipeline tool.
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Docs: [docs.circuitforge.tech/avocet](https://docs.circuitforge.tech/avocet)
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## Forgejo-primary
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Avocet is developed and maintained on Forgejo at [git.opensourcesolarpunk.com/Circuit-Forge/avocet](https://git.opensourcesolarpunk.com/Circuit-Forge/avocet). GitHub and Codeberg are read-only mirrors.
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---
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## License
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[Business Source License 1.1](LICENSE) — classifier training is an AI feature under the CircuitForge licensing model.
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Free for personal non-commercial self-hosting. Commercial use or SaaS re-hosting requires a paid license. Converts to MIT after 4 years.
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© 2026 Circuit Forge LLC — Privacy · Safety · Accessibility
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