file-server/README.md

# FileServer

A minimal Julia service that receives files over HTTP and hands them off to a
pool of worker threads for processing. The HTTP endpoint does no real work: it
spools each uploaded file to disk, pushes a lightweight reference onto a work
queue, and responds immediately — staying free to accept the next upload.

The per-file "processing" runs each file through a small neural-network
classifier that labels it **known** (a file type resembling the training set) or
**unknown**, and logs the result. See "File classifier" below.

## Architecture

The pipeline is four stages, each with its own bounded queue and its own worker
pool (tuned independently, since classification is CPU-bound, known-file
enrichment is process-/IO-bound, content triage is cheap IO, and language
enrichment mixes CPU with a subprocess):

```
        POST /upload (multipart)
                 │
                 ▼
        ┌─────────────────┐     spool bytes to disk
        │  HTTP handler    │────────────────────────► data/spool/<uuid>-<name>
        │  (Oxygen.jl)     │
        └────────┬─────────┘     enqueue reference (non-blocking)
                 │                        │
                 ▼                        ▼
          202 + job IDs        ┌────────────────────┐
          (503 if full)        │  stage-1 queue     │  classification
                               └─────────┬──────────┘
                                         │ dequeue
                     ┌───────────────────┼───────────────────┐
                     ▼                   ▼                    ▼
              classify wkr 1      classify wkr 2   …   classify wkr N
                     │
        ┌────────────┴────────────┐
    :unknown                    :known
        │  move to data/unknown/,  │  move to data/known/, then
        ▼  then enqueue (blocking) ▼  enqueue (blocking backpressure)
  ┌────────────────────┐        ┌────────────────────┐
  │  unknown queue     │        │  known queue       │  enrichment
  └─────────┬──────────┘        └─────────┬──────────┘
            │ dequeue                     │ dequeue
   ┌────────┼────────┐        ┌───────────┼───────────┐
   ▼        ▼        ▼        ▼           ▼           ▼
 unk 1  unk 2 … unk K   known wkr 1  known wkr 2 … known wkr M
   │  binary-vs-text sniff       │  exiftool → normalized sidecar
   ├─► data/binary/<uuid>-<name> success ──┴──► data/done/<uuid>-<name>
   │   (terminal)                           data/done/<uuid>-<name>.meta.json
   │                                        (sidecar-first commit)
   │  :text  move to data/text/,  failure  ───────► data/failed/<uuid>-<name>
   ▼  then enqueue (blocking backpressure)
  ┌────────────────────┐
  │  text queue        │  language enrichment
  └─────────┬──────────┘
            │ dequeue
   ┌────────┼────────┐
   ▼        ▼        ▼
 txt 1  txt 2 … txt P
   │  Languages.jl (natural language) + github-linguist (programming language)
   └─► data/text_done/<uuid>-<name>  +  data/text_done/<uuid>-<name>.meta.json
                                        (sidecar-first commit)
```

Stages 2 (known-file enrichment) and 3 (content triage) run in parallel: stage 1
feeds both the known and unknown queues. Stage 3 in turn feeds stage 4 (language
enrichment) for every file it sorts as text.

Key properties:

- **Fast intake:** the queue only ever carries small references; file bytes live
  on disk, so memory stays flat regardless of file size.
- **Backpressure:** each queue is bounded (default 1000). When the *intake* queue
  is full, uploads get `503 Service Unavailable`. When the *known* queue is full,
  the stage-1 worker blocks and retries (a classified file is never dropped).
- **Crash-resilient:** files survive on disk. On startup, recovery is
  stage-aware: leftovers in `data/spool/` re-enter classification, `data/known/`
  re-enter enrichment, `data/unknown/` re-enter content triage, and `data/text/`
  re-enter language enrichment (`recovered` / `recovered_known` /
  `recovered_unknown` / `recovered_text` in the log), so a file resumes at its
  correct stage instead of restarting from scratch.
- **Graceful shutdown:** SIGINT (Ctrl-C) and SIGTERM (systemd/Docker/k8s `stop`)
  both stop accepting uploads, then drain the stages *in order* — close the
  stage-1 queue and wait out the classify workers (the only producer of the known
  *and* unknown queues), then close those queues and wait out the enrich and
  content-triage workers (content triage being the only producer of the text
  queue), then close the text queue and wait out the language-enrichment workers.
  (See "Shutdown" below for one cosmetic caveat on SIGTERM.)
- **Safe filenames:** client-supplied names are sanitized and prefixed with a
  server-minted UUID before touching the filesystem (no path traversal).

### Metadata enrichment (stage 2)

Files the classifier labels **known** are handed to a second pool that extracts
metadata with [`exiftool`](https://exiftool.org/) (`exiftool -json -G -n`) —
chosen because no native Julia library comes close to its multi-format coverage.
The output is normalized into a small, stable, documented schema and written as a
JSON **sidecar** next to the file in `data/done/`, e.g.
`data/done/<uuid>-<name>.meta.json`. The original bytes are never modified.

> **Prerequisite:** `exiftool` must be on `PATH` (e.g. `apt install
> libimage-exiftool-perl`). The server **fails fast at startup** if it's missing.

Sidecar top-level fields (all nullable — present only when available), plus the
complete raw `exiftool` object under `raw`:

| Field | Meaning |
|---|---|
| `id`, `original_name` | job id and client-supplied name |
| `file_type`, `mime_type` | e.g. `PDF` / `application/pdf` |
| `file_size` | bytes (authoritative, from intake — not exiftool) |
| `created_date`, `modified_date` | content timestamps |
| `author` | person (`Author`/`Artist`/`By-line`) |
| `created_by` | authoring app/tool (`Producer`/`CreatorTool`/`Creator`/`Software`/…) |
| `dimensions` | `{width, height}` for media |
| `duration` | seconds, for audio/video |
| `page_count` | for documents |
| `error` | set on a *degraded* sidecar (see below) |
| `raw` | full `exiftool` output |

Each normalized field is a coalesce over a priority list of exiftool tags
(`src/metadata.jl`); extend a field by appending tag names. If extraction fails
or `exiftool` times out (`FS_EXIFTOOL_TIMEOUT`, default 30s), the file still
completes to `data/done/` with a **degraded sidecar** — `file_size`/`file_type`
plus an `error` note — rather than being quarantined, because it's still a wanted
known file. Only genuine I/O errors (can't write the sidecar or move the file)
send it to `data/failed/`.

The sidecar is committed **before** the file is moved into `data/done/`, so a
file's presence there always implies its sidecar is already present; a crash in
between leaves only a harmless orphan sidecar, and recovery re-enriches
idempotently.

### Content triage (stage 3)

Files the classifier labels **unknown** are handed to a third pool that sorts
them into two coarse buckets so downstream tooling can treat them differently:

- **`data/binary/`** — the file looks like binary data.
- **`data/text/`** — the file looks like text.

The test is a **UTF-8 sniff**: read the first 8000 bytes and call the file text
when that window is valid UTF-8 and holds no control bytes outside the text-safe
set (tab, newline, CR, and friends, plus ESC for ANSI-colored logs); otherwise
binary. It's cheap (no full read) and Unicode-aware — unlike the older NUL-byte
or printable-ASCII heuristics, it keeps non-ASCII text (accents, CJK, emoji) in
`text/` instead of misfiling it, while binary formats — which rarely form valid
UTF-8 near their start — still land in `binary/`. A NUL byte is valid UTF-8 but
not a text control byte, so it still reads as binary. A multi-byte character
split by the 8000-byte boundary is trimmed before the check so it isn't mistaken
for malformed bytes. An empty file is treated as text. `binary/` is terminal on
the live path (but is the input the offline **stage-5 discovery** sweeps — see
below); `text/` is handed to stage 4 (`src/content.jl`).

### Language enrichment (stage 4)

Files that stage 3 sorts as **text** are handed to a fourth pool that identifies
their language and writes a `.meta.json` sidecar, mirroring the stage-2
known-file enrichment. Two detectors run per file:

- **natural language** — [`Languages.jl`](https://github.com/JuliaText/Languages.jl)'s
  `LanguageDetector` (a Julia port of the `whatlang` n-gram model) reads a bounded
  prefix (up to `LANG_SAMPLE_BYTES`, 64 KiB) and reports the language's English
  name, ISO 639-3 code, and a confidence in `[0,1]`. Pure Julia, no subprocess.
  The detector is built once at startup and shared read-only across the pool.
- **programming language** — the [`github-linguist`](https://github.com/github-linguist/linguist)
  CLI recognizes source and markup by extension + content heuristics (e.g.
  `Python`, `Markdown`). Plain prose reports as `Text` and unrecognized content as
  `null`; both collapse to *no programming language*.

The sidecar schema:

| field | meaning |
|---|---|
| `id`, `original_name` | from intake |
| `file_size` | bytes (authoritative, from intake) |
| `content_type` | always `"text"` |
| `language` | natural-language English name (e.g. `English`), or `null` |
| `language_code` | ISO 639-3 code (e.g. `eng`), or `null` |
| `language_confidence` | detector confidence in `[0,1]`, or `null` |
| `programming_language` | e.g. `Python`, `Markdown`, or `null` |
| `error` | set if natural-language detection produced nothing |

> **`github-linguist` and the git-repo quirk:** run against a path *inside* a git
> repository, linguist reads the file's committed git blob, not the on-disk bytes
> — and an untracked file (which everything under `data/` is) has no blob, so it
> crashes. Stage 4 sidesteps this by copying each file to a fresh temp dir under
> `/tmp` (outside any repo, preserving the name so extension heuristics still
> fire) and pointing linguist there.
>
> Programming-language detection is **best-effort**: if `github-linguist` is
> missing (a startup warning, not a fatal error, unlike `exiftool`), fails, or
> times out (`FS_LINGUIST_TIMEOUT`, default 30s), `programming_language` is simply
> `null` and the file still completes. Natural-language detection failing produces
> a **degraded sidecar** (with an `error` note) rather than a quarantine, because
> the file is still wanted text.

Like stage 2, the sidecar is committed **before** the file is moved into
`data/text_done/`, so the file's presence there always implies its sidecar is
present; recovery re-enriches idempotently (`src/language.jl`).

### Unknown-format discovery (stage 5, offline)

The `binary/` sink from stage 3 is the pile of genuinely *unrecognized* files.
Stage 5 mines it for **recurring new file formats** by clustering files on their
header bytes — a growing catalog of discovered formats, each with a magic-byte
signature that can eventually be promoted into the classifier's fast path. Unlike
stages 1–4 it is **not on the request hot path**: it is a single-owner *batch*
process (the catalog is mutable shared state, the opposite of the stateless
classifier), and because promotion is human-gated nothing here is
latency-sensitive. The full rationale — and the assumptions we deliberately
rejected — live in [`model/DESIGN_clustering.md`](model/DESIGN_clustering.md).

The model (`src/cluster.jl`, base-Julia, no extra deps) is a Dirichlet-process
mixture of **per-position categoricals** over the first 32 header bytes, on a
257-symbol alphabet (byte `0–255` plus a `past-EOF` symbol so short fixed-length
formats are modeled honestly). Bytes are treated as **categorical, not numeric**
— `0x89` and `0x88` are not "close" — so this deliberately does *not* reuse the
classifier's `[0,1]` byte scaling. A fixed uniform **background** component
absorbs structureless (compressed/encrypted) blobs so they don't mint spurious
clusters. A cluster's spiked positions become a libmagic-style signature;
clusters with enough members and enough fixed positions self-**nominate** for
promotion (a human does the one irreversible step, redefining "known").

**Status:** the offline science (phase A) is implemented and calibrated; the live
catalog process (phase B) is designed and its scoring core (`assign_file`) is in
place, but its batch-runner plumbing is not yet built.

Calibration is its own offline script (like training — never in the request
path), scored against magic-collapsed ground truth (so `docx`≡`zip` and the whole
ELF family count as one format each, which is the *correct* answer, not an error):

```bash
julia --project=. bin/cluster_calibrate.jl [training_set_dir]   # defaults to ../training_set
```

It grid-tunes the hyperparameters to maximize Adjusted Rand Index against known
formats and cross-checks against a model-free NCD (gzip) baseline. On the 700-file
training corpus the calibrated defaults (`n=32`, `α=1.0`, `β=0.1`) recover the
known formats at **ARI 0.77** (0.885 excluding tar), with `gzip`, `pkzip`
(`docx`+`zip` merged), and `jpeg` forming clean, promotable clusters; the NCD
baseline agrees. See `DESIGN_clustering.md` §11 for the full results, including the
one known limitation (ELF and these tarballs share a long run of header zero-
padding and merge — the v2 fix is inverse-entropy position weighting).

## The queue seam (→ RabbitMQ later)

The HTTP handler and workers only ever call `enqueue!`, `dequeue!`, and
`close!` on a `JobQueue` (see `src/queue.jl`). Today that's an in-process
`ChannelQueue`. To move to RabbitMQ (or any broker), implement a new `JobQueue`
subtype with those three methods and swap the construction in `run` — no handler
or worker code changes.

## Running

```bash
# install deps (first time)
julia --project=. -e 'using Pkg; Pkg.instantiate()'

# external tools: exiftool (stage 2, required) and github-linguist (stage 4,
# optional — programming-language detection). e.g. on Debian/Ubuntu:
#   apt install libimage-exiftool-perl
#   gem install github-linguist

# start the server; -t sets the number of OS threads available to workers
julia --project=. -t auto bin/server.jl
```

## Shutdown

Both SIGINT and SIGTERM trigger the same idempotent graceful drain
(stop serving → close queue → wait for workers → exit):

- **SIGINT** is caught as an `InterruptException` (we call
  `Base.exit_on_sigint(false)`), so shutdown is clean and quiet.
- **SIGTERM** can't be intercepted directly — Julia blocks it on worker threads
  and handles it in its own runtime, so a user `signal()` handler never fires.
  Instead we hook the drain into an `atexit` handler, which Julia's SIGTERM path
  does run. Caveat: Julia prints its own `signal 15: Terminated` backtrace
  *before* `atexit` runs. It's harmless noise — the drain still completes right
  after it — but if you want a fully quiet stop under a process manager,
  configure it to send SIGINT instead (systemd: `KillSignal=SIGINT`; Docker:
  `STOPSIGNAL SIGINT`). Give the stop timeout enough headroom to drain
  in-flight work (systemd: `TimeoutStopSec`).

## File classifier

Each file is scored by a fixed-structure neural network (Lux.jl) that answers a
single binary question: is this file **known** (like the types in the training
set) or **unknown**? It's novelty detection, not exact file-typing — it won't
tell you "PDF", just "this looks like something I was trained on, or not".

- **Features:** the first 16 bytes + last 16 bytes of the file, each scaled
  0–255 → `[0,1]`, giving a 32-dim input. Files under 32 bytes can't form that
  window and are classified `unknown` without touching the model.
- **Architecture:** `Dense(32→64,relu) → Dense(64→16,relu) → Dense(16→2)`,
  raw logits; decision is `argmax` (class 1 = known, class 2 = unknown).
- **Artifact:** trained weights live in `model/classifier.jld2` (committed), so
  the server just loads them at startup. Missing/unreadable ⇒ the server fails
  fast rather than run without classification.
- **Effect today:** *annotate-only*. The class is logged
  (`classification=known|unknown`) but every file still moves to `done/`; the
  classifier can't misroute real files while it's unproven.

The architecture and byte→feature mapping are defined once in `src/model.jl` and
shared by the trainer and the server, so they can't drift apart.

### Training

Training is a separate, offline script — it never runs in the request path:

```bash
julia --project=. bin/train.jl <positives_dir> [negatives_dir]
```

- **positives_dir** — every file in it (≥32 bytes) is a "known" example.
- **negatives_dir** *(optional)* — a grab-bag of *other* real file types used as
  "unknown" examples. Negatives are generated ~1:1 with positives, split 50/50
  between uniform-random byte vectors and grab-bag files. With no grab-bag dir,
  negatives are all random (weaker: the net may just learn "high entropy =
  unknown" rather than your actual types, so a grab-bag of real off-distribution
  files is recommended).

The script uses an 80/20 seeded split, reports validation accuracy, and writes
`model/classifier.jld2` (path overridable via `FS_MODEL_PATH`). A fixed seed
(`FS_TRAIN_SEED`, default 42) drives negative generation, the split, and weight
init, so the artifact is exactly regenerable from the same inputs.

## Configuration (environment variables)

| Variable            | Default        | Meaning                                  |
|---------------------|----------------|------------------------------------------|
| `FS_HOST`                 | `127.0.0.1`    | Bind address                                 |
| `FS_PORT`                 | `8080`         | Port                                         |
| `FS_WORKERS`              | `nthreads()`   | Stage-1 (classification) worker tasks        |
| `FS_QUEUE_CAPACITY`       | `1000`         | Max pending intake jobs before `503`         |
| `FS_KNOWN_WORKERS`        | `nthreads()`   | Stage-2 (enrichment) worker tasks            |
| `FS_KNOWN_QUEUE_CAPACITY` | `1000`         | Max pending enrichment jobs (then backpressure) |
| `FS_UNKNOWN_WORKERS`      | `nthreads()`   | Stage-3 (content triage) worker tasks        |
| `FS_UNKNOWN_QUEUE_CAPACITY` | `1000`       | Max pending triage jobs (then backpressure)  |
| `FS_TEXT_WORKERS`         | `nthreads()`   | Stage-4 (language enrichment) worker tasks   |
| `FS_TEXT_QUEUE_CAPACITY`  | `1000`         | Max pending language jobs (then backpressure) |
| `FS_SPOOL_DIR`            | `data/spool`   | Incoming files (pending classification)      |
| `FS_KNOWN_DIR`            | `data/known`   | Classified-known, awaiting enrichment        |
| `FS_UNKNOWN_DIR`          | `data/unknown` | Classified-unknown, awaiting content triage  |
| `FS_BINARY_DIR`           | `data/binary`  | Stage-3 sink: unknown files that look binary  |
| `FS_TEXT_DIR`             | `data/text`    | Classified-text, awaiting language enrichment |
| `FS_DONE_DIR`             | `data/done`    | Enriched known files (+ `.meta.json`)        |
| `FS_TEXT_DONE_DIR`        | `data/text_done` | Enriched text files (+ `.meta.json`)       |
| `FS_FAILED_DIR`           | `data/failed`  | Files whose processing threw                 |
| `FS_MODEL_PATH`           | `model/classifier.jld2` | Classifier artifact loaded at startup |
| `FS_EXIFTOOL_TIMEOUT`     | `30`           | Seconds before a stuck exiftool is killed    |
| `FS_LINGUIST_TIMEOUT`     | `30`           | Seconds before a stuck github-linguist is killed |
| `FS_CLUSTER_DIR`          | `data/binary`  | Stage-5 input: the unknown/binary pile to sweep |
| `FS_CLUSTER_N`            | `32`           | Header bytes modeled per file                |
| `FS_CLUSTER_ALPHA`        | `1.0`          | CRP concentration (propensity to spawn new formats) |
| `FS_CLUSTER_PSEUDOCOUNT`  | `0.1`          | Dirichlet pseudocount β (calibrated)         |
| `FS_CLUSTER_BG_MASS`      | `5.0`          | Mass of the uniform background component      |
| `FS_PROMOTE_MIN_MEMBERS`  | `20`           | Cluster size threshold for promotion nomination |
| `FS_PROMOTE_MIN_MAGIC`    | `3`            | Required fixed signature positions to nominate |

> To get real parallelism, start Julia with enough threads (`-t N`) to cover all
> pools. If `FS_WORKERS + FS_KNOWN_WORKERS + FS_UNKNOWN_WORKERS + FS_TEXT_WORKERS`
> exceeds available threads you'll get a warning (non-fatal) and workers will
> share threads.

## Usage

```bash
# health check
curl http://127.0.0.1:8080/health
# {"status":"ok"}

# upload one or more files (multipart/form-data)
curl -F "a=@report.pdf" -F "b=@data.csv" http://127.0.0.1:8080/upload
# 202 {"accepted":[{"id":"<uuid>","name":"report.pdf"}, ...]}
```

Each file in a request becomes its own job. Responses:

- `202 Accepted` — all files spooled and queued (with per-file job IDs)
- `400 Bad Request` — not multipart, or no files present
- `503 Service Unavailable` — queue full, retry later
- `500 Internal Server Error` — failed to write a file to disk

## Layout

```
src/
  FileServer.jl   module + run() (startup, recovery, workers, serve, shutdown)
  config.jl       Config struct + env parsing
  job.jl          Job (the queue reference)
  queue.jl        JobQueue seam + in-process ChannelQueue
  spool.jl        filename sanitizing, spool/move, startup recovery
  model.jl        NN architecture + byte→feature mapping (shared with trainer)
  classify.jl     load artifact + classify a file at inference time
  metadata.jl     exiftool extraction + normalized sidecar (stage 2)
  content.jl      binary-vs-text sniff for unknown files (stage 3)
  language.jl     natural + programming language enrichment for text (stage 4)
  cluster.jl      header-byte clustering for unknown-format discovery (stage 5, offline)
  worker.jl       parametrized worker loop + classify/enrich/triage/language handlers
  server.jl       HTTP routes/handlers
bin/
  server.jl           entry point
  train.jl            offline training script → model/classifier.jld2
  cluster_calibrate.jl offline stage-5 hyperparameter calibration + NCD baseline
model/
  classifier.jld2       committed trained weights (loaded at startup)
  DESIGN_clustering.md  stage-5 design rationale + calibration results
```