file-observer 1.14.0

Not a file watcher — a one-shot, read-only scan that emits a deterministic JSON manifest of what's in a directory before you ingest it.

File Observer

Know what's in your files before you open them.

A one-shot, read-only observation pass — not a file watcher. Point it at a directory, get one deterministic JSON manifest of what's inside, before you ingest it.

File Observer makes a single read-only pass over a directory and tells you exactly what's inside — file types, metadata, conversation patterns, author fingerprints, structural signals — all in one deterministic JSON manifest. You run it on demand; it doesn't stay resident or watch for changes. It reads everything. It changes nothing.

(If you already know Apache Tika: think a deterministic Tika built for pipelines.)

pip install file-observer
fo ./your-project --specialists

Scanned 4,366 files (3,526 text, 840 binary) in 31 directories.

1,163 supported (336 with specialist metadata). 3,203 unsupported extensions.
Quality: 676 clean, 3,690 degraded. 4 safety flags, 2 polyglots.

Vectors: author_aggregate found 64 distinct authors across 114 files.
chatlog matched 22 files. reference_tokens ran on 806 files (2,164 URLs,
382 paths, 262 @mentions). filename_patterns matched 84 of 4366 files.

Largest directories: tika-parsers (2,037), tika-pipes (459), tika-core (440).

That's the human-readable summary. The full manifest is structured JSON — here's a truncated file object so you can see the data contract before you install:

{
  "schema_version": "1.8",
  "context": { "scanner_version": "1.13.0", "logic_version": "1.4.3", "...": "…" },
  "files": [
    {
      "path": "docs/report.pdf",
      "mime_type": "application/pdf",
      "checksum_sha256": "9f86d081884c7d65…",
      "is_binary": true,
      "requires_specialist_tool": true,
      "specialist_tool": "pdf_extraction",
      "safety_flags": ["has_javascript"],
      "signal_provenance": {
        "requires_specialist_tool": { "layer": "derived", "method": "specialist_tools_registry", "trigger": "extension_match" }
      },
      "...": "…"
    }
  ],
  "vectors_collected": [
    { "vector_id": "chatlog", "method_version": 9, "identity_digest": "a3f1c2…", "...": "…" }
  ],
  "manifest_checksum": "7d2bafef…",
  "manifest_signature": { "algorithm": "hmac-sha256", "key_id": "default", "value": "…" }
}

Every derived field carries a signal_provenance entry; every vector an identity_digest; the whole manifest a checksum and optional HMAC signature.

New here? Walk through the tutorial (first scan → pipeline integration) or run the examples — self-contained, one per concept.

Package	file-observer
CLI	file-observer or fo (shorthand)
Version	1.13.0
Schema	1.8
Python	>= 3.12
License	AGPL-3.0 (commercial license available)
Tests	849 passed, 1 skipped; ran clean (zero fatal errors) — see "Validated at scale" below

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Why File Observer?

Your pipeline needs to know what it's processing before it processes it. File Observer is the observation layer that sits at the front of any document pipeline — ingestion, classification, OCR, embedding, audit. It tells the pipeline what's coming without touching the files. (Need to react to filesystem changes as they happen? That's a watcher like watchdog or watchfiles — a different tool.)

• Deterministic. Same files + same config = identical manifest, every time. Cross-environment variance is explained, never hidden.
• Auditable. Every derived field has a provenance trace — which method, which trigger, which inputs. Nothing is a black box.
• Honest. null means "not observed within bounds," not "not present." Safety flags are observations, not assessments. The scanner records; the consumer interprets.
• Verified. Cryptographic identity digests on every vector. HMAC-signed manifests. Chain-of-custody across incremental scans.

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What it observes

25 file types, 4 capability tiers

Tier	Runs for	What it extracts
Universal	Every file	Identity, checksum, MIME, file signatures, polyglot detection, routing flags
Baseline	Text files	Encoding, preview, tags, frontmatter, chatlog detection, reference tokens, filename patterns
Structural	Text files	Title, headings, CSV headers, JSON/YAML/XML/TOML keys, technology hints
Specialist	Supported formats (opt-in)	PDF pages, image dimensions, email envelopes, spreadsheet structure, document metadata

Supported specialist formats: .pdf, .png, .jpg, .msg, .eml, .xlsx, .xls, .docx, .doc, .rtf, .jsonl

5 observation vectors with cryptographic identity

Vector	What it finds
chatlog	Conversation patterns — turns, speakers (per-speaker counts/alternation), section markers. Detects prose transcripts and conversational JSON/JSONL across common schemas (role/from/speaker + text/value/content). Works on .txt, .md, .jsonl, .json.
reference_tokens	@mentions, wiki links, code blocks, URLs, emails, file paths, ticket numbers
author_aggregate	Cross-format author normalization. Spots template defaults vs real humans. (WHO authored.)
provenance	Production provenance — normalized toolchains (producer/creator via a closed table), production_years, and digitization (born_digital / scanned / ocr_detected / unknown). Cross-format: PDF + OOXML app.xml. (WHAT-TOOL / WHEN / digitization.)
filename_patterns	Date prefixes, version markers, numbered revisions, template names, UUIDs, copy suffixes

Each vector carries an identity digest (SHA-256). Same digest = same rules + same tuning = same output. Always. (These are observation vectors — named, fingerprinted observations — not embedding vectors for a vector database.)

Safety and integrity

• Safety flags — detects JavaScript in PDFs, macros in DOCX, OLE objects in RTF, external entities in XML
• Manifest checksum — SHA-256 over the canonical manifest
• HMAC signatures — optional signed manifests for audit chains
• Delta scanning — diff two manifests from separate runs to see added/modified/removed files. Snapshot-to-snapshot, not live change events.
• Per-directory summary — corpus shape visible at a glance
• Duplicate detection — files grouped by identical content checksum (quality.duplicate_clusters); surfaces redundant copies for migration/dedup
• Per-specialist stats — attempted/succeeded/failed per specialist tool, so extraction quality is visible, not implied

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Quick start

Install

pip install file-observer

# Optional: specialist format support
pip install "file-observer[msg]"       # .msg/.doc/.xls (OLE2 formats)
pip install "file-observer[security]"  # Hardened XML parsing
pip install "file-observer[dev]"       # Full dev environment

Optional: libmagic sharpens content-based MIME detection. As of v1.3 it's no longer required — without it (Windows, minimal containers) File Observer falls back to a built-in pure-Python content sniff for common binary formats (archives, images, data, media), then extension-based inference. Install it for the widest coverage:

sudo apt install libmagic1    # Debian/Ubuntu
brew install libmagic         # macOS
pip install python-magic-bin  # Windows (or rely on the pure-Python fallback)

Scan

# Quick scan
fo ./project

# Deep scan with specialist metadata
fo ./project --specialists

# Named profile with JSONL output
fo ./project --profile deep_extract --format jsonl

# Delta scan against a previous manifest, signed
fo ./project --previous-manifest ./last.json --signing-key-file ./key

Use in code

from pathlib import Path
from file_observer import Scanner, ScannerConfig, manifest_to_json

config = ScannerConfig(enable_specialists=True)
manifest = Scanner(source_dir=Path("./documents"), config=config).scan()

# Human-readable summary
print(manifest.summary)

# Find conversation logs
for f in manifest.files:
    if f.is_chatlog and f.specialist_metadata:
        chat = f.specialist_metadata["chatlog"]
        print(f"{f.path}: {chat['turn_count']} turns, {chat['speaker_labels']}")

# Triage via quality block
q = manifest.quality
print(f"{q.clean_files}/{q.total_files} clean, {q.safety_flags} safety flags")

# Write manifest
Path("manifest.json").write_text(manifest_to_json(manifest))

Every scan also produces a standalone Markdown report (report_v{version}_{timestamp}.md) — readable in any browser, shareable, no JSON parsing required.

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Use cases

Document pipeline preprocessing

Point File Observer at an incoming document folder before your ingestor touches it. Know which files need OCR, which have specialist metadata, which are mislabeled, and which carry safety flags — before processing begins.

AI training data curation

Scanning AI conversation logs, knowledge bases, and document corpora? File Observer detects chatlog patterns in .txt, .md, and .jsonl files, counts turns and speakers, and surfaces reference tokens (URLs, @mentions, code blocks) across thousands of files. Built for the datasets that train and evaluate language models.

Audit and compliance

Every field has a provenance trace. Every vector has a cryptographic identity digest. Manifests can be HMAC-signed with chain-of-custody across incremental scans. When the auditor asks "how do you know this file contains X?" — the manifest answers.

Knowledge management and vault analysis

Run File Observer against an Obsidian vault, a Confluence export, or a shared drive. The per-directory summary shows corpus shape instantly. Reference tokens reveal link density, cross-references, and structural patterns. Author aggregation spots template defaults vs real contributors.

Migration and deduplication

Moving files between systems? File Observer gives you checksums, MIME analysis, format signatures, and polyglot detection for every file. Delta scanning tracks what changed between runs. Filename patterns catch copy suffixes, numbered revisions, and UUID-named files.

Security triage

Safety flags surface JavaScript in PDFs, macros in DOCX files, OLE objects in RTF, and external entities in XML — without opening or executing anything. Surface them to your security pipeline, where your own policy decides quarantine or triage. The flags are structural observations, not verdicts — expect false positives and negatives, and tune your own thresholds.

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How it works

fo ./corpus --specialists
  |
  +-- Universal tier     Every file: checksum, MIME, signatures, routing
  +-- Baseline tier      Text files: encoding, preview, tags, chatlog detection
  +-- Structural tier    Text files: title, headings, keys, technology hints
  +-- Specialist tier    Format-specific: PDF, images, email, spreadsheets, documents
  +-- Vector pass        chatlog, reference_tokens, filename_patterns (per-file)
  +-- Corpus vectors     author_aggregate (after all files processed)
  +-- Summary            Human-readable paragraph + per-directory breakdown
  |
  +-- Output: manifest.json + report.md

One file failure never halts the scan. Errors are captured per-file, per-stage. The manifest is always complete.

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Configurable depth

Profile	Baseline	Specialists	Use case
fast_sort	8KB	Off	Quick triage, file routing
general	64KB	Off	Standard observation
deep_extract	1MB	On	Full metadata extraction

Per-extension overrides let you give specific formats more budget:

fo ./docs --specialists --extension-override .pdf:specialist_budget=524288

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Validated at scale

File Observer has run cleanly — zero fatal errors — across 12 real-world corpora totaling 28,756 files. (This measures robustness, not extraction accuracy; precision/recall benchmarks are planned.)

Corpus	Files	What it tested
Apache Tika	4,366	152 document specialists, 69 PDFs, 57 spreadsheets, 13 emails
OBS Studio	5,201	Large C/C++ project, 91 filename patterns
AutoGPT	3,945	AI platform, 1,612 @mentions; chatlog FP-hardening validation (raw detections cut sharply by v1.2.x)
FastAPI	3,002	Documentation-heavy Python, chatlog tuning validation
OpenPreserve	753	Adversarial format samples, 285 PDFs
Claude Code logs	125	Real AI conversation transcripts, JSONL chatlog detection
Flask, tmux, self-scan	11K+	Diverse code repos

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Documentation

Document	What it covers
Tutorial	Guided tour from first scan to pipeline integration — start here
Examples	Runnable, self-contained examples, one per concept
SCHEMA.md	The complete output surface (generated by --schema)
HISTORY.md	Every version from v0.1 to the current release, with specs and compliance reports
PUBLIC_CONTRACT.md	Consumer stability commitments — what you can rely on
LIMITATIONS.md	What File Observer deliberately doesn't do
CONVENTIONS.md	Internal naming, versioning, and tracking
v1.13.0 RFC Specification	Current release spec — --schema self-description. v1.0.0 RFC remains the binding schema-freeze contract.
v1.12.0 RFC Specification	Prior — encrypted + uncompressed-xref PDF residual closure.
v1.11.0 RFC Specification	Prior — --watch continuous trigger loop.

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API Reference

Core classes

Scanner(source_dir: Path, config: ScannerConfig | None = None)
Scanner.scan() -> ScanManifest

Configuration

ScannerConfig(
    enable_specialists=False,    # Enable format-specific extraction
    preview_max_chars=1000,      # Content preview length
    sample_size=8192,            # Binary detection sample
    baseline_max_bytes=65536,    # Text decode limit
    specialist_budget=131072,    # OOXML read budget
    format="json",               # "json" or "jsonl"
    exclude_hidden=False,        # Skip dot-files
    ignore_file=None,            # Path to .scannerignore
    previous_manifest=None,      # Delta scan reference
    signing_key=None,            # HMAC signing key
)

Output

manifest_to_json(manifest)      # Pretty-printed JSON
manifest_to_jsonl(manifest)     # NDJSON streaming format
manifest_to_markdown(manifest)  # Human-readable report

Key data classes

• ScanManifest — top-level: context, stats, quality, vectors_collected, summary, files[]
• FileRecord — per-file: path, mime, checksum, encoding, specialist_metadata, reference_tokens, filename_patterns, safety_flags, signal_provenance, errors
• ScanContext — environment fingerprint: versions, platform, dependencies
• VectorRecord — vector identity, digest, scope, applied count, summary

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Contributing

We welcome contributions. See CONTRIBUTING.md for the full guide.

Quick version:

1. Fork and clone
2. pip install -e ".[dev]" and run tests
3. Sign the CLA on your first PR
4. One concern per PR, tests required, determinism preserved

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License

File Observer is dual-licensed:

• Open source under AGPL-3.0 — use freely, contribute back
• Commercial license available for cases where AGPL terms don't fit

Which one applies to you

The AGPL is fine — no commercial license needed — for:

• Internal use: running File Observer inside your own organization, including on private servers, with no obligation to publish anything.
• Personal projects, research, and evaluation.
• Open-source projects that are themselves AGPL-compatible.

You likely want a commercial license if you:

• Embed File Observer in a proprietary product you distribute without releasing that product's source under the AGPL.
• Offer it over a network as a service (SaaS). The AGPL's network clause (§13) means that if users interact with a modified version over a network, you must offer them its complete corresponding source. A commercial license removes that obligation.
• Distribute File Observer (or a derivative) to third parties without the AGPL's source-disclosure requirements.

In short: AGPL obligations are triggered by distribution and by network use of modified versions, not by private internal use. If you're unsure whether your use triggers them, that's exactly what a commercial license resolves.

Contact [e-mail] for commercial terms.

This is a plain-language summary, not legal advice or a substitute for the license text. Where this summary and the license differ, the license governs.

Trademarks

Apache® and Apache Tika™ are trademarks of the Apache Software Foundation. The Unix file command is referenced descriptively. File Observer is an independent project and is not affiliated with, endorsed by, or sponsored by the Apache Software Foundation; any reference to Apache Tika is comparative only.

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Built by Russalo. The scanner records. The consumer interprets. The identity digest makes the recording auditable.