Messaging Reactions, Unread Badges, and a 136-Test Import Gauntlet

A big day on the Parthenon platform with two distinct workstreams landing in parallel: a fully-featured emoji reaction system for the collaborative messaging layer, and a comprehensive 136-test import suite designed to stress-test every data ingestion pathway the platform supports — including some deliberately nasty adversarial inputs.

Emoji Reactions Come to the Messaging Layer

The most visible feature work today was a complete emoji reaction system built into the commons messaging components. This was a multi-commit layered build, with each component composing cleanly on top of the last.

Component Architecture

The reaction system is built from four composable pieces:

• EmojiPicker — A lightweight picker component that serves as the entry point for adding a reaction to any message.
• ReactionTooltip — Displays who reacted with a given emoji when hovering over a reaction pill, giving users full visibility into reaction attribution.
• ReactionPills — The core display component. Renders grouped emoji reactions with counts, supports toggling your own reaction on/off, and integrates ReactionTooltip for hover details.
• React option in message action menu — Wires the EmojiPicker into the per-message action menu so users can trigger a reaction from the familiar "..." menu.

ReactionPills has been integrated into both MessageItem (inline thread messages) and ThreadView replies, so reactions are consistently available across every message surface in the UI.

Real-Time Sync via Echo

Reactions would be useless without real-time sync. A ReactionUpdated event listener was added to the Echo websocket hook, meaning reaction counts update live across all connected clients without a page refresh. This follows the same pattern already established for message delivery and read receipts.

Unread Count Badges on Channel List

Separately, the channel list sidebar now shows unread count badges on each channel. This is a foundational UX improvement — users can now see at a glance which channels have activity since their last visit, rather than having to click into each one. The badge integrates with the existing read-state tracking and will decrement correctly as messages are consumed.

A round of code quality fixes was applied across Chunks 2–4 of this feature work, addressing review findings before merge.

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The Import Test Gauntlet

On the backend, the feature/fhir-omop-ig-compliance branch received a substantial test investment: 136 tests across 7 test files, covering every import pathway from raw file upload through AI-assisted schema mapping to final validated ingestion. This is the kind of test infrastructure that pays dividends for months.

What's Being Tested

The suite covers both the PHP (Pest) backend and the Python AI service (pytest):

Layer	File	Focus
PHP	GisImportTest.php	File parsing, encoding detection, geo code detection, column stats
PHP	IngestionPipelineTest.php	CsvProfiler, type inference, PII detection
PHP	GisImportApiTest.php	API endpoints, auth, upload validation, workflow
PHP	IngestionApiTest.php	Full pipeline: upload → profile → schema map → concept review → validate
PHP	ImportSecurityTest.php	Content sniffing, injection, path traversal, permission matrix, rate limiting
Python	test_gis_import.py	GIS analyzer endpoints, ChromaDB learning, Ollama graceful degradation
Python	test_schema_mapping.py	Regex fallback, confidence scoring, CDM target mapping

The IngestionApiTest.php alone covers 37 scenarios with 129 assertions — essentially a full integration test of the Abby AI-assisted import workflow end-to-end.

The Fixture Library

Perhaps the most valuable artifact from this work is the 22-file test fixture library at backend/tests/fixtures/imports/. It's organized into three tiers:

• Golden files (5): Well-formed reference inputs like county-svi.csv, hospitals.csv, and boundaries.geojson that should always parse cleanly.
• Messy files (6): Real-world problem cases — actual BOM bytes in bom-utf8.csv, ISO-8859-1 encoded latin1-fips.csv, mixed line endings (CR/LF/CRLF in the same file), duplicate headers, and quoted commas.
• Adversarial files (5): Zero-byte empty.csv, headers-only files, binary data disguised as CSV, a no-geo file to test graceful fallback, and injection-headers.csv with SQL injection and XSS payloads in the column headers themselves.

The adversarial fixtures are particularly important for the FHIR/OMOP compliance branch — research data pipelines frequently receive files from external systems with no guarantees about encoding, structure, or intent. Having these as reproducible test cases means regressions in security or resilience will be caught immediately.

AI Service Resilience

The Python tests include explicit coverage for Ollama graceful degradation in the GIS analyzer. If the local LLM is unavailable, the service should fall back to rule-based heuristics rather than hard-failing — a critical reliability property for a platform that may run in air-gapped or resource-constrained environments.

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What's Next

• The reaction system is feature-complete for the core messaging layer — next up is likely notification integration (e.g., notifying a user when someone reacts to their message) and persistence of reaction state across sessions.
• The import test suite sets the stage for the broader FHIR/OMOP IG compliance work on that branch. With the ingestion pipeline thoroughly exercised, the team can now refactor the CDM mapping logic with confidence.
• The ImportSecurityTest.php rate limiting coverage suggests we may be looking at API rate limiting hardening as an upcoming infrastructure task.