Hybrid Image Search Dev Log #12 — Reranking Pipeline Fixes, Dual-Batch Generation, Frontend Sprint

Fri, 10 Apr 2026 00:00:00 +0900

Previous: Hybrid Image Search Dev Log #11

Dev log #12 covers three major tracks. First, I tracked down and fixed four bugs that had been quietly breaking the reranking pipeline. Second, I replaced the single-tone + angle-image generation approach with a dual-batch system (3-tone and 5-tone) driven by text-based angle directives. Third, I implemented a batch of frontend components — AnglePicker, ReactionButtons, LikesTab, ImageLightbox, and FeedbackModal. Total diff: 42 files, +2,749/-662 lines.

Overall Work Flow

flowchart TD
 A["Reranking pipeline fixes"] --> B["Restore CE weight from 0.03"]
 A --> C["Switch to Korean-finetuned model"]
 A --> D["expanded_query → original_query"]
 A --> E["geometric mean → arithmetic mean"]

 F["Multi-tone + angle text overhaul"] --> G["angle_text.py <br/> lens.py presets"]
 F --> H["Dual batch <br/> 3-tone / 5-tone"]
 F --> I["person-intent <br/> auto model injection"]

 J["Frontend sprint"] --> K["AnglePicker"]
 J --> L["ReactionButtons <br/> LikesTab"]
 J --> M["ImageLightbox <br/> FeedbackModal"]

 E --> F
 H --> J

1. Reranking Pipeline — Four Bugs Fixed at Once

Reranking was effectively doing nothing. When I traced the root cause, I found not one bug but four.

Problem Analysis

Bug	Symptom	Impact
CE weight 0.03	Cross-Encoder scores contributed only 3% to the final score	Reranking had almost zero effect
Model not fine-tuned on Korean	Poor relevance judgment for Korean queries	Degraded search quality
Passing `expanded_query` to CE	Expanded query distorted CE scores away from the original intent	Irrelevant results ranked higher
Geometric mean	A single low sub-score dragged down otherwise strong partial matches	Good partial matches disappeared from results

Fix

All four were fixed together. CE weight was restored to a sensible value, original_query replaced expanded_query as the Cross-Encoder input, and geometric mean was replaced with arithmetic mean.

I also attempted a model upgrade. bge-reranker-v2-m3 (568M parameters) showed noticeably better Korean performance, but took 16 seconds per component on an EC2 CPU — not viable in production. I rolled back to mmarco-mMiniLMv2 (136M) and kept the other three fixes.

flowchart LR
 subgraph Before["Before"]
 Q1["expanded_query"] --> CE1["CE <br/> weight=0.03"]
 CE1 --> GM["geometric mean"]
 end

 subgraph After["After"]
 Q2["original_query"] --> CE2["CE <br/> weight restored"]
 CE2 --> AM["arithmetic mean"]
 end

 Before -->|"4 bugs fixed"| After

2. Multi-Tone + Angle Text Overhaul

The previous approach injected a single tone and a single angle reference image per generation. This sprint replaced that entirely.

Angle: From Images to Text Directives

I removed angle reference images and switched to text-based presets. angle_text.py defines presets like “45-degree downward angle,” “overhead,” and “eye level.” lens.py adds per-category focal length presets (e.g., 85mm for portraits, 24mm for landscapes).

The advantage is straightforward: no more cost of finding and managing angle reference images, and finer control is possible at the prompt level.

Tone: Single → Dual Batch (3-tone / 5-tone)

A single generation request now runs a 3-tone batch and a 5-tone batch in parallel. Users can compare results via a tone3/tone5 toggle in the frontend. The DB schema was updated to add multi-tone columns, and log_generation was updated accordingly.

person-intent Auto Model Injection

An intent_person field was added to the Gemini classification prompt. If the user’s reference images contain no people but the query implies a person, the system automatically injects a model image from the refs/model_image_ref/ directory. This covers the case where the intent is person-focused but the uploaded references don’t include any people.

3. Frontend Feature Sprint

Frontend components were built out to match the backend changes.

New Components

AnglePicker — Search and select angle presets. Fetches the list from /api/angle-presets. Integrated into the detail page so the angle can be changed after generation.
ReactionButtons — Quick emoji reaction buttons for generated images.
LikesTab — A gallery tab collecting all images the user has liked.
ImageLightbox — Expanded view on thumbnail click.
FeedbackModal — A modal for submitting detailed text-based feedback.

Dual-Batch UI

MAX_REFS was raised to 7 on the frontend, and dual-batch generation is now supported. The detail page shows multi-tone metadata and a tone3/tone5 toggle.

API Endpoints

Added GET /api/angle-presets, reaction and feedback endpoints, and updated API interface type definitions.

4. Production Server Debugging & Infrastructure

Grafana Data Missing

DEPLOYMENT_ENV was not set, and a missing newline in the .env file was causing the S3 path to be assembled incorrectly. Both were fixed to restore monitoring data collection.

Missing DB Migration

The injected_model_filename column was absent from the production database, causing the auto model injection feature to fail. A migration script was added to resolve this.

Infrastructure Improvements

Switched prod SSH key to ed25519 and added a lifecycle guard
Closed open ports and configured nginx reverse proxy
Removed duplicate 401 errors that occurred during startup auth checks
Fixed mobile responsive layout
Updated Security Group descriptions to match AWS validation requirements
Removed APP_ENVIRONMENT from ecosystem.config.js

Wrap-Up

The most meaningful work in this sprint was the reranking fix. Four bugs coexisted in a way that masked each other’s effects — fixing them one at a time actually made results worse in some cases. Only after fixing all four simultaneously did reranking start working correctly.

The dual-batch generation and text-based angle directives haven’t gathered enough user feedback yet. The next step is to use reaction and feedback data to validate whether 3-tone or 5-tone is preferred, and whether text angle presets actually outperform angle reference images.

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