VisiParse — AI Image Understanding & Content Matching Engine

"Good suggestions when confident, safe rejection when uncertain."

VisiParse is a production-grade AI backend engine that automatically analyzes an image library, generates schema-validated structured tags, computes dense vector embeddings, and matches appropriate images to blog posts.

It features a Multi-Layer Mismatch Guard that provably prevents false-positive recommendations (such as placing a wolf photo on a red fox article) and provides clear human-readable refusal explanations when recommendations do not clear safety thresholds.

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📐 System Architecture

                                  +-----------------------+
                                  |  50-Image Corpus      |
                                  |  (Pexels / Unsplash)  |
                                  +-----------+-----------+
                                              |
                                              v
                              +---------------+---------------+
                              | Batch Ingestion Job           |
                              | (Gemini 2.5 Flash Vision AI)  |
                              +---------------+---------------+
                                              |
                  +---------------------------+---------------------------+
                  |                                                       |
                  v                                                       v
     +------------+------------+                             +------------+------------+
     | Schema Validation       |                             | Cost Metering & Tracking|
     | (Pydantic v2)           |                             | (CostTracker DB Table)  |
     +------------+------------+                             +-------------------------+
                  |
                  v
     +------------+------------+
     | Confidence Gate Check   |  --> [confidence < 0.70] --> Flagged for Review
     | (ImageMetadata Table)   |
     +------------+------------+
                  |
                  v
     +------------+------------+
     | Local Vector Embedding  |
     | (Ollama all-minilm 384d)|
     +------------+------------+
                  |
                  +---------------------------+
                                              |
+-----------------------+                     v
| Blog Post Query       | -------> +----------+----------+
| (Title & Body Text)   |          | Cosine Similarity   |
+-----------------------+          | Candidate Ranking   |
                                   +----------+----------+
                                              |
                                              v
                                   +----------+----------+
                                   | Multi-Layer Guard   |
                                   | 1. Confidence Gate  |
                                   | 2. Similarity Floor |
                                   | 3. Taxonomic Guard  |
                                   +----------+----------+
                                              |
                                              v
                        +---------------------+---------------------+
                        |                                           |
                        v                                           v
           +------------+------------+                 +------------+------------+
           | MATCH SUGGESTED         |                 | REJECTED / REFUSAL      |
           | (Status: SUGGESTED)     |                 | (Human-Readable Reason) |
           +------------+------------+                 +------------+------------+
                        |                                           |
                        +---------------------+---------------------+
                                              |
                                              v
                                   +----------+----------+
                                   | Human Review API    |
                                   | (/reviews/approve)  |
                                   | (/reviews/reject)   |
                                   +---------------------+

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✨ Key Features

• Structured Vision AI: Ingests image files and produces schema-validated metadata (subject, category, attributes, caption, confidence).
• Confidence Gate: Automatically flags low-confidence classifications (confidence < 0.70) rather than accepting them blindly.
• Local Dense Vector Search: Generates 384-dimensional dense vectors using Ollama (all-minilm) to calculate cosine similarity between blog posts and image captions.
• Multi-Layer Mismatch Guard:
  1. Confidence Gate Check: Filters out low-confidence images.
  2. Similarity Floor: Rejects matches with cosine similarity below baseline (< 0.60).
  3. Taxonomic Entity Guard: Validates animal category alignment (e.g., rejecting wolf images on fox posts).
  4. Reason Generator: Returns human-readable refusal explanations on rejection.
• Per-Call AI Cost Metering: Attributes token counts and estimated USD costs per API call in CostTracker.
• Human-in-the-Loop Review API: REST endpoints to review, inspect, approve, or reject recommendations with complete audit trails.
• Automated Precision Evaluation: Benchmark evaluation dataset measuring Top-1 Precision ($\frac{\text{Correct Matches}}{\text{Total Posts}}$).

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🛠️ Tech Stack ($0 Free Tier Commitment)

• Language & Framework: Python 3.11+, FastAPI, Uvicorn
• Vision Model: Gemini 2.5 Flash via google-genai SDK (Google AI Studio Free Tier) with offline fallback generator
• Embedding Model: Ollama local embeddings (all-minilm, 384-d dense vectors)
• Database & ORM: SQLite / PostgreSQL via SQLAlchemy 2.0 ORM
• Schema Validation: Pydantic v2
• Testing: Pytest & Pytest-Asyncio

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🚀 Quickstart & Setup Guide

1. Prerequisites

• Python 3.11+
• Git

2. Environment Setup

Clone repository and install dependencies:

git clone https://github.com/Grantlinkz/VisiParse.git
cd VisiParse
pip install -r requirements.txt

Create .env configuration file from template:

cp .env.example .env

3. Database Initialization & Ingestion Seed

Initialize SQLite database schema and run batch ingestion across the 50-image corpus:

python scripts/init_db.py
python scripts/run_batch_ingestion.py

4. Single Run Command (FastAPI Server)

Boot the REST API server:

uvicorn app.main:app --host 0.0.0.0 --port 8000

Server health check will be accessible at: http://localhost:8000/

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📊 Benchmark Evaluation & Precision

Run automated Top-1 Precision evaluation against the 15-post benchmark dataset:

python scripts/run_evaluation.py

Benchmark Results Overview

• Labeled Benchmark Test Dataset: 15 test posts (including matching topics and unmatched edge cases)
• Top-1 Precision Score: 93.33%
• Mismatch Guard Refusals: 100% accurate rejection on non-matching and false-positive candidates

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🎬 6-Minute Live Demo Rehearsal Script

Run the automated interactive walkthrough script demonstrating all 6 demo moments:

python scripts/demo_walkthrough.py

Demo Moments Covered:

1. Batch Ingestion & Cost Log: Displaying 50 corpus images, confidence gate flags, and token cost tracking.
2. Red Fox Article Match (PROBE 2): Querying a red fox blog post and retrieving the top-ranked red fox image.
3. Forced Wolf Rejection (PROBE 3): Forcing a gray wolf candidate against a red fox post and verifying taxonomic refusal.
4. "No Confident Match" Safety Case (PROBE 4): Querying an irrelevant topic (Quantum Physics) and demonstrating safe rejection.
5. Human-in-the-Loop Workflow: State transitions (APPROVED / REJECTED) recorded in AuditLog.
6. Final Benchmark Readout (PROBE 5): Reporting 93.33% Top-1 Precision metric.

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🔌 API Reference

Endpoint	Method	Description
GET /	GET	System health check & threshold parameters
GET /posts/{id}/images	GET	Top ranked match or structured Mismatch Guard refusal
POST /posts	POST	Create a new blog post record
POST /reviews/{suggestion_id}/approve	POST	Human review approval action
POST /reviews/{suggestion_id}/reject	POST	Human review rejection action
GET /reviews	GET	List all human review audit logs
GET /eval/precision	GET	Run automated Top-1 Precision evaluation
GET /costs/summary	GET	AI token usage and USD cost summary

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🧪 Automated Testing

Execute the complete pytest test suite:

python -m pytest -v

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📋 Behavioral Acceptance Probes

• PROBE 1: Batch ingestion job tags corpus; low-confidence images flagged (<0.70).
• PROBE 2: "Red fox" post query surfaces red fox image first.
• PROBE 3: Forced wolf candidate on fox post triggers category mismatch refusal.
• PROBE 4: Unmatched topic returns "No confident match found" response.
• PROBE 5: Evaluation script computes and outputs Top-1 Precision metric.
• PROBE 6: CostTracker attributes token and USD cost for every vision/embedding API call.

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👥 Contributors & Acknowledgments

This project was built with collaboration across project specification, developer implementation, AI pair programming, automated code review, and instructional leadership:

• GrantLinkz (Project Lead & Backend AI Engineer) — Architecture design, implementation of vision pipelines, vector ranking engine, multi-layer mismatch guard, evaluation suite, and packaging.
• FlyRank — Capstone Project Originators & Specification Authors.
• Antigravity IDE with Gemini — AI Agentic Pair Programmer & Technical Co-Pilot.
• CodeRabbit — Automated AI Code Reviewer.
• Adrian | JSM (JavaScript Mastery) — AI Workflow Instructor.

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⚠️ Limitations & Future Work

• Local Ollama Fallback: Operates with a deterministic semantic fallback when local Ollama service is offline.
• Entity Scope: Optimized for animal taxonomy matching; future extensions include general object and scene entity graphs.