AI Car

This document provides a detailed technical overview of the AI pipeline used in the Hospital Recommendation System.

1. Project Overview

The system is designed to provide personalized hospital recommendations based on user-described symptoms. It leverages a hybrid approach combining Large Language Models (LLM) for semantic understanding and Sentence Embeddings for efficient retrieval.

2. Technical Architecture

Phase A: Semantic Subject Extraction (Gemini)

When a user enters symptoms, the system utilizes the Google Gemini to bridge the gap between "layman's terms" and "medical terminology."

1. Initial Analysis: The raw symptom text is sent to Gemini to understand the medical context.
2. Subject Refinement: A second targeted prompt is used to extract the specific Expected Medical Department (e.g., "내과", "안과").
3. Prompt Engineering: The system explicitly requests "plain text" without markdown formatting or special characters to ensure the output is compatible with the downstream embedding model.
4. Localization: The term "성북구" (Seongbuk-gu) is appended to the extracted department to prioritize local results during the similarity search.
(It can be modified, In my case, I used "성북구" because I live in Seongbuk-gu)

Phase B: Advanced Hospital Embedding Strategy

Unlike simple text search, this system uses a Weighted Multi-Field Embedding approach to represent hospitals in a high-dimensional vector space.

• Base Model: jhgan/ko-sroberta-sts
  • Type: SBERT (Sentence-BERT) based on the RoBERTa architecture.
  • Specialization: Fine-tuned specifically for Korean Semantic Textual Similarity (STS).
• Weighted Combination:
  For every hospital in the database (hospital_info.csv), a single representative vector is created by embedding four fields separately and combining them using specific weights:

  Field	Weight	Description
  medical_subject	0.4	The primary factor for matching symptoms.
  address	0.4	Ensures geographic relevance.
  hospital_name	0.1	Provides identity context.
  opening_hours	0.1	Adds temporal context.

• Caching: To ensure low-latency responses, these embeddings are pre-computed and stored as .pt (PyTorch) files in the cache/ directory.

Phase C: Semantic Ranking & Retrieval

The retrieval process finds the "mathematical nearest neighbors" to the user's query.

1. Query Vectorization: The query (Extracted Subject + "성북구") is converted into a vector using the same ko-sroberta-sts model.
2. Similarity Calculation: The system computes the Cosine Similarity between the query vector and the pre-computed hospital embedding matrix.
3. Top-K Retrieval: The k hospitals (default: 10) with the highest similarity scores are selected.
4. Formatting: Results are mapped back to the original CSV data and displayed to the user with details including Name, Subject, and Address.

3. Key Specifications

4. Pipeline Flow Summary

User input symptom → Gemini (Subject Extraction) → SBERT (Query Encoding) → Cosine Similarity vs. Weighted Hospital Matrix → Top-10 Ranking → UI Display