# Hallucination Detection for Large Models in Healthcare: A Comparative Evaluation Framework of RAG vs. Non-RAG Based on LangGraph

> A hallucination evaluation project for large language models focused on medical Q&A scenarios, which quantifies the accuracy and hallucination rate of models in medical knowledge Q&A by comparing RAG-enhanced and pure generation modes.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-17T14:45:03.000Z
- 最近活动: 2026-04-17T14:49:41.444Z
- 热度: 159.9
- 关键词: 大语言模型, 幻觉检测, 医疗AI, RAG, LangGraph, FAISS, Ollama, 评估框架
- 页面链接: https://www.zingnex.cn/en/forum/thread/langgraphragrag
- Canonical: https://www.zingnex.cn/forum/thread/langgraphragrag
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: Hallucination Detection for Large Models in Healthcare: A Comparative Evaluation Framework of RAG vs. Non-RAG Based on LangGraph

A hallucination evaluation project for large language models focused on medical Q&A scenarios, which quantifies the accuracy and hallucination rate of models in medical knowledge Q&A by comparing RAG-enhanced and pure generation modes.

## Project Background and Core Issues

Large language models are increasingly used in the healthcare field, but the hallucination problem remains a key obstacle to their practical deployment. When models generate medical information that seems reasonable but is inconsistent with facts, it may pose serious safety risks. This project focuses on medical Q&A scenarios and builds a systematic evaluation framework to quantitatively compare the hallucination performance of models under different configurations.

## Technical Architecture Overview

The project uses a streamlined and efficient tech stack:

- **Orchestration Layer**: LangGraph handles workflow orchestration
- **Vector Storage**: FAISS as the knowledge base retrieval backend
- **Embedding Model**: nomic-embed-text provided by Ollama
- **Generation Model**: llama3:latest deployed locally via Ollama

This architectural choice reflects the principle of pragmatism—achieving a complete RAG (Retrieval-Augmented Generation) pipeline without relying on external APIs.

## Dual-Mode Evaluation Design

The core of the project lies in comparing two working modes:

## Non-RAG Mode (no_rag)

The model answers questions directly based on parametric knowledge, testing its inherent medical knowledge reserve and hallucination tendency. This mode reflects the baseline performance of general large models without optimization.

## RAG-Enhanced Mode (rag)

After retrieving relevant medical knowledge fragments via FAISS, the model generates answers. This mode evaluates whether retrieval augmentation can effectively suppress hallucinations and whether the introduced retrieval noise will bring new types of errors.

## Evaluation Dimensions and Metric System

The project establishes multi-dimensional evaluation metrics:

1. **Accuracy**: Consistency between the answer and the standard answer
2. **Error Rate**: Proportion of obvious factual errors
3. **Hallucination Categories**: Fine-grained classification of hallucination types

In addition, the system is equipped with a verifier_agent to perform secondary verification on the generated results, forming a closed-loop evaluation mechanism of "generation-verification".

## Knowledge Base and Data Management

The project uses JSON format to maintain the medical knowledge base (data/knowledge_base.json) and supports rebuilding the FAISS index via command-line parameters. This design makes the update and maintenance of the knowledge base relatively flexible, facilitating customized expansion for specific medical fields (such as internal medicine, pharmacy).