# Algorithm Reasoning Language Model: An AI-Powered Interview Preparation Platform

> An intelligent interview preparation system built with FastAPI and RAG technology, providing users with personalized algorithm learning paths and adaptive guidance through a Socratic dialogue robot.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-16T09:35:33.000Z
- 最近活动: 2026-05-16T09:48:08.798Z
- 热度: 161.8
- 关键词: 算法面试, RAG, FastAPI, 苏格拉底式教学, AI辅导, LeetCode, 向量检索, FAISS, 面试准备
- 页面链接: https://www.zingnex.cn/en/forum/thread/ai-900facfa
- Canonical: https://www.zingnex.cn/forum/thread/ai-900facfa
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: Algorithm Reasoning Language Model: An AI-Powered Interview Preparation Platform

An intelligent interview preparation system built with FastAPI and RAG technology, providing users with personalized algorithm learning paths and adaptive guidance through a Socratic dialogue robot.

## Project Background and Core Objectives

Algorithm interviews are a critical component in the recruitment process of many tech companies, but the preparation process often faces several pain points: not knowing where to start with the vast number of LeetCode problems, lack of targeted practice plans, no timely guidance when encountering difficulties, and inability to effectively track learning progress. This project aims to leverage AI technology to generate personalized learning roadmaps for each user and provide Socratic intelligent tutoring.

## System Architecture: Modular Design

The project adopts a clear layered architecture with well-defined responsibilities for each module:

## 1. Application Layer (FastAPI Web Application)

As the system's entry point, it uses the FastAPI framework to provide web services. It includes features such as user authentication (JWT + bcrypt), interview schedule management, and dashboard display. Server-side rendering is implemented using the Jinja2 template engine, and SQLite is used for data persistence.

## 2. RAG Layer (Retrieval and Recommendation)

The core intelligent recommendation module is responsible for selecting the 10 most suitable problems from the question bank to form a learning roadmap based on the user's chosen interview company and remaining days. It uses the idea of Retrieval-Augmented Generation (RAG) combined with vector similarity calculation to achieve precise matching.

## 3. Embeddings Layer (Vector Embedding and Search)

It uses HuggingFace's pre-trained models to convert problem text into vector representations, and implements efficient similarity search through FAISS. This layer provides semantic understanding capabilities for RAG recommendations and can identify potential connections between problems.

## 4. Tutor Layer (Socratic Dialogue Robot)

The AI core of the project, a Socratic-style tutoring robot. Unlike directly giving answers, it guides users to think through questions and cultivates algorithmic thinking skills. This module supports tool calls, allowing code execution or relevant knowledge queries during conversations.

## Data Flow and Workflow

The complete workflow of the system is as follows:

1. **User registers for an interview**: Selects the target company and the number of days until the interview
2. **Generate learning roadmap**: The RAG module selects 10 high-quality problems from approximately 11,000 questions based on the company's historical problem distribution and difficulty requirements
3. **Daily practice**: Users check their progress on the dashboard and click on problems to enter the details page
4. **Intelligent tutoring**: The Socratic robot provides guided dialogue in the sidebar to help users understand problem-solving ideas
5. **Progress tracking**: After completing a problem, users check the mark, and the system automatically updates the completion rate