AI Resume Screening System Based on Vector Database: How Semantic Search Revolutionizes Recruitment Processes

This article introduces the open-source AI resume screening system ResumeScreeningSystem, which uses vector databases and semantic search technology to go beyond traditional keyword matching and achieve semantic-level matching between resumes and job descriptions. It addresses the pain points of low efficiency in recruitment screening and missing excellent candidates. The system adopts a React frontend, FastAPI backend, vector database (supporting Endee), and Sentence Transformers embedding model, bringing an intelligent upgrade to the recruitment process.

In traditional recruitment, HR needs to manually screen a large number of resumes. The keyword matching method has limitations: it cannot understand synonyms (e.g., "Python development" vs. "Python programming"), contextual meanings, or skill relevance, leading to low efficiency and easy omission of excellent candidates.

ResumeScreeningSystem targets internship positions, with semantic matching as its core. The architecture includes:

1. Frontend: Built with React + Vite, supporting batch upload of resumes in formats like PDF/DOCX, and real-time display of matching results when inputting a JD (Job Description);
2. Backend: FastAPI framework, including modules for document parsing, embedding generation (Sentence Transformers), vector storage and retrieval, and comprehensive sorting;
3. Vector database: Supports Endee (deployed via Docker) and an in-memory fallback solution, converting text into high-dimensional vectors to store semantically similar documents in close proximity.

System workflow:

1. Resume upload and parsing: Extract text from resumes in various formats;
2. Vectorization processing: Generate semantic vectors using Sentence Transformers;
3. Vector storage: Store in the database and build indexes;
4. JD vectorization: Convert the job description into a query vector;
5. Semantic retrieval: Perform similarity search in the vector database;
6. Comprehensive scoring: Rank candidates by combining semantic similarity and keyword matching.

Technical highlights:

• Semantic understanding: Recognizes synonyms, skill relevance, and cross-language support (depending on the model);
• Engineering practices: One-click startup with Docker Compose, environment variable configuration, and flexible switching between development and production environments. This system demonstrates the practical application of AI technology in recruitment scenarios.

Currently targeting internship positions, future extensions include:

• Enterprise-level features: User authentication, recruiter dashboard, permission management;
• Data integration: Export CSV to integrate with existing HR systems;
• Intelligent JD parsing: Automatically extract structured information from PDF-formatted JDs;
• Deployment optimization: Frontend on Vercel, backend on Render/AWS EC2.

Although ResumeScreeningSystem is not large-scale, it fully demonstrates the potential of AI in the digital transformation of recruitment. The combination of vector databases and semantic search provides new ideas for solving the problem of recruitment information matching. For developers, it is a case study for learning AI application architecture; for HR, it indicates the direction of future recruitment tools. With the advancement of large models and embedding technologies, more intelligent and accurate talent matching systems are worth looking forward to.