LangGraph-based Intelligent Recruitment Matching System: Practical Application of RAG and Vector Databases

This project demonstrates a complete AI-driven recruitment system that uses LangGraph for workflow orchestration, RAG for enhanced retrieval, FAISS vector database, and the Claude large model to achieve semantic-level intelligent matching between resumes and job descriptions. Compared to traditional keyword matching, the system can understand the semantic relationship between "React Developer" and "JavaScript Frontend Expert", solving the problems of time-consuming screening and easy omission of high-quality candidates in traditional recruitment.

Traditional recruitment systems rely on keyword matching, which has limitations such as missing synonyms (e.g., "React.js" vs. "React"), inability to understand semantics ("Kubernetes Expert" vs. "Container Orchestration"), lack of context differentiation ("5 years of React experience" vs. "familiar with React"), and sensitivity to spelling errors. Semantic matching converts text into vectors and calculates similarity in vector space, enabling automatic handling of synonyms, understanding of concept hierarchies, robustness to spelling variations, and capture of implicit skill associations.

The system consists of four core components:

1. LangGraph Workflow Orchestration: Coordinates steps such as requirement extraction, query vectorization, retrieval execution, intelligent ranking, and result generation, supporting state management and multi-turn conversations.
2. RAG-Enhanced Retrieval: In the indexing phase, resumes are parsed and converted into vectors stored in FAISS; in the query phase, user requirements are vectorized to search for similar resumes, and the large model's generation capability is combined to avoid hallucinations.
3. FAISS Vector Database: Supports high-dimensional vector storage, fast approximate search, memory-disk balance, and persistence. Retrieval latency for tens of thousands of resumes is controlled within 100 milliseconds.
4. Claude Large Model Ranking: Ranks candidates based on factors like skill matching degree and experience, generates matching explanations and interview suggestions, and handles ambiguous information.

Take "finding a React developer with more than 5 years of experience (with TypeScript/Next.js experience)" as an example:

1. Requirement Extraction: LangGraph calls Claude to parse the requirement into a JSON containing skills, experience, and professional direction.
2. Query Vectorization: The sentence-transformers model converts the requirement into a 384-dimensional vector.
3. Semantic Retrieval: FAISS returns the top 3 similar resumes (John: similarity 0.89, Sarah: 0.72, Michael: 0.65).
4. Intelligent Ranking and Explanation: Claude ranks the candidates and generates recommendation scores (John:95%, Michael:82%, Sarah:68%) along with interview suggestions.

Core Modules: resume_parser.py (text extraction), embedding_generator.py (embedding model), vector_store.py (FAISS index), llm_ranker.py (Claude encapsulation), chat_interface.py (interactive interface), langgraph_workflow.py (workflow definition). Reasons for Selection:

• FAISS: Cost-effective for local deployment, data self-controllable, suitable for small to medium-sized resume databases.
• LangGraph: Supports state management and conditional branches, easy to extend for multi-turn conversations.
• Embedding Model: all-MiniLM-L6-v2 balances performance and effectiveness, with 384-dimensional vectors occupying small space.

Direct Application Scenarios: Internal corporate recruitment, headhunter screening, recruitment platform recommendation engines, HR tool integration. Expansion Directions: Multilingual support, dynamic learning (optimize ranking based on HR feedback), resume generation suggestions, salary prediction (based on skills and market data).

Implementation Suggestions:

• Quick Start: Install dependencies (langgraph, faiss-cpu, sentence-transformers, etc.).
• Data Preparation: Support PDF/DOCX/TXT, unified naming conventions, clean scanned PDFs.
• Performance Optimization: Use IVF index for large-scale databases, batch embedding generation, cache popular queries. Limitations: Dependent on resume quality, unable to verify experience authenticity, lack of cultural fit assessment. Usage Suggestions: Use as a preliminary screening tool, continuously calibrate ranking weights, and comply with privacy regulations.

This open-source project demonstrates the practical value of large language models and vector retrieval in the recruitment field, providing an implementable semantic matching solution, and promoting the paradigm shift in recruitment from keywords to semantics, and from rules to intelligence. The modular design facilitates customization and expansion, and the LangGraph architecture provides a foundation for function iteration, making it a good starting point for exploring HR AI applications.