Resume Judge

Scores resumes using semantic embeddings and retrieval-augmented generation (RAG).

Resume Score Analyzer is an end-to-end AI resume scoring workflow.

Upload PDF resumes → select skills → receive strict 0–100 skill scores.

Pipeline:

Store: Split resumes into chunks and embed into ChromaDB.

Retrieve: Fetch relevant excerpts using similarity search.

Judge: LLM outputs strict numeric scores.

Goal:

Avoid keyword matching.

Evaluate contextual experience differences such as:

“used React once” vs “led multiple React projects”.

Technologies:

  • LangChain
  • ChromaDB
  • OpenAI embeddings (text-embedding-3-small)
  • GPT-4o-mini scoring model

Workflow:

Upload → Chunk → Embed → Retrieve → Score → Display matrix results.

Docker ChromaDB runs on localhost:8000.

Server:

Express + Multer + LangChain.

Client:

Next.js interface displaying skill cards with color-coded scores.

Output example:

{ "resumeId": "1761205988038", "scores": { "React": 72, "AWS": 35, "SQL": 80 } }