Turn a static resume into an AI-powered conversation.
AI-Resume is a containerized web application that acts as your digital professional proxy. It provides a "30-second scan" layout for speed, backed by a semantic AI agent that recruiters can "interview" in real-time. Using natural language, they can query specific experience (e.g., "How has she handled MLOps at scale?") and receive a synthesized, evidence-based summary derived from your personal semantic knowledge base.
ai-resume-walkthrough-2026-02-04.mp4
A recruiter asks about AI experience, tests job fit with a real job description, and explores leadership style — all without scheduling a call.
Try it with a fictional candidate: jane-doe-ai-resume.schwichtenberg.us
Suggested things to try:
- Ask about specific skills or experience
- Paste a real job description into the Fit Assessment tab
- Ask a question the resume can't answer (watch the honest response)
| You are a... | What you get |
|---|---|
| Resume Owner | Deploy your resume as an always-available AI agent. Visitors get thoughtful answers about your experience without you being in the room. |
| Recruiter / Hiring Manager | Instant, detailed answers about a candidate. Ask about specific skills, experience depth, or job fit. No more scanning PDFs or scheduling screening calls. |
| Engineer / Architect | A reference implementation of a production RAG system: Rust gRPC service, Python FastAPI, React frontend, semantic search with cross-encoder re-ranking, container deployment. |
AI Chat — Ask anything about the candidate's background. The agent retrieves relevant resume context via hybrid search (BM25 + vector embeddings + cross-encoder re-ranking) and generates grounded, citation-backed answers. It won't hallucinate or make things up.
Fit Assessment — Paste a real job description and get an honest analysis: key matches, gaps, and a recommendation. Pre-analyzed examples show strong and weak fit scenarios so you know what calibrated output looks like.
Experience Cards — Structured view of roles, projects, and skills loaded dynamically from a single portable data file.
master_resume.md Python ingest .mv2 file Rust gRPC Python API React SPA
(your resume) ───► (chunk + embed) ───► (vector DB) ───► (search) ───► (LLM + SSE) ───► (chat UI)
<5ms streaming responsive
All content comes from a single Markdown file with YAML frontmatter. No hardcoded data in the frontend — everything flows through the API from the .mv2 vector database.
Three containers behind a reverse proxy:
| Service | Stack | Role |
|---|---|---|
| memvid-service | Rust, Tonic gRPC, memvid-core | Semantic search, state lookup, Ask mode with re-ranking |
| api-service | Python, FastAPI, OpenRouter | LLM orchestration, fit assessment, SSE streaming |
| frontend | React, TypeScript, Tailwind, shadcn/ui | Chat UI, experience cards, fit assessment |
Key technical decisions:
- Hybrid search: BM25 lexical + vector semantic + cross-encoder re-ranking (Reciprocal Rank Fusion)
- Honest by design: System prompts enforce factual grounding; guardrails block prompt injection
- Single-file portability: One
.mv2file contains all embeddings, metadata, and profile data - Read-only containers: All services run rootless with read-only filesystems
# 1. Create your resume
cp data/example_resume.md data/master_resume.md
# Edit with your information (see docs/MASTER_DOCUMENT_SCHEMA.md for format)
# 2. Ingest into vector database
cd ingest && uv run python ingest.py --verify
# 3. Configure secrets
cp deployment/.env.example deployment/.env
# Add your OPENROUTER_API_KEY
# 4. Deploy
cd deployment && podman-compose up -dSee docs/SETUP.md for complete instructions including multi-arch container builds.
| Document | Description |
|---|---|
| Setup | Installation, building, deployment |
| Architecture | System design, data flow, network topology |
| PRD | Product requirements, success metrics |
| Development | Contributing guide, workflows |
| Master Document Schema | Resume markdown format |
| Agentic Flow | LLM orchestration and prompt design |
| Security | Security hardening, vulnerability management |
| Test Coverage | Test strategy and coverage reports |
| TODO | Roadmap and task breakdown |
This is a reference project by Frank Schwichtenberg — built to solve a real problem (making resumes interactive) while demonstrating production engineering practices across the stack:
- Systems design: Rust + Python hybrid architecture with gRPC boundaries
- Search & retrieval: Semantic search, BM25, cross-encoder re-ranking, metadata filtering
- LLM engineering: RAG pipeline, prompt design, guardrails, streaming responses
- Infrastructure: Multi-arch containers, rootless deployment, read-only filesystems
- Security: Prompt injection defense, rate limiting, input validation, container hardening
PolyForm Noncommercial License 1.0.0 — See LICENSE file.