Career Digital Twin
An AI-powered career assistant that turns a static CV into a live, queryable professional presence — answering questions about publications, research, skills, and experience with grounded, source-referenced responses.
RAG
Grounded Retrieval Architecture
∞
Multi-Turn Conversations
Live
Deployed on Hugging Face Spaces
The Problem
A static CV can't answer questions — and professionals answer the same ones repeatedly
Recruiters, collaborators, and conference organizers regularly ask the same career questions — what research has been published, what projects were involved in, what technical skills are held, what experience is most relevant to a specific role. A PDF resume can't respond, can't clarify, and can't engage. The professional either answers each query manually — repeatedly — or leaves questions unanswered. Neither scales. What's needed is a persistent, accurate, always-available representative that knows the career data and can answer on behalf of the professional.
The Solution
A RAG-powered agent that knows the career data and answers for you
DigitalTwin App ingests a structured resume, publications list, and project summaries — chunks and embeds them using OpenAI embeddings — and stores them in a ChromaDB vector store. When a user asks a question, the system retrieves the most semantically relevant career facts and passes them to an OpenAI SDK conversational agent that responds accurately, in context, and with grounded source references. The agent maintains multi-turn conversation history and is deployed as an interactive Gradio application on Hugging Face Spaces — publicly accessible and always available.
Key Outcome
A live, deployed career agent that turns a static CV into a queryable professional presence — answering questions about publications, research, skills, and experience with factual, grounded responses, deployed on Hugging Face Spaces and accessible to anyone with the link.
Technical Deep Dive
Architecture & Design
RAG Pipeline
Phase 1 — Ingestion
Data Sources
Career Data
CV · Publications · Project summaries · Skills
Processing
Chunking & Embedding
pandas structuring · OpenAI embeddings · ChromaDB vector store
Phase 2 — Query & Retrieval
User Input
Question via Gradio UI
Multi-turn conversation · Career-related queries
Semantic Retrieval
ChromaDB Lookup
Embeds query · Retrieves most relevant career facts
Phase 3 — Generation
Conversational Agent · OpenAI SDK
DigitalTwin Agent
Retrieved facts + conversation history → grounded, persona-matched response
Phase 4 — Deployment
Live Deployment
Hugging Face Spaces
Gradio UI · Publicly accessible · HF Secrets for API key management
Phase 1
Ingestion
Career data — CV, publications list, and project summaries — is loaded and structured using pandas, then chunked into semantically coherent passages. Each chunk is embedded using OpenAI embeddings and stored in a ChromaDB vector store, building the factual knowledge base the agent retrieves from at query time.
Phase 2
Query & Retrieval
When a user submits a question via the Gradio UI, the query is embedded using the same OpenAI embedding model and used to perform a semantic similarity search against the ChromaDB vector store. The most relevant career fact chunks are retrieved and passed to the agent as grounding context for its response.
Phase 3
Generation
The OpenAI SDK conversational agent receives the retrieved career facts alongside the full conversation history and a system prompt that defines the agent's persona. It generates a grounded, context-aware response that accurately represents the professional's background — maintaining tone and persona consistency across multi-turn conversations.
Phase 4
Deployment
The full application is deployed on Hugging Face Spaces using the Gradio SDK. API keys are managed securely via HF Spaces Secrets — never exposed in code. The deployed app is publicly accessible via a persistent URL, enabling anyone to query the career agent directly without requiring local setup.
Key Design Decisions
RAG grounds the agent in facts — not hallucinations
Without retrieval, a conversational LLM would generate plausible-sounding but potentially inaccurate career details. By grounding every response in semantically retrieved chunks from the actual career data, the agent is constrained to facts that exist in the knowledge base — making it a trustworthy representative rather than a generative one.
System prompt persona customization shapes identity
The agent's system prompt defines not just what it knows but how it communicates — tone, level of formality, and the professional identity it represents. This makes the agent feel like a genuine extension of the professional rather than a generic chatbot responding to career queries.
ChromaDB enables fast local semantic search without infrastructure
ChromaDB runs as an embedded vector store — no external database server, no cloud vector infrastructure required. For a single-professional career knowledge base, this provides sufficient retrieval performance while keeping the deployment simple and the setup reproducible on any machine with a Python environment.
Tech Stack
| Technology | Purpose |
|---|---|
| OpenAI SDK | Conversational agent, embedding generation, and response synthesis |
| ChromaDB | Embedded vector store for semantic retrieval of career data chunks |
| Gradio | Interactive web UI with multi-turn conversation support |
| Hugging Face Spaces | Cloud deployment with persistent public URL |
| pandas | Career data loading, structuring, and preprocessing |
| python-dotenv / HF Secrets | Secure API key management for local and cloud deployment |
| Python | Core language and pipeline orchestration |
Results & Metrics
What the system delivers
RAG
Grounded Architecture
Every response grounded in retrieved career facts — no hallucinated credentials or invented history
∞
Multi-Turn Conversations
Full conversation history maintained across turns — context-aware follow-up questions supported
Live
Deployed on HF Spaces
Publicly accessible via persistent URL — no local setup required for users
Factually accurate career responses
RAG retrieval constrains the agent to facts present in the career knowledge base. Queries about publications, research experience, technical skills, and project history return grounded, source-referenced responses — not plausible but fabricated answers.
Context-aware multi-turn dialogue
The agent maintains full conversation history across turns — enabling follow-up questions, clarifications, and multi-step queries without losing context. A recruiter can ask about a publication, then ask for more detail, then ask about related skills, all in a single coherent conversation.
Publicly deployed and always available
Deployed on Hugging Face Spaces with a persistent public URL — accessible to recruiters, collaborators, and conference organizers at any time without requiring the professional to be present or respond manually.
Secure credential management
API keys are managed via python-dotenv locally and HF Spaces Secrets in production — never hardcoded or exposed in the repository. The same codebase runs securely in both local development and cloud deployment environments.