Internship Diary Entry Details Internship: AI Engineer - SynerSense
Date: 14 Mar 2026
Deployment Goal
Deploy the Relabeling Module and Local RAG Vault infrastructure onto the client laptop, transitioning the system from development staging into a stable, privacy-first local setup.
What I Worked On
Infrastructure Setup (Local + Cloud Bridge)
- Configured a PostgreSQL + pgvector container via Docker.
- Configured persistent volumes so the database state survives container restarts and OS reboots.
- Set up a secure connection between the cloud-hosted SvelteKit frontend and the local FastAPI backend via Cloudflare Tunnel, avoiding direct exposure of local ports.
Workspace Persistence (Reference State)
- Verified the
.state/directory and initializedreferences.json. - Ensured selected reference thumbnails are stored locally and restored automatically on reload.
Local Embedding Workflow (Performance & Stability)
- Loaded the Sentence Transformers embedding model on the client CPU.
- Measured and optimized response latency to achieve acceptable local similarity-query performance.
Smoke Testing (UX + Functionality)
- Validated Reference Selection Mode and Relabel Mode end-to-end.
- Confirmed reference thumbnails render correctly inside the scatter plot.
- Verified hover previews and UI responsiveness under annotation workloads.
Hours Worked
8
References (Links)
- Docker - https://docs.docker.com
- pgvector - https://github.com/pgvector/pgvector
- Cloudflare Tunnel - https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/
- FastAPI - https://fastapi.tiangolo.com
- SvelteKit - https://kit.svelte.dev/docs
- Sentence Transformers - https://www.sbert.net
Learnings & Risks
Key Learnings
- On-site deployment of AI systems on client hardware while preserving compatibility with existing infrastructure.
- Working with Docker volume mappings between Windows hosts and Linux containers.
- Designing privacy-first AI architectures where vector databases and embeddings run locally.
- Communicating vector search and embedding concepts to non-technical stakeholders.
Blockers / Risks
- The client’s laptop has lower memory capacity than the development machine, so monitoring embedding/model usage is required to keep the UI responsive.
- The current JSON-based workspace state works, but users need a reset mechanism to quickly clear their reference workspace.
Skills Used
Python, FastAPI, SvelteKit, Docker, PostgreSQL, pgvector, AI Deployment, System Configuration, Local AI Infrastructure, Debugging, Client Support, Documentation.