From Local RAG Bot to a Cloud-Native Chatbot
How I moved a personal RAG chatbot to Cloud Run with PostgreSQL, session-aware retrieval, and Vertex AI.
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How I moved a personal RAG chatbot to Cloud Run with PostgreSQL, session-aware retrieval, and Vertex AI.
How I moved a local Python data collector to Google Cloud so a teammate could trigger it remotely through Telegram.
I separated transcription, translation, and subtitle rendering to make a personal video translation pipeline easier to debug.
I wrapped my multi-agent chatbot in Docker and FastAPI so I could use it through a simple browser interface.
I added safety checks, retrieval ranking, and response editing to make my personal assistant more reliable.
I built a personal assistant around my Obsidian notes to learn how retrieval, agents, and personal knowledge can work together.
A short fraud-detection project helped me revisit class imbalance, Random Forest, SMOTETomek, and precision-recall metrics.
I moved my AI newsroom from CrewAI to LangGraph to gain clearer state, workflow control, and more consistent output.
I built a small AI newsroom with CrewAI to collect, filter, and summarize the AI news I actually want to read.
I used Google Apps Script and Gemini to classify incoming Gmail messages and make a crowded inbox easier to scan.
A practical introduction to agent workflows, using a CrewAI newsroom example to explain roles, loops, and trade-offs.