>~/contacts $ ssh sid@portfolio

Contact Page

Welcome to the communication terminal. Execute commands to chat with the AI agent, send emails, submit feedback, and inspect analytics such as top queries and user interaction logs.

sid@portfolio:~ $ ./contact --init

Agent Traffic

2450TOTAL REQUESTS

99.5% UPTIME

Daily Token Load

45.0kOF 1M BUDGET

4.5% LOAD

Knowledge Base

VECTOR_DB: SYNCED

128INDEXED DOCUMENTS

Recent System Inquiries

01"How do I reset local cache?"

02"Are API keys per workspace?"

03"Exporting logs to CSV format"

Transmit_Protocol_v2.4.0

LOC: 127.0.0.1

LOG: INIT...

D_SOCK: OK

MEM: 44.2%

CRYPT: RSA

Highlighted_Log // Question of the Week

[ INCOMING_QUERY_DETECTED ]

"Hey Siddhant! Your AI agent is really fast. I'm building something similar for my college project. How do you manage the context window so efficiently without blowing up the token budget?"

— Anonymous User • Date: Apr 18

[ SYSTEM_ADMIN_RESPONSE ]

>Great question! The secret sauce is the RAG (Retrieval-Augmented Generation) architecture. Instead of sending the entire database to the model, I chunk my data and store it in Supabase using pgvector. When you ask a question, the system only pulls the top 3 most relevant chunks and passes those to the prompt. This keeps the token count super low while maintaining high accuracy. Glad you liked it!

Protocol Module 02

System Feedback

Report anomalies or commend system performance.