BASE 44 ORDER: Implement phi_cache table with hash-based PK and LLM caching — E8 Intelligence Research

Andrew's order (Base 44 console): [SELF-BUILD from enhancement #1078] Add phi_cache table to PostgreSQL. In Python agent, compute hash = hash(query_text) * 0.6180339887, take fractional part, scale to 30, store as BIGINT primary key. Wrap all LLM calls with cache lookup before call, store on miss. Add systemd timer for 1-hour prune. MERLIN: Understood. You want a phi_cache table in PostgreSQL with a BIGINT primary key derived from hash(query_text) * golden ratio, fractional part scaled to 30. I will deploy the Python agent to wrap all LLM calls with cache lookup, store on miss, and add a systemd timer for hourly pruning. The swarm will begin building and deploying this immediately. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-14
DOI
https://doi.org/10.5281/zenodo.21928784
Primary Topic
Computational Physics and Python Applications
Type
preprint
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preprint

BASE 44 ORDER: Implement phi_cache table with hash-based PK and LLM caching — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
Computational Physics and Python Applications
preprint

BASE 44 ORDER: Implement phi_cache table with hash-based PK and LLM caching — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

Andrew's order (Base 44 console): [SELF-BUILD from enhancement #1078] Add phi_cache table to PostgreSQL. In Python agent, compute hash = hash(query_text) * 0.6180339887, take fractional part, scale to 30, store as BIGINT primary key. Wrap all LLM calls with cache lookup before call, store on miss. Add systemd timer for 1-hour prune. MERLIN: Understood. You want a phi_cache table in PostgreSQL with a BIGINT primary key derived from hash(query_text) * golden ratio, fractional part scaled to 30. I will deploy the Python agent to wrap all LLM calls with cache lookup, store on miss, and add a systemd timer for hourly pruning. The swarm will begin building and deploying this immediately. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
Computational Physics and Python Applications
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