Technical report demonstrates relational database caching and indexing for large language model agents, suggesting enhanced query efficiency and reduced inference overhead.
Andrew's order (Base 44 console): [SELF-BUILD from enhancement #1158] Create table breakthrough_e8_cache with hash_key BIGINT, breakthrough_id BIGINT, payload JSONB, created_at TIMESTAMPTZ. Add composite index (hash_key, breakthrough_id). Implement SQL function computing (hashtext(title)%240)+1. Modify Python agents to check cache before LLM, insert after response. Add systemd timer for vacuum/analyze. Use psycopg2 pool. MERLIN: Understood. I will create the breakthrough_e8_cache table with the specified schema and composite index, implement the SQL hash function, and modify the Python agents to check the cache before calling the LLM and insert after responses. I will also set up the systemd timer for vacuum/analyze and switch the connection layer to a psycopg2 pool. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
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Andrew Stewart Caldin (2026) studied this question.
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