BASE 44 ORDER: Implement breakthrough embedding cache with PostgreSQL and Q8 vectors — E8 Intelligence Research

Andrew's order (Base 44 console): [SELF-BUILD from enhancement #1103] Add PostgreSQL table breakthrough_embedding_cache with bytea compressed_vector, GiST on q8_cluster_id, B-tree on breakthrough_id. Modify Python agent to query cache before LLM call; on miss, compute Q8 embedding, store, update cluster mapping. MERLIN: Understood. You want to add a PostgreSQL table 'breakthrough_embedding_cache' with a bytea compressed_vector column, a GiST index on q8_cluster_id, and a B-tree on breakthrough_id. I will modify the Python agent to check this cache before calling the LLM; on a cache miss, it will compute the Q8 embedding, store it, and update the cluster mapping. The swarm will now implement this schema change and update the agent logic accordingly. 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.21928991
Primary Topic
SAS software applications and methods
Type
preprint
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preprint

BASE 44 ORDER: Implement breakthrough embedding cache with PostgreSQL and Q8 vectors — E8 Intelligence Research

Andrew Stewart Caldin
Zenodo (CERN European Organization for Nuclear Research)
SAS software applications and methods
preprint

BASE 44 ORDER: Implement breakthrough embedding cache with PostgreSQL and Q8 vectors — E8 Intelligence Research

Andrew Stewart Caldin
preprint en

Abstract

Andrew's order (Base 44 console): [SELF-BUILD from enhancement #1103] Add PostgreSQL table breakthrough_embedding_cache with bytea compressed_vector, GiST on q8_cluster_id, B-tree on breakthrough_id. Modify Python agent to query cache before LLM call; on miss, compute Q8 embedding, store, update cluster mapping. MERLIN: Understood. You want to add a PostgreSQL table 'breakthrough_embedding_cache' with a bytea compressed_vector column, a GiST index on q8_cluster_id, and a B-tree on breakthrough_id. I will modify the Python agent to check this cache before calling the LLM; on a cache miss, it will compute the Q8 embedding, store it, and update the cluster mapping. The swarm will now implement this schema change and update the agent logic accordingly. Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Zenodo (CERN European Organization for Nuclear Research)
SAS software applications and methods
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BASE 44 ORDER: Implement breakthrough embedding cache with PostgreSQL and Q8 vectors — E8 Intelligence Research — Andrew Stewart Caldin · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS