[API] Build Gemini RAG tool for sovereign memory retrieval #756

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Timmy · 2026-03-30T01:56:58Z

Timmy commented

2026-03-30 01:56:58 +00:00

Phase 5 — Advanced Integration

Objective

Use Gemini's File Search tool to implement a RAG (Retrieval-Augmented Generation) system for Timmy's long-term memory — without building a custom vector store.

Design

Upload Timmy's memory corpus (conversation logs, decision records, learning artifacts) to Gemini File Search
Before each reasoning call, query relevant memories
Inject retrieved context into the prompt
Periodically consolidate and prune the memory corpus

Acceptance

Define the memory corpus format and ingestion pipeline
Upload initial corpus (SOUL.md, architecture docs, key conversations)
Implement memory query → context injection in the reasoning pipeline
Add memory write-back: important decisions and learnings get stored
Benchmark: does RAG improve response quality for project-specific questions?
Implement context caching for frequently-accessed memories

Alternative

If File Search is insufficient, fall back to Gemini embeddings + local vector store.

Notes

Use Gemini's context caching to reduce cost on the core SOUL.md/system context.

## Phase 5 — Advanced Integration ### Objective Use Gemini's File Search tool to implement a RAG (Retrieval-Augmented Generation) system for Timmy's long-term memory — without building a custom vector store. ### Design - Upload Timmy's memory corpus (conversation logs, decision records, learning artifacts) to Gemini File Search - Before each reasoning call, query relevant memories - Inject retrieved context into the prompt - Periodically consolidate and prune the memory corpus ### Acceptance - [ ] Define the memory corpus format and ingestion pipeline - [ ] Upload initial corpus (SOUL.md, architecture docs, key conversations) - [ ] Implement memory query → context injection in the reasoning pipeline - [ ] Add memory write-back: important decisions and learnings get stored - [ ] Benchmark: does RAG improve response quality for project-specific questions? - [ ] Implement context caching for frequently-accessed memories ### Alternative If File Search is insufficient, fall back to Gemini embeddings + local vector store. ### Notes Use Gemini's context caching to reduce cost on the core SOUL.md/system context.

Timmy added this to the M5: Google AI Ultra Integration milestone 2026-03-30 01:56:58 +00:00

Timmy added the google-ai-ultra sovereignty p2-backlog gemini-api labels 2026-03-30 01:56:58 +00:00

Timmy referenced this issue

2026-03-30 01:58:51 +00:00

[EPIC] Google AI Ultra Full Integration — Master PR #763

Timmy changed title from ~~[API] Build Gemini-powered RAG system using File Search for Timmy's memory~~ to [API] Build Gemini RAG tool for sovereign memory retrieval

2026-03-30 02:56:22 +00:00

Timmy commented

2026-04-03 22:59:56 +00:00

Audit: Google AI Ultra integration epic — these are aspirational proposals, not scoped work. Closing. Reopen individually with acceptance criteria if needed.

Timmy closed this issue

2026-04-03 22:59:56 +00:00

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Reference: Timmy_Foundation/the-nexus#756