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Sacred Brain – Hippocampus Session Log

This file captures the work performed during the Codex CLI session so it can be shared with other assistants (e.g., ChatGPT) for continuity.

Initial Spec

  • Build a Raspberry Pi–friendly FastAPI microservice named “hippocampus.”
  • Provide REST endpoints to store/query/summarize long-term memories backed by Mem0 (with an in-memory fallback).
  • Ship code, docs, tests, and simple ops scaffolding (systemd service + dev script).
  • Structure the repo with brain/hippocampus/ modules plus tests/docs/config.

Deliverables Implemented

  1. Core service scaffolding

    • FastAPI app with /health, /memories (POST/GET), /summaries, and later /memories/{memory_id} DELETE.
    • Config loader (brain/hippocampus/config.py) reading TOML + env overrides.
    • Logging setup (brain/hippocampus/logging_config.py).
    • Mem0 adapter with auto fallback to an in-memory backend, including summary helper.
    • Pydantic models for all request/response contracts.

  2. Project structure & tooling

    • .gitignore, pyproject.toml, requirements.txt, and README quickstart.
    • Example config (config/hippocampus.toml).
    • Ops helpers: ops/scripts/dev_run.sh and sample systemd unit.

  3. Tests

    • Adapter tests for add/query/summarize/delete.
    • API tests covering create/query/summarize/delete endpoints via FastAPI TestClient.
    • pytest runs clean inside .venv (6 tests passing).

  4. Docs

    • docs/ARCHITECTURE.md (component overview).
    • docs/API.md describing each HTTP endpoint and payload.
    • README updated with delete capability and Mem0 install guidance.

  5. Persistence fallback

    • Added a SQLite-backed storage backend (with configurable path) that is now the default when Mem0 cloud access is unavailable.
    • Extended adapter tests to cover persistence, retrieval, and deletion flows to ensure offline durability.

  6. Self-hosted Mem0 integration

    • Added configuration flags for mem0.enabled and mem0.backend_url, plus a Mem0RemoteClient wrapper for a LAN-accessible Mem0 deployment.
    • Mem0Adapter now routes through the remote backend when enabled and automatically falls back to the in-memory store if the SDK/import fails or runtime calls error out.
    • Expanded tests and docs to describe the self-hosted expectation, fallback strategy, and optional SQLite mode.

  7. Code Quality & Refactoring

    • Introduced ruff and mypy for linting and type checking (configured in pyproject.toml).
    • Refactored matrix_respond logic into a dedicated BotRouter class (brain/hippocampus/bot_router.py), simplifying app.py.
    • Enhanced Mem0Adapter error handling to distinguish between configuration errors (missing SDK) and connection issues.
    • Added comprehensive tests for BotRouter covering Sam, Agno, and fallback summarization strategies.

Extra Feature Added During Session

  • Implemented memory deletion flow (DELETE /memories/{memory_id}) and updated docs/tests to keep the API complete.

Outstanding Ideas / Next Steps

  1. Integrate with the official Mem0 SDK when available and adapt delete/query semantics to match the live API.
  2. Consider authentication/authorization for multi-tenant deployments.
  3. Add persistence beyond the in-memory fallback (e.g., file or database) if Mem0 cloud is unavailable.
  4. Enhance summarisation by delegating to an LLM-backed service once defined.

How to Reproduce / Run

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
export HIPPOCAMPUS_CONFIG=config/hippocampus.toml
uvicorn brain.hippocampus.app:app --reload

Run tests:

source .venv/bin/activate
pytest

This log can be shared verbatim with other assistants to give them full context on what has been built so far and what potential follow-up tasks exist.