coreason-archive 0.5.1

persistence layer for Cognitive State across the CoReason ecosystem

coreason-archive

Persistence layer for "Cognitive State" across the CoReason ecosystem.

Executive Summary

coreason-archive is the persistence layer for "Cognitive State" across the CoReason ecosystem. It addresses the critical failure mode of modern AI: "Digital Amnesia."

Standard RAG (Retrieval Augmented Generation) only looks at static documents (coreason-mcp). coreason-archive looks at Dynamic Experience. It stores the reasoning traces, decisions, and user preferences generated during runtime.

Version 3.0 upgrades the architecture from a simple Vector Cache to a Hybrid Neuro-Symbolic Memory System. It combines Vector Search (for semantic similarity) with a Knowledge Graph (for structural relationships) and a Temporal Engine (for time-decay). This ensures that an agent doesn't just recall "similar text" but understands "who, when, and why" a decision was made, respecting strict enterprise boundaries.

Functional Philosophy

The agent must implement the Scope-Link-Rank-Retrieve Loop:

1. Hybrid Memory Structure (Neuro-Symbolic):
   • Semantic (Vector): "Find thoughts similar to 'Dosing Protocol'."
   • Structural (Graph): "Find all thoughts linked to 'Project Apollo' and 'Dr. Smith'."
   • SOTA Best Practice: Using vectors for fuzzy matching and graphs for explicit entity tracking prevents "Context Collapse" in complex workflows.
2. Federated Scoping (The Hierarchy of Truth):
   • Memory is not a flat bucket. It is a hierarchy: User > Project > Department > Global.
   • A "User Preference" (e.g., "Don't use tables") overrides a "Global Default."
3. Active Epistemic Decay:
   • Knowledge has a half-life. A cached thought about "Q3 Strategy" is worthless in Q4.
   • We implement Time-Aware Retrieval where older memories have lower retrieval scores unless explicitly pinned.
4. Memory Portability (The Digital Twin):
   • When a user moves departments, their personal cognitive state follows them, but their former team's secrets are left behind.
5. Asynchronous & Framework-Agnostic:
   • The system utilizes a TaskRunner abstraction (defaulting to asyncio/anyio) to handle background ingestion tasks, ensuring it can be integrated into any Python stack (FastAPI, Django, CLI) without vendor lock-in.

Getting Started

Prerequisites

• Python 3.12+
• Poetry

Installation

1. Clone the repository:

   git clone https://github.com/example/example.git
   cd my_python_project
   

2. Install dependencies:

   poetry install
   

Usage

• Run the linter:

  poetry run pre-commit run --all-files
  

• Run the tests:

  poetry run pytest
  

For detailed documentation, please refer to the docs/ folder or the MkDocs site.