Anto01 8ea53f4003 feat: fold project-scoped memories into context pack
The retrieval-quality review on 2026-04-11 found that active
project/knowledge/episodic memories never reached the pack: only
Trusted Project State and identity/preference memories were being
assembled. Reinforcement bumped confidence on memories that had
no retrieval outlet, so the reflection loop was half-open.

This change adds a third memory tier between identity/preference
and retrieved chunks:

- PROJECT_MEMORY_BUDGET_RATIO = 0.15
- Memory types: project, knowledge, episodic
- Only populated when a canonical project is in scope — without
  a project hint, project memories stay out (cross-project bleed
  would rot the signal)
- Rendered under a dedicated "--- Project Memories ---" header
  so the LLM can distinguish it from the identity/preference band
- Trim order in _trim_context_to_budget: retrieval → project
  memories → identity/preference → project state (most recently
  added tier drops first when budget is tight)

get_memories_for_context gains header/footer kwargs so the two
memory blocks can be distinguished in a single pack without a
second helper.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-11 11:35:40 -04:00

AtoCore

Personal context engine that enriches LLM interactions with durable memory, structured context, and project knowledge.

Quick Start

pip install -e .
uvicorn src.atocore.main:app --port 8100

Usage

# Ingest markdown files
curl -X POST http://localhost:8100/ingest \
  -H "Content-Type: application/json" \
  -d '{"path": "/path/to/notes"}'

# Build enriched context for a prompt
curl -X POST http://localhost:8100/context/build \
  -H "Content-Type: application/json" \
  -d '{"prompt": "What is the project status?", "project": "myproject"}'

# CLI ingestion
python scripts/ingest_folder.py --path /path/to/notes

# Live operator client
python scripts/atocore_client.py health
python scripts/atocore_client.py audit-query "gigabit" 5

API Endpoints

Method Path Description
POST /ingest Ingest markdown file or folder
POST /query Retrieve relevant chunks
POST /context/build Build full context pack
GET /health Health check
GET /debug/context Inspect last context pack

Architecture

FastAPI (port 8100)
  |- Ingestion: markdown -> parse -> chunk -> embed -> store
  |- Retrieval: query -> embed -> vector search -> rank
  |- Context Builder: retrieve -> boost -> budget -> format
  |- SQLite (documents, chunks, memories, projects, interactions)
  '- ChromaDB (vector embeddings)

Configuration

Set via environment variables (prefix ATOCORE_):

Variable Default Description
ATOCORE_DEBUG false Enable debug logging
ATOCORE_PORT 8100 Server port
ATOCORE_CHUNK_MAX_SIZE 800 Max chunk size (chars)
ATOCORE_CONTEXT_BUDGET 3000 Context pack budget (chars)
ATOCORE_EMBEDDING_MODEL paraphrase-multilingual-MiniLM-L12-v2 Embedding model

Testing

pip install -e ".[dev]"
pytest

Operations

  • scripts/atocore_client.py provides a live API client for project refresh, project-state inspection, and retrieval-quality audits.
  • docs/operations.md captures the current operational priority order: retrieval quality, Wave 2 trusted-operational ingestion, AtoDrive scoping, and restore validation.

Architecture Notes

Implementation-facing architecture notes live under docs/architecture/.

Current additions:

  • docs/architecture/engineering-knowledge-hybrid-architecture.md — 5-layer hybrid model
  • docs/architecture/engineering-ontology-v1.md — V1 object and relationship inventory
  • docs/architecture/engineering-query-catalog.md — 20 v1-required queries
  • docs/architecture/memory-vs-entities.md — canonical home split
  • docs/architecture/promotion-rules.md — Layer 0 to Layer 2 pipeline
  • docs/architecture/conflict-model.md — contradictory facts detection and resolution
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