Anto01 5aeeb1cad1 feat: query-relevance ordering for memory selection
get_memories_for_context now accepts an optional query string.
When provided, candidate memories are reranked by lexical overlap
with the query (stemmed token intersection, ties broken by
confidence) before the budget walk. Without a query the order is
unchanged — effectively "by confidence desc" as before — so
non-builder callers see no behaviour change.

The fetch limit is raised from 10 to 30 so there's a real pool to
rerank. Token overlap reuses _normalize/_tokenize from
reinforcement.py so ranking and reinforcement matching share the
same notion of distinctive terms.

build_context passes the user_prompt through to both the identity/
preference and project-memory calls. The retrieval harness
regression the fix is targeting:

- p05-vendor-signal FAIL @ 1161645: "Zygo" missing from the pack
  even though an active vendor memory contained it. Root cause:
  higher-confidence p05 memories filled the 25% budget slice
  before the vendor memory ever got a chance. Query-aware ordering
  puts the vendor memory first when the query is about vendors.

New regression test test_project_memories_query_relevance_ordering
locks the behaviour in with two p05 memories and a tight budget.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-11 12:47:05 -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
Description
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