feat: implement AtoCore Phase 0 + Phase 0.5 (foundation + PoC)
Complete implementation of the personal context engine foundation:
- FastAPI server with 5 endpoints (ingest, query, context/build, health, debug)
- SQLite database with 5 tables (documents, chunks, memories, projects, interactions)
- Heading-aware markdown chunker (800 char max, recursive splitting)
- Multilingual embeddings via sentence-transformers (EN/FR)
- ChromaDB vector store with cosine similarity retrieval
- Context builder with project boosting, dedup, and budget enforcement
- CLI scripts for batch ingestion and test prompt evaluation
- 19 unit tests passing, 79% coverage
- Validated on 482 real project files (8383 chunks, 0 errors)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-05 09:21:27 -04:00
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"""Tests for the retrieval system."""
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from atocore.ingestion.pipeline import ingest_file
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2026-04-05 17:53:23 -04:00
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from atocore.models.database import get_connection, init_db
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feat: implement AtoCore Phase 0 + Phase 0.5 (foundation + PoC)
Complete implementation of the personal context engine foundation:
- FastAPI server with 5 endpoints (ingest, query, context/build, health, debug)
- SQLite database with 5 tables (documents, chunks, memories, projects, interactions)
- Heading-aware markdown chunker (800 char max, recursive splitting)
- Multilingual embeddings via sentence-transformers (EN/FR)
- ChromaDB vector store with cosine similarity retrieval
- Context builder with project boosting, dedup, and budget enforcement
- CLI scripts for batch ingestion and test prompt evaluation
- 19 unit tests passing, 79% coverage
- Validated on 482 real project files (8383 chunks, 0 errors)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-05 09:21:27 -04:00
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from atocore.retrieval.retriever import retrieve
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from atocore.retrieval.vector_store import get_vector_store
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def test_retrieve_returns_results(tmp_data_dir, sample_markdown):
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"""Test that retrieval returns relevant chunks."""
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init_db()
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ingest_file(sample_markdown)
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results = retrieve("What are the memory types?", top_k=5)
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assert len(results) > 0
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assert all(r.score > 0 for r in results)
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assert all(r.content for r in results)
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def test_retrieve_scores_ranked(tmp_data_dir, sample_markdown):
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"""Test that results are ranked by score."""
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init_db()
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ingest_file(sample_markdown)
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results = retrieve("architecture layers", top_k=5)
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if len(results) >= 2:
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scores = [r.score for r in results]
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assert scores == sorted(scores, reverse=True)
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def test_vector_store_count(tmp_data_dir, sample_markdown):
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"""Test that vector store tracks chunk count."""
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init_db()
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# Reset singleton for clean test
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import atocore.retrieval.vector_store as vs
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vs._store = None
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ingest_file(sample_markdown)
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store = get_vector_store()
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assert store.count > 0
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2026-04-05 17:53:23 -04:00
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def test_retrieve_skips_stale_vector_entries(tmp_data_dir, sample_markdown, monkeypatch):
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"""Retriever should ignore vector hits whose chunk rows no longer exist."""
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init_db()
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ingest_file(sample_markdown)
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with get_connection() as conn:
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chunk_ids = [row["id"] for row in conn.execute("SELECT id FROM source_chunks").fetchall()]
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class FakeStore:
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def query(self, query_embedding, top_k=10, where=None):
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return {
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"ids": [[chunk_ids[0], "missing-chunk"]],
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"documents": [["valid doc", "stale doc"]],
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"metadatas": [[
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{"heading_path": "Overview", "source_file": "valid.md", "tags": "[]", "title": "Valid", "document_id": "doc-1"},
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{"heading_path": "Ghost", "source_file": "ghost.md", "tags": "[]", "title": "Ghost", "document_id": "doc-2"},
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]],
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"distances": [[0.1, 0.2]],
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}
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monkeypatch.setattr("atocore.retrieval.retriever.get_vector_store", lambda: FakeStore())
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monkeypatch.setattr("atocore.retrieval.retriever.embed_query", lambda query: [0.0, 0.1])
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results = retrieve("overview", top_k=2)
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assert len(results) == 1
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assert results[0].chunk_id == chunk_ids[0]
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2026-04-06 13:32:33 -04:00
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def test_retrieve_project_hint_boosts_matching_chunks(monkeypatch):
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class FakeStore:
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def query(self, query_embedding, top_k=10, where=None):
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return {
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"ids": [["chunk-a", "chunk-b"]],
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"documents": [["project doc", "other doc"]],
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"metadatas": [[
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{
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"heading_path": "Overview",
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"source_file": "p04-gigabit/pkm/_index.md",
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"tags": '["p04-gigabit"]',
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"title": "P04",
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"document_id": "doc-a",
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},
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{
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"heading_path": "Overview",
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"source_file": "p05-interferometer/pkm/_index.md",
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"tags": '["p05-interferometer"]',
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"title": "P05",
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"document_id": "doc-b",
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},
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]],
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"distances": [[0.3, 0.25]],
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}
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monkeypatch.setattr("atocore.retrieval.retriever.get_vector_store", lambda: FakeStore())
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monkeypatch.setattr("atocore.retrieval.retriever.embed_query", lambda query: [0.0, 0.1])
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monkeypatch.setattr(
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"atocore.retrieval.retriever._existing_chunk_ids",
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lambda chunk_ids: set(chunk_ids),
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)
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monkeypatch.setattr(
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"atocore.retrieval.retriever.get_registered_project",
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lambda project_name: type(
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"Project",
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(),
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{
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"project_id": "p04-gigabit",
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"aliases": ("p04", "gigabit"),
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"ingest_roots": (),
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},
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)(),
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)
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results = retrieve("mirror architecture", top_k=2, project_hint="p04")
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assert len(results) == 2
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assert results[0].chunk_id == "chunk-a"
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assert results[0].score > results[1].score
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