Anto01
b4afbbb53a
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>
42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
"""Tests for the retrieval system."""
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from atocore.ingestion.pipeline import ingest_file
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from atocore.models.database import init_db
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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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