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>
77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
"""CLI script to run test prompts and compare baseline vs enriched."""
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import argparse
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import sys
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from pathlib import Path
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import yaml
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# Add src to path
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sys.path.insert(0, str(Path(__file__).parent.parent / "src"))
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from atocore.context.builder import build_context
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from atocore.models.database import init_db
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from atocore.observability.logger import setup_logging
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def main():
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parser = argparse.ArgumentParser(description="Run test prompts against AtoCore")
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parser.add_argument(
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"--prompts",
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default=str(Path(__file__).parent.parent / "tests" / "test_prompts" / "prompts.yaml"),
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help="Path to prompts YAML file",
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)
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args = parser.parse_args()
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setup_logging()
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init_db()
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prompts_path = Path(args.prompts)
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if not prompts_path.exists():
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print(f"Error: {prompts_path} not found")
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sys.exit(1)
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with open(prompts_path) as f:
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data = yaml.safe_load(f)
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prompts = data.get("prompts", [])
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print(f"Running {len(prompts)} test prompts...\n")
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for p in prompts:
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prompt_id = p["id"]
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prompt_text = p["prompt"]
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project = p.get("project")
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expected = p.get("expected", "")
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print(f"{'='*60}")
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print(f"[{prompt_id}] {prompt_text}")
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print(f"Project: {project or 'none'}")
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print(f"Expected: {expected}")
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print(f"-" * 60)
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pack = build_context(
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user_prompt=prompt_text,
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project_hint=project,
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)
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print(f"Chunks retrieved: {len(pack.chunks_used)}")
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print(f"Total chars: {pack.total_chars} / {pack.budget}")
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print(f"Duration: {pack.duration_ms}ms")
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print()
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for i, chunk in enumerate(pack.chunks_used[:5]):
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print(f" [{i+1}] Score: {chunk.score:.2f} | {chunk.source_file}")
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print(f" Section: {chunk.heading_path}")
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print(f" Preview: {chunk.content[:120]}...")
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print()
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print(f"Full prompt length: {len(pack.full_prompt)} chars")
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print()
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print(f"{'='*60}")
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print("Done. Review output above to assess retrieval quality.")
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if __name__ == "__main__":
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main()
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