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>
40 lines
817 B
Python
40 lines
817 B
Python
"""AtoCore configuration via environment variables."""
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from pathlib import Path
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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debug: bool = False
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data_dir: Path = Path("./data")
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host: str = "127.0.0.1"
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port: int = 8100
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# Embedding
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embedding_model: str = (
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"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
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)
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# Chunking
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chunk_max_size: int = 800
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chunk_overlap: int = 100
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chunk_min_size: int = 50
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# Context
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context_budget: int = 3000
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context_top_k: int = 15
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model_config = {"env_prefix": "ATOCORE_"}
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@property
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def db_path(self) -> Path:
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return self.data_dir / "atocore.db"
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@property
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def chroma_path(self) -> Path:
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return self.data_dir / "chroma"
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settings = Settings()
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