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>

src/atocore/retrieval/embeddings.py

@@ -0,0 +1,32 @@
+"""Embedding model management."""
+
+from sentence_transformers import SentenceTransformer
+
+from atocore.config import settings
+from atocore.observability.logger import get_logger
+
+log = get_logger("embeddings")
+
+_model: SentenceTransformer | None = None
+
+
+def get_model() -> SentenceTransformer:
+    """Load and cache the embedding model."""
+    global _model
+    if _model is None:
+        log.info("loading_embedding_model", model=settings.embedding_model)
+        _model = SentenceTransformer(settings.embedding_model)
+        log.info("embedding_model_loaded", model=settings.embedding_model)
+    return _model
+
+
+def embed_texts(texts: list[str]) -> list[list[float]]:
+    """Generate embeddings for a list of texts."""
+    model = get_model()
+    embeddings = model.encode(texts, show_progress_bar=False, normalize_embeddings=True)
+    return embeddings.tolist()
+
+
+def embed_query(query: str) -> list[float]:
+    """Generate embedding for a single query."""
+    return embed_texts([query])[0]