| """Central configuration for the Turkish medical vector-search pipeline. | |
| Every tunable lives here so the build / search / eval scripts stay in sync and | |
| the README can point to a single source of truth. | |
| """ | |
| from pathlib import Path | |
| # --- Paths ----------------------------------------------------------------- | |
| ROOT = Path(__file__).resolve().parent.parent | |
| DATA_DIR = ROOT / "data" | |
| OUTPUT_DIR = ROOT / "outputs" | |
| CHROMA_DIR = ROOT / "chroma_db" | |
| CHUNKS_PARQUET = DATA_DIR / "chunks.parquet" # HF-delivery table (url, chunk_text, chunk_vector, ...) | |
| TEST_QUESTIONS = DATA_DIR / "test_questions.json" # 20 positive + 10 negative | |
| BENCHMARK_JSON = OUTPUT_DIR / "benchmark_results.json" | |
| THRESHOLD_CSV = OUTPUT_DIR / "threshold_analysis.csv" | |
| # --- Data source ----------------------------------------------------------- | |
| # Gated HF dataset — access is auto-granted after accepting the terms while | |
| # logged in (huggingface-cli login). We scope to a single hospital (Acibadem) | |
| # for a clean, self-consistent corpus, per the "belirli bir hastane" option. | |
| DATASET_REPO = "umutertugrul/turkish-hospital-medical-articles" | |
| HOSPITAL_FILE = "data/acibadem-00000-of-00001.parquet" | |
| SOURCE_NAME = "acibadem" | |
| N_ARTICLES = 250 # articles sampled (spec range: 100–1000) | |
| MIN_ARTICLE_CHARS = 500 # drop stubs | |
| RANDOM_SEED = 42 # reproducible sampling | |
| # --- Embedding model ------------------------------------------------------- | |
| # magibu/embeddingmagibu-200m: Turkish-focused, Gemma3-based distilled encoder. | |
| # 768-dim output, 8192-token context, produces L2-normalised vectors. | |
| EMBED_MODEL = "magibu/embeddingmagibu-200m" | |
| EMBED_DIM = 768 | |
| NORMALIZE = True # unit vectors -> dot product == cosine similarity | |
| # --- Chunking (paragraph/sentence-aware, token-budgeted, with overlap) ----- | |
| MAX_TOKENS_PER_CHUNK = 384 | |
| CHUNK_OVERLAP_TOKENS = 64 | |
| MIN_CHUNK_TOKENS = 24 # discard trailing scraps | |
| # --- Vector store ---------------------------------------------------------- | |
| COLLECTION_NAME = "acibadem_medical" | |
| DISTANCE_SPACE = "cosine" # Chroma distance = 1 - cosine_similarity | |
| # --- Search & threshold ---------------------------------------------------- | |
| TOP_K = 5 | |
| # Cosine-similarity gate: queries whose best match scores below this are | |
| # answered with the "not in my documents" refusal instead of a retrieved chunk. | |
| # The value below is the empirical optimum found by src/evaluate.py — see the | |
| # threshold analysis in README.md. | |
| SIMILARITY_THRESHOLD = 0.49 | |
| REFUSAL_MESSAGE = "Bu sorunun cevabı dokümanlarımda yer almamaktadır." | |