mbochniak01
Add full RAG evaluation pipeline with L1 metrics and UI
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from pathlib import Path
KNOWLEDGE_ROOT = Path(__file__).parent.parent / "knowledge"
EMBEDDER_MODEL = "all-MiniLM-L6-v2"
DOMAIN_CLIENTS: dict[str, list[str]] = {
"retail": ["novamart", "shelfwise"],
"pharma": ["clinixone", "pharmalink"],
}
CLIENT_DOMAIN: dict[str, str] = {
client: domain
for domain, clients in DOMAIN_CLIENTS.items()
for client in clients
}
DISPLAY_NAMES: dict[str, str] = {
"novamart": "NovaMart",
"shelfwise": "ShelfWise",
"clinixone": "ClinixOne",
"pharmalink": "PharmaLink",
}
def term_catalog_path(domain: str) -> Path:
return KNOWLEDGE_ROOT / domain / "term-catalog.yaml"
def features_path(domain: str) -> Path:
return KNOWLEDGE_ROOT / domain / "features.yaml"
def domain_for(client: str) -> str:
if client not in CLIENT_DOMAIN:
raise ValueError(f"Unknown client: {client!r}. Valid: {list(CLIENT_DOMAIN)}")
return CLIENT_DOMAIN[client]