| """ |
| Search term mappings — applied before embedding wherever vector search occurs. |
| |
| Keys are lowercase; the lookup is case-insensitive. |
| When a user's query matches a key, the mapped value is sent to the embedder |
| instead, so abbreviations and acronyms get a richer representation. |
| |
| Edit QUERY_MAPPINGS freely — no other file needs to change. |
| """ |
| from __future__ import annotations |
|
|
| QUERY_MAPPINGS: dict[str, str] = { |
| "ai": "artificial intelligence", |
| "ki": "Künstliche Intelligenz", |
| "ml": "machine learning", |
| "dl": "deep learning", |
| "nlp": "natural language processing", |
| "cv": "computer vision", |
| "rl": "reinforcement learning", |
| "llm": "large language model", |
| "crispr": "gene editing", |
| } |
|
|
|
|
| def apply_mapping(term: str) -> tuple[str, bool]: |
| """ |
| Return (mapped_term, was_mapped). |
| |
| Looks up term.strip().lower() in QUERY_MAPPINGS. |
| If found, returns the mapped value and True. |
| Otherwise returns the original stripped term and False. |
| """ |
| stripped = term.strip() |
| mapped = QUERY_MAPPINGS.get(stripped.lower()) |
| if mapped: |
| return mapped, True |
| return stripped, False |
|
|
