File size: 2,329 Bytes
f6a6230 1655b84 8e010a3 1655b84 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | from clinical_rag_eval.evaluate import find_evidence_phrase_hits, find_keyword_hits
from clinical_rag_eval.ingest import chunk_text, is_low_value_chunk, normalize_text
from clinical_rag_eval.langchain_retriever import preview_text
def test_normalize_text_collapses_whitespace() -> None:
raw_text = " MIMIC-IV\n\ncontains clinical\tdata. "
assert normalize_text(raw_text) == "MIMIC-IV contains clinical data."
def test_character_chunking_uses_overlap() -> None:
chunks = chunk_text("abcdefghijklmnopqrstuvwxyz", chunk_size=10, overlap=3)
assert chunks == ["abcdefghij", "hijklmnopq", "opqrstuvwx", "vwxyz"]
def test_low_value_chunk_detects_reference_heavy_text() -> None:
text = (
"References [1] Smith et al. 2020 http://example.com "
"[2] Jones et al. 2021 https://example.org "
"[3] arXiv:2301.00001 [4] doi:10.1000/test"
)
assert is_low_value_chunk(text)
def test_low_value_chunk_keeps_explanatory_text() -> None:
text = (
"Retrieval augmented generation combines retrieved context with a "
"generation model so answers can be grounded in external knowledge."
)
assert not is_low_value_chunk(text)
def test_keyword_matching_is_case_insensitive() -> None:
documents = [
"MIMIC-IV is a publicly available electronic health record dataset."
]
keywords = ["Publicly Available", "electronic health record", "missing"]
assert find_keyword_hits(documents, keywords) == [
"Publicly Available",
"electronic health record",
]
def test_evidence_phrase_matching_normalizes_whitespace() -> None:
documents = [
"Documents are split into chunks,\nencoded into vectors, and stored "
"in a vector database."
]
phrases = [
"split into chunks, encoded into vectors",
"stored in a vector database",
"not present",
]
assert find_evidence_phrase_hits(documents, phrases) == [
"split into chunks, encoded into vectors",
"stored in a vector database",
]
def test_langchain_preview_cleans_text_without_model_downloads() -> None:
text = "Retrieval-\naugmented generation\nuses retrieved context."
assert preview_text(text, max_chars=80) == (
"Retrieval-augmented generation uses retrieved context."
)
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