From 9327a0d43edbecae0dc7fe7ac18cbf6b9171ccbe Mon Sep 17 00:00:00 2001
From: Daniel Han <michaelhan2050@gmail.com>
Date: Sun, 31 May 2026 14:23:34 +0000
Subject: [PATCH] Studio RAG trim: drop late-chunking test

Removes the test for the late_chunk_encode path deleted in the previous commit.
Remaining RAG suite: 99 passed (the 2 multimodal failures are a pre-existing
test-isolation issue on the base branch, not introduced here).
---
 tests/python/test_rag_late_chunking.py | 113 -------------------------
 1 file changed, 113 deletions(-)
 delete mode 100644 tests/python/test_rag_late_chunking.py

diff --git a/tests/python/test_rag_late_chunking.py b/tests/python/test_rag_late_chunking.py
deleted file mode 100644
index d9747d1d13..0000000000
--- a/tests/python/test_rag_late_chunking.py
+++ /dev/null
@@ -1,113 +0,0 @@
-"""Late chunking tests (Phase 3B-late).
-
-Pure-python coverage of `chunk_pages_with_spans` runs always. The
-encoder test loads a small SentenceTransformer and is gated behind the
-existing `server` marker so default `pytest` runs skip it.
-"""
-
-import sys
-from pathlib import Path
-
-import pytest
-
-REPO_ROOT = Path(__file__).resolve().parents[2]
-STUDIO_BACKEND = REPO_ROOT / "studio" / "backend"
-if str(STUDIO_BACKEND) not in sys.path:
-    sys.path.insert(0, str(STUDIO_BACKEND))
-
-from core.rag.chunking import chunk_pages_with_spans
-from core.rag.parsers import ParsedPage
-
-
-def _wc_counter(text: str) -> int:
-    return max(1, len(text.split()))
-
-
-def test_spans_index_back_to_full_doc_text():
-    pages = [
-        ParsedPage(text = "# Section A\n\n" + ("alpha " * 20), page_number = 1),
-        ParsedPage(text = "# Section B\n\n" + ("beta " * 20), page_number = 2),
-    ]
-    full_doc, chunks, char_spans = chunk_pages_with_spans(
-        pages,
-        max_tokens = 12,
-        overlap_tokens = 0,
-        token_counter = _wc_counter,
-    )
-    assert chunks
-    assert len(chunks) == len(char_spans)
-    for chunk, (start, end) in zip(chunks, char_spans):
-        # Chunk text must equal the full_doc slice it claims.
-        assert full_doc[start:end] == chunk.text
-
-
-def test_chunks_inherit_page_number_by_overlap():
-    pages = [
-        ParsedPage(text = "page-one text here", page_number = 1),
-        ParsedPage(text = "page-two text here", page_number = 2),
-    ]
-    _full_doc, chunks, _spans = chunk_pages_with_spans(
-        pages,
-        max_tokens = 4,
-        overlap_tokens = 0,
-        token_counter = _wc_counter,
-    )
-    pages_seen = {c.page_number for c in chunks}
-    assert pages_seen <= {1, 2}
-    # Each page contributes at least one chunk.
-    assert 1 in pages_seen
-    assert 2 in pages_seen
-
-
-def test_full_doc_joins_pages_with_blank_line_separator():
-    pages = [
-        ParsedPage(text = "first", page_number = 1),
-        ParsedPage(text = "second", page_number = 2),
-    ]
-    full_doc, _chunks, _spans = chunk_pages_with_spans(
-        pages,
-        max_tokens = 5,
-        overlap_tokens = 0,
-        token_counter = _wc_counter,
-    )
-    assert "first" in full_doc
-    assert "second" in full_doc
-    # Pages separated by exactly one blank line.
-    assert "first\n\nsecond" in full_doc
-
-
-@pytest.mark.server
-def test_late_chunk_encode_returns_one_vector_per_span():
-    pytest.importorskip("sentence_transformers")
-    pytest.importorskip("torch")
-    # all-MiniLM-L6-v2: ~80MB, 384 dims.
-    import os
-
-    os.environ.setdefault(
-        "UNSLOTH_RAG_EMBEDDING_MODEL",
-        "sentence-transformers/all-MiniLM-L6-v2",
-    )
-    from core.rag import embeddings as embeddings_module
-
-    embeddings_module._model = None  # force re-load
-    embeddings_module._model_name = None
-
-    doc_text = (
-        "# Intro\n\n"
-        "The quick brown fox jumps over the lazy dog.\n\n"
-        "# Methods\n\n"
-        "We trained the model on a corpus of 100M tokens.\n\n"
-        "# Results\n\n"
-        "Accuracy improved by 12% over the baseline."
-    )
-    # char_spans: one per section, picked manually.
-    char_spans = [
-        (doc_text.index("The quick"), doc_text.index("\n\n# Methods")),
-        (doc_text.index("We trained"), doc_text.index("\n\n# Results")),
-        (doc_text.index("Accuracy"), len(doc_text)),
-    ]
-    vectors = embeddings_module.late_chunk_encode(doc_text, char_spans)
-    assert len(vectors) == len(char_spans)
-    dim = vectors[0].shape[0]
-    for v in vectors:
-        assert v.shape == (dim,)