mirror of
https://github.com/VectifyAI/PageIndex.git
synced 2026-07-24 21:41:04 +02:00
a0fb863957
- demos: use the post-cutoff attention-residuals paper (2603.15031) again so answers provably come from retrieval, and run the QA agent on retrieve_model - client: re-expose model / retrieve_model as public attributes (0.2.x surface) - pipeline: markdown summaries go back through generate_summaries_for_structure_md with the 200-token threshold (small nodes skip the LLM call) - README: restore (yes/no, default: X) flag docs with correct defaults; note that retrieve_model drives agent QA
204 lines
8.2 KiB
Python
204 lines
8.2 KiB
Python
# tests/sdk/test_pipeline.py
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import asyncio
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from unittest.mock import patch, AsyncMock
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from pageindex.parser.protocol import ContentNode, ParsedDocument
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from pageindex.index.pipeline import (
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detect_strategy, build_tree_from_levels, build_index,
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_content_based_pipeline, _NullLogger,
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)
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def test_detect_strategy_with_level():
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nodes = [
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ContentNode(content="# Intro", tokens=10, title="Intro", index=1, level=1),
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ContentNode(content="## Details", tokens=10, title="Details", index=5, level=2),
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]
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assert detect_strategy(nodes) == "level_based"
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def test_detect_strategy_without_level():
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nodes = [
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ContentNode(content="Page 1 text", tokens=100, index=1),
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ContentNode(content="Page 2 text", tokens=100, index=2),
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]
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assert detect_strategy(nodes) == "content_based"
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def test_detect_strategy_empty_nodes_is_level_based():
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"""An empty node list (e.g. an empty/whitespace-only source file) must
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route to level_based, whose build_tree_from_levels([]) returns an empty
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structure with zero LLM calls — not content_based, whose TOC-detection
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pipeline needs real page content and wastes an LLM call before failing."""
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assert detect_strategy([]) == "level_based"
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def test_build_index_on_empty_document_makes_no_llm_calls():
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from pageindex.config import IndexConfig
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parsed = ParsedDocument(doc_name="empty", nodes=[])
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opt = IndexConfig(if_add_node_summary=False, if_add_doc_description=False)
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result = build_index(parsed, opt=opt)
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assert result == {"doc_name": "empty", "structure": []}
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def test_build_tree_from_levels():
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nodes = [
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ContentNode(content="ch1 text", tokens=10, title="Chapter 1", index=1, level=1),
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ContentNode(content="s1.1 text", tokens=10, title="Section 1.1", index=5, level=2),
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ContentNode(content="s1.2 text", tokens=10, title="Section 1.2", index=10, level=2),
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ContentNode(content="ch2 text", tokens=10, title="Chapter 2", index=20, level=1),
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]
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tree = build_tree_from_levels(nodes)
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assert len(tree) == 2 # 2 root nodes (chapters)
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assert tree[0]["title"] == "Chapter 1"
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assert len(tree[0]["nodes"]) == 2 # 2 sections under chapter 1
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assert tree[0]["nodes"][0]["title"] == "Section 1.1"
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assert tree[0]["nodes"][1]["title"] == "Section 1.2"
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assert tree[1]["title"] == "Chapter 2"
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assert len(tree[1]["nodes"]) == 0
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def test_build_tree_from_levels_single_level():
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nodes = [
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ContentNode(content="a", tokens=5, title="A", index=1, level=1),
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ContentNode(content="b", tokens=5, title="B", index=2, level=1),
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]
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tree = build_tree_from_levels(nodes)
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assert len(tree) == 2
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assert tree[0]["title"] == "A"
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assert tree[1]["title"] == "B"
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def test_build_tree_from_levels_zero_based_levels():
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nodes = [
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ContentNode(content="c", tokens=5, title="Chapter", index=1, level=0),
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ContentNode(content="s", tokens=5, title="Section", index=2, level=1),
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]
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tree = build_tree_from_levels(nodes)
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assert len(tree) == 1
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assert tree[0]["title"] == "Chapter"
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assert [n["title"] for n in tree[0]["nodes"]] == ["Section"]
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def test_build_tree_from_levels_deep_nesting():
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nodes = [
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ContentNode(content="h1", tokens=5, title="H1", index=1, level=1),
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ContentNode(content="h2", tokens=5, title="H2", index=2, level=2),
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ContentNode(content="h3", tokens=5, title="H3", index=3, level=3),
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]
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tree = build_tree_from_levels(nodes)
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assert len(tree) == 1
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assert tree[0]["title"] == "H1"
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assert len(tree[0]["nodes"]) == 1
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assert tree[0]["nodes"][0]["title"] == "H2"
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assert len(tree[0]["nodes"][0]["nodes"]) == 1
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assert tree[0]["nodes"][0]["nodes"][0]["title"] == "H3"
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def test_content_based_pipeline_does_not_raise():
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"""_content_based_pipeline should delegate to tree_parser, not raise NotImplementedError."""
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fake_tree = [{"title": "Intro", "start_index": 1, "end_index": 2, "nodes": []}]
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async def fake_tree_parser(page_list, opt, doc=None, logger=None):
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return fake_tree
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page_list = [("Page 1 text", 50), ("Page 2 text", 60)]
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from types import SimpleNamespace
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opt = SimpleNamespace(model="test-model")
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with patch("pageindex.index.page_index.tree_parser", new=fake_tree_parser):
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result = asyncio.run(_content_based_pipeline(page_list, opt))
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assert result == fake_tree
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def test_null_logger_methods():
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"""NullLogger should have info/error/debug and not raise."""
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logger = _NullLogger()
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logger.info("test message")
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logger.error("test error")
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logger.debug("test debug")
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logger.info({"key": "value"})
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def _structure_has_text(nodes) -> bool:
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for n in nodes:
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if "text" in n:
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return True
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if n.get("nodes") and _structure_has_text(n["nodes"]):
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return True
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return False
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def test_level_based_strips_text_by_default():
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"""Markdown (level_based) must honor if_add_node_text=False — build_tree_from_
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levels seeds 'text', and it used to leak into the output/storage."""
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from pageindex.config import IndexConfig
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nodes = [
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ContentNode(content="# Intro\nbody one", tokens=5, title="Intro", index=1, level=1),
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ContentNode(content="## Sub\nbody two", tokens=5, title="Sub", index=2, level=2),
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]
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parsed = ParsedDocument(doc_name="d", nodes=nodes)
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# No summary/description -> no LLM calls.
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opt = IndexConfig(if_add_node_summary=False, if_add_doc_description=False,
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if_add_node_text=False)
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result = build_index(parsed, opt=opt)
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assert not _structure_has_text(result["structure"])
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def test_level_based_keeps_text_when_requested():
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from pageindex.config import IndexConfig
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nodes = [ContentNode(content="# Intro\nbody", tokens=5, title="Intro", index=1, level=1)]
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parsed = ParsedDocument(doc_name="d", nodes=nodes)
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opt = IndexConfig(if_add_node_summary=False, if_add_doc_description=False,
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if_add_node_text=True)
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result = build_index(parsed, opt=opt)
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assert _structure_has_text(result["structure"])
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def test_build_index_scopes_llm_params_to_the_call(monkeypatch):
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"""IndexConfig(llm_params=...) must reach get_llm_params() for the duration
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of this build_index() call only, and not leak into the process default."""
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from pageindex.config import IndexConfig, get_llm_params, set_llm_params
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set_llm_params(temperature=0)
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seen = {}
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async def fake_generate_summaries(structure, summary_token_threshold=200, model=None):
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seen["llm_params"] = get_llm_params()
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monkeypatch.setattr(
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"pageindex.index.page_index_md.generate_summaries_for_structure_md",
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fake_generate_summaries,
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)
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# level_based (Markdown) strategy avoids the content_based path's own real
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# LLM-driven TOC detection, so this stays a fast, network-free unit test.
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nodes = [ContentNode(content="# Intro\nbody", tokens=5, title="Intro", index=1, level=1)]
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parsed = ParsedDocument(doc_name="d", nodes=nodes)
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opt = IndexConfig(if_add_node_summary=True, if_add_doc_description=False,
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llm_params={"temperature": 1})
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build_index(parsed, opt=opt)
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assert seen["llm_params"]["temperature"] == 1 # scoped override was in effect
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assert get_llm_params()["temperature"] == 0 # process default untouched afterward
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def test_check_title_appearance_tolerates_out_of_range_physical_index():
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"""An LLM-emitted physical_index outside page_list must be marked 'no', not
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raise IndexError (which happens during task construction, outside the
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gather's return_exceptions protection, and would abort the whole build)."""
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from pageindex.index.page_index import check_title_appearance_in_start_concurrent
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page_list = [("only page text", 3)] # length 1
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structure = [
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{"title": "A", "physical_index": 5}, # out of range -> would IndexError
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{"title": "B", "physical_index": 0}, # 0 -> would wrap to page_list[-1]
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{"title": "C", "physical_index": None}, # missing
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{"title": "D"}, # no physical_index key at all
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]
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result = asyncio.run(
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check_title_appearance_in_start_concurrent(structure, page_list)
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)
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assert all(item["appear_start"] == "no" for item in result)
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