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Addresses items 4-8 from the max-effort review of PR #272 (VectifyAI/PageIndex#272). - agent.py: wrap_with_doc_context() strips '<'/'>' from doc_name/doc_description (untrusted: unsanitized filename / LLM-generated from document content) before inserting them into the <docs>...</docs> block, so embedded content can never form a literal </docs> that closes the delimiter early and escapes the untrusted-data boundary SCOPED_SYSTEM_PROMPT relies on. Deterministic per-field transform, doesn't touch the (cacheable) static system prompt. - ConfigLoader.load() (legacy 0.2.x compat) now routes merged overrides through IndexConfig before returning, so a legacy 'no' string gets pydantic's bool coercion instead of surviving as a truthy non-empty string — page_index_main's bare `if opt.if_add_node_summary:` checks (changed from `== 'yes'` elsewhere in this PR) were silently inverting caller intent and firing unwanted billed LLM calls. - verify_toc, process_large_node_recursively, tree_parser, generate_summaries_for_structure, generate_summaries_for_structure_md: added return_exceptions=True to their asyncio.gather calls (llm_completion/ llm_acompletion raise RuntimeError on retry exhaustion, added earlier in this PR), each with a degrade path matching the pattern already used by sibling hardened gathers in the same files. One transient LLM failure no longer aborts the whole document's indexing. - _llm_semaphore is now a true process-wide ceiling (threading.Semaphore, shared across every thread/event loop) instead of one asyncio.Semaphore per event loop -- concurrently indexing N documents on N threads no longer multiplies the effective cap by N. A max_concurrency_scope() override is layered as a second, nested, per-loop restriction that can only tighten the effective cap within the ceiling, never widen past it. - set_llm_params() mutated a bare process-wide dict with no per-call isolation, unlike max_concurrency which already had ContextVar scoping. Added llm_params_scope() (mirrors max_concurrency_scope) + IndexConfig.llm_params, wired into build_index() the same way max_concurrency already was, so concurrent indexing jobs with different llm kwargs don't leak into each other. Adds regression tests for all five. Full suite: 221 passed, 2 skipped (one pre-existing, unrelated flaky cloud-streaming test intermittently fails on rerun; confirmed independent of this change). Claude-Session: https://claude.ai/code/session_01Kx5DgKbhK1N8autqXH8SmS
147 lines
5.8 KiB
Python
147 lines
5.8 KiB
Python
# pageindex/index/pipeline.py
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from __future__ import annotations
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from ..parser.protocol import ContentNode, ParsedDocument
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def detect_strategy(nodes: list[ContentNode]) -> str:
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"""Determine which indexing strategy to use based on node data."""
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if any(n.level is not None for n in nodes):
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return "level_based"
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return "content_based"
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def build_tree_from_levels(nodes: list[ContentNode]) -> list[dict]:
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"""Strategy 0: Build tree from explicit level information.
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Adapted from pageindex/page_index_md.py:build_tree_from_nodes."""
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stack = []
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root_nodes = []
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for node in nodes:
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tree_node = {
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"title": node.title or "",
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"text": node.content,
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"line_num": node.index,
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"nodes": [],
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}
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current_level = node.level or 1
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while stack and stack[-1][1] >= current_level:
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stack.pop()
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if not stack:
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root_nodes.append(tree_node)
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else:
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parent_node, _ = stack[-1]
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parent_node["nodes"].append(tree_node)
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stack.append((tree_node, current_level))
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return root_nodes
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def _run_async(coro):
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"""Run an async coroutine, handling the case where an event loop is already running."""
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import asyncio
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import concurrent.futures
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import contextvars
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# Only the detection is guarded — NOT the run. If the coroutine's own work
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# raises RuntimeError, letting it fall into `except RuntimeError` here would
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# misfire the "no running loop" branch and mask the real error behind a
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# bogus "asyncio.run() cannot be called from a running event loop".
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try:
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asyncio.get_running_loop()
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except RuntimeError:
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# No running loop -- drive the coroutine directly.
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return asyncio.run(coro)
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# Already inside an event loop -- run in a separate thread so we don't nest
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# asyncio.run. Copy the current context so ContextVar-based settings (e.g.
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# the max_concurrency_scope override set by build_index) propagate into the
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# worker thread; .result() re-raises the worker's real exception unchanged.
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ctx = contextvars.copy_context()
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with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
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return pool.submit(ctx.run, asyncio.run, coro).result()
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def build_index(parsed: ParsedDocument, model: str = None, opt=None) -> dict:
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"""Main entry point: ParsedDocument -> tree structure dict.
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Routes to the appropriate strategy and runs enhancement."""
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from .utils import (write_node_id, add_node_text, remove_structure_text,
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generate_summaries_for_structure, generate_doc_description,
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create_clean_structure_for_description)
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from ..config import IndexConfig, max_concurrency_scope, llm_params_scope
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if opt is None:
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opt = IndexConfig(model=model) if model else IndexConfig()
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# Scope the per-index concurrency cap AND llm kwargs to THIS call only (per
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# thread/async context), so concurrent indexing of other documents isn't
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# affected and a one-off value never sticks as the process default.
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with max_concurrency_scope(getattr(opt, "max_concurrency", None)), \
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llm_params_scope(getattr(opt, "llm_params", None)):
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nodes = parsed.nodes
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strategy = detect_strategy(nodes)
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if strategy == "level_based":
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structure = build_tree_from_levels(nodes)
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# For level-based, text is already in the tree nodes
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else:
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# Strategies 1-3: convert ContentNode list to page_list format for existing pipeline
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page_list = [(n.content, n.tokens) for n in nodes]
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structure = _run_async(_content_based_pipeline(page_list, opt))
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# Unified enhancement
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if opt.if_add_node_id:
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write_node_id(structure)
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if strategy != "level_based":
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if opt.if_add_node_text or opt.if_add_node_summary:
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add_node_text(structure, page_list)
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if opt.if_add_node_summary:
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_run_async(generate_summaries_for_structure(structure, model=opt.model))
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result = {
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"doc_name": parsed.doc_name,
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"structure": structure,
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}
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if opt.if_add_doc_description:
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clean_structure = create_clean_structure_for_description(structure)
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result["doc_description"] = generate_doc_description(
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clean_structure, model=opt.model
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)
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# 'text' is populated for level_based (Markdown, always) or for
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# content_based when if_add_node_text/if_add_node_summary requested it.
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# Strip it LAST, for BOTH strategies, unless explicitly requested —
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# otherwise a default index leaks each node's full text into
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# get_document_structure / storage, inconsistent with
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# if_add_node_text=False, the README, and the legacy md_to_tree. Skip
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# the walk entirely when text was never added in the first place
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# (content_based with if_add_node_text=if_add_node_summary=False) —
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# there's nothing to strip.
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text_present = strategy == "level_based" or opt.if_add_node_text or opt.if_add_node_summary
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if text_present and not opt.if_add_node_text:
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remove_structure_text(structure)
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return result
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class _NullLogger:
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"""Minimal logger that satisfies the tree_parser interface without writing files."""
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def info(self, message, **kwargs): pass
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def error(self, message, **kwargs): pass
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def debug(self, message, **kwargs): pass
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async def _content_based_pipeline(page_list, opt):
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"""Strategies 1-3: delegates to the existing PDF pipeline from pageindex/page_index.py.
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The page_list is already in the format expected by tree_parser:
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[(page_text, token_count), ...]
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"""
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from .page_index import tree_parser
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logger = _NullLogger()
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structure = await tree_parser(page_list, opt, doc=None, logger=logger)
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return structure
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