fix: prevent Document nodes from bypassing chunking pipeline#509
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LlamaIndex's Document class inherits from BaseNode, so the previous isinstance(doc, BaseNode) check classified all Documents as pre-embedded nodes, skipping SentenceSplitter entirely. This sent full-document text (1M+ chars) to the embedding API, triggering HTTP 400 input-length errors. Replace the type check with an explicit embedding-presence check that correctly distinguishes pre-embedded nodes (e.g. ImageNode from multimodal loaders) from regular Documents that still require splitting.
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May 27, 2026
Security: lock down the TutorBot tool sandbox (shell exec is opt-in, all filesystem/shell access confined to the bot workspace) and isolate per-user resources, closing #518, #517, #516, #515, #514 and #506 (first hardened in #507). Bug fixes: chat input disabled after the first turn (#520), KB embedding failure on long documents (#521 / #509), profile creation under Docker (#512 / #513), Qwen reasoning models failing native tool calling (#527 / #528), the GPT-5 init-wizard token parameter (#508), and oversized session-event truncation (#524). Features: HTTP/SSE API for multi-turn chat with a specific TutorBot (#511), multimodal image fallback for vision-capable providers without a capability entry, safe ZIP knowledge upload, and a /settings/network page with model fetching (community PRs #522 and #523 reimplemented locally). Also bumps __version__ to 1.4.1, adds the v1.4.1 release notes, updates the README Releases section, and ships the Astro + Starlight docs site under site/. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
Fixes a bug where all documents bypass
SentenceSplitterduring KB indexing, causing full-document text (1M+ chars) to be sent to the embedding API and triggering HTTP 400 input-length errors.Root Cause
LlamaIndex's
Documentclass inherits fromBaseNode. The previous filtering logic:classified every Document as a pre-embedded node, skipping the chunking pipeline entirely.
Fix
Replace the naive
isinstance(doc, BaseNode)check with_has_precomputed_embedding()that:Documentinstances as needing chunkingBaseNodesubclasses and already carry an embedding vector (e.g.ImageNodefrom multimodal loaders)Testing
Verified end-to-end: KB creation with a 1M+ char PDF now correctly chunks into 512-token segments and embeds successfully via DashScope
text-embedding-v3.