Extract the alignment scaffolding so the format-specific PRs (text&code,
markdown golden, HTML) don't conflict on align_test.go.
Adds (test-only, no production behavior change):
- GoldenDoc{Meta,Items} and parseGolden: tolerant of both the legacy bare
array and the new {meta, items} golden format.
- LoadGoldenDoc, AcceptedDivergences(meta), FilterOutDocTypes(items, drop):
meta-driven divergence handling, no hardcoded divergence lists.
- TextCodeAlignOptions / DefaultTextCodeDelimiter, HTMLAlignOptions /
StripHTMLHeadingMarker: normalizer presets reused by the alignment tests.
- LoadGolden now tolerates the {meta, items} format.
Stacks on #18014 (the align_test.go framework is already in main).
Port dataset-level wiki incremental compile and refactor splitByTokens
token budgeting. Includes replace-only wiki merge, KNN dedup routing,
and template/config wiring.
## Summary
This PR improves the RAGFlow agentic-search path in three areas: it
stops the outer agent from re-looping over the same rag call, lets the
medium thinking mode discover and follow new sub-claims mid-loop, and
strengthens retrieval by having the LLM emit synonym-rich queries with
time/date/number terms boosted.
1. Avoid the outer re-loop — keep all multi-hop cycles inside agentic
RAG
2. Dynamic claims in medium mode — keep querying newly discovered
sub-questions
medium now enables allows_dynamic_claims. During orchestration, when
claim analysis discovers a new required sub-question
(discovered_claims), the loop spawns it as a new ClaimTarget and
continues searching it in subsequent cycles (bounded by the
dynamic-claim budget) instead of stopping. Also added:
3. Stronger query strategy — synonym-rich queries + time/date/number
weighting
LLM-generated synonyms: the claim-analysis prompt now instructs the
model to write each next_queries entry as a retrieval-boosted query that
actively folds in entity aliases, DATE/TIME synonyms (e.g. 1994 → 1994,
66th Academy Awards), and number/unit variants (e.g. 1.95 m → 6 ft 5
in).
Time/date/number boosting: query.py boosts numeric/date tokens to a high
weight (_NUM_DATE_TOKEN_RE).
This handoff doc was accidentally introduced by PR #18005 and should not
be part of the repository. Remove it to keep the tree clean.
Co-authored-by: xugangqiang <xugangqiang@users.noreply.github.com>
## What problem does this PR solve?
`TenantLLMService.model_instance` constructs vision providers with
`lang` as the third positional argument and `base_url` as a keyword
argument.
`LocalAICV` declared `base_url` as its third parameter, causing:
```text
TypeError: LocalAICV.__init__() got multiple values for argument 'base_url'
```
This prevents LocalAI vision models from being used during document
parsing.
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
