This PR fixes two issues with MinerU PDF parser where table and image chunks were not properly classified or associated:
1. **Table chunks** were incorrectly classified as `text` instead of `table`
2. **Image chunks** were missing image resource association and classified as `text`
## Summary
The TitleChunker (both `hierarchy` and `group` methods) had no
token-size ceiling, so a long section without sub-headings became one
giant chunk. This adds a configurable `chunk_token_cap` that guarantees
every text chunk stays within a token budget.
When `TokenChunker` starts a fresh chunk with an overlap prefix (Go `computeOverlapPrefix` / Python visible-text cut), the previous chunk's **tail PDF coordinates were dropped**. As a result, the overlap head of a PDF chunk is displayed but **not highlighted** — the highlight box is shifted/truncated relative to the displayed span (infiniflow/ragflow#18148).
Unifies the Go TokenChunker merge path on a single `mergeUnits` core and
fixes coordinate-tag drift in the Python JSON merge at `overlap > 0`.
Rebased on top of #17979 (delimiter_mode convergence).
Converge `TokenChunker.delimiter_mode` from three values (`token_size`,
`delimiter`, `one`) to two (`delimiter`, `one`). The unified `delimiter`
mode now carries the old `token_size` semantics: when no active
(backtick) delimiter is present, text/JSON chunks are merged up to
`chunk_token_size`; when a backtick delimiter is present, the text is
split by it and not merged. `one` continues to be handled by the
separate `OneChunker`.
Ports the dataset knowledge compilation (wiki/graph/tree/mindmap) to the
Go scheduler with a status contract, aligns wiki storage/retrieval with
Python, and sizes prompts by content_length.
## Summary
Fixes a regression introduced by #17203 (strict-cap atom-split) and a
secondary delimiter-handling bug from #17723.
**Root cause:**
- #17203 added `_split_oversized_unit` / `_compute_chunk_update`, which
split oversize units into ≤ token_size pieces. This collapsed
`token_size=1` into 1-token chunks and set the cap at 512, mismatching
the model-layer truncation boundary (embedding ~8191 / rerank
500/4096/8192/2048). Atom-split is unnecessary: oversize units stay
whole and the model layer truncates.
- #17723's delimiter handling dropped consecutive delimiters (`A####B`
-> `A##B`), glued JSON items with `"".join`, ignored
`children_delimiters`, and stripped whitespace delimiters.
## Changes
- New pure helper `merge_paragraphs(paragraphs, token_size, strategy)`
with a `MergeStrategy` enum (`UNDER_CAP` / `OVER_CAP`); **default
`OVER_CAP`**. `UNDER_CAP` is a strict cap (never overflows
`token_size`); `OVER_CAP` greedily accumulates adjacent paragraphs while
the projected total stays within `token_size`, merging one
boundary-overflow paragraph before closing. Oversize paragraphs stand
alone.
- `naive_merge` / `naive_merge_with_images` /
`RAGFlowTxtParser.parser_txt` now use `merge_paragraphs`; atom-split
removed. `naive_merge` / `naive_merge_with_images` always split a
section on the delimiter whenever one is present (even when the section
already fits `token_size`), so delimiter text never leaks into a chunk.
Only the empty-delimiter (size-only) mode skips splitting.
- `token_chunker`: delimiter text is dropped (not stripped); JSON flush
joins buffered items with `"\n"`; `children_delimiters` and
`PDF_POSITIONS_KEY` are preserved on the delimiter path. PDF positions
are now attributed **per segment** — each split chunk carries only the
positions of the item(s) that contributed to it — fixing a leak where
page-N coordinates were attached to page-M chunks and all segments
shared one preview image.
- `test_txt_parser.py` rewritten to assert the new contract (not the old
strict cap); `naive_merge` and delimiter-case-sensitive matrices
updated.
## Contract (refs #17799)
- user specified delimiter = chunk boundary; user specified delimiter
text never enters a chunk.
- `token_size` = soft target + merge strategy; no atom-split.
- Default strategy = `OVER_CAP`; migration can switch to `UNDER_CAP`
(strict cap).
- `OVER_CAP` has no hard cap; the model layer truncates oversize units.
`UNDER_CAP` enforces a strict cap.
## Notes
- Closes the wrong-object revert in #17774 (revert #17723 would
re-introduce delimiter-in-chunk and the strict cap).
- Go-side alignment (`internal/ingestion/component/chunker/token.go`) is
a follow-up PR.
---------
Co-authored-by: CodeBuddy <noreply@tencent.com>
## Summary
`TokenChunker._invoke` discarded the user's configured `delimiters`
whenever no backtick-wrapped delimiter was present: it passed a
hardcoded `""` to `naive_merge`, so the configured delimiters (including
the default `["\n"]`) were never forwarded. `naive_merge` then ignored
the newline sentence boundary and cut chunks **mid-sentence** once the
token budget was exceeded.
## Root cause
`token_chunker.py:326` called `naive_merge(payload, chunk_token_size,
"", overlapped_percent)`. `_compile_delimiter_pattern` intentionally
returns `""` for bare (non-backtick) delimiters — that return value is a
*path selector* (empty → token-budget merge; non-empty → hard
`_split_text_by_pattern` split). The bug was not in that selector but in
the `else` branch, which threw away `self._param.delimiters` instead of
forwarding it.
## Fix
Forward the configured delimiters as a soft boundary:
```python
else naive_merge(
payload,
self._param.chunk_token_size,
"".join(self._param.delimiters),
overlapped_percent,
)
```
`naive_merge` already parses the string via the canonical
`parse_delimiter_field`, so bare and backtick-wrapped delimiters are
honored as soft boundaries while the token budget is still respected.
The path-selection role of `_compile_delimiter_pattern` is untouched:
backtick-wrapped delimiters still select the hard
`_split_text_by_pattern` path; bare delimiters still take the
token-budget merge path. No regression for any previously-working
(wrapped-delimiter) configuration.
## Test
Adds `rag/flow/tests/test_token_chunker_delimiter.py`:
- `test_token_chunker_token_size_mode_does_not_split_sentences` — fails
on the old code (3 sentences cut mid-stream), passes after the fix.
- `test_naive_merge_empty_delimiter_ignores_newline_break` — root-cause
companion asserting `naive_merge("")` cuts while `naive_merge("\n")`
preserves boundaries.
Both tests skip when the tokenizer is unavailable (dead-tokenizer
guard).
## Note
This branch contains only this one-line fix on top of `main`; it is
intentionally independent of the unrelated tokenizer/offline-BPE work.
Co-authored-by: CodeBuddy <noreply@codebuddy.ai>
## Summary
Refactor the Go `KnowledgeCompilerComponent` so its parameter is a
**single string template id** instead of a DSL-level `variant` (or
plural group id list). The `variant` is no longer in the DSL — it is now
**derived at runtime from the resolved compilation template's `kind`
field**.
This aligns the Go ingestion port with the frontend Compiler operator,
which emits a singular `compilation_template_group_id` and does not
write `variant` into the generated `compiler.json`.
### What problem does this PR solve?
Adds first-class support for **Mistral OCR** (`POST /v1/ocr`) as a
document parser, and fixes the long-standing bug where selecting
`mistral-ocr-latest` fails with `Can't find model for
<tenant>/image2text/mistral-ocr-latest`.
`mistral-ocr-latest` is Mistral's dedicated document-OCR endpoint, not a
vision-chat (`image2text`) model, but the catalog tagged it `image2text`
— so it resolved to the `CvModel` registry, which has no `Mistral`
entry, and there was no `OcrModel` entry either. This PR registers it
correctly and wires it end to end.
Closes#17056Closes#5782Closes#7075
**What it does**
1. **`MistralParser` + `MistralOcrModel`**
(`deepdoc/parser/mistral_parser.py`, `rag/llm/ocr_model.py`) — a proper
`OcrModel` factory `Mistral OCR`, mirroring the SoMark cloud-OCR
template. Tables stay inline as HTML; the page range maps to Mistral's
native `pages` selector (absolute page indices, billed per selected
page, so multi-task documents do not re-OCR the whole file); documents
over the inline limit go through the `/v1/files` signed-URL flow with
cleanup.
2. **Removes the `image2text` mis-tag** for `mistral-ocr-latest` from
the `Mistral` factory in `conf/llm_factories.json` (it now lives only in
the `Mistral OCR` factory, typed `ocr`). This is what closes the `Can't
find model` path.
3. **`MistralCV`** (`rag/llm/cv_model.py`) — a thin `GptV4` subclass
over Mistral's OpenAI-compatible endpoint, registering a `Mistral` entry
in the `CvModel` registry so Mistral vision models (`pixtral-*`) become
usable as `image2text` at all.
4. **Figure description** — Mistral-OCR-extracted figures are captioned
using the tenant's configured `image2text` model (any provider),
matching MinerU/deepdoc behaviour.
5. **Wires the parser into every chunking method** (`naive`, `paper`,
`book`, `laws`, `manual`, `one`, `presentation`) and the `rag/flow` DAG
path. This also fixes a related latent gap where those chunkers
forwarded only `mineru_llm_name`, so any model-based OCR provider
selected on a non-`naive` method silently fell through.
**Notes on the API contract** (verified against the live Mistral API):
`pages` is a selector (returns absolute `index`, bills only the
requested pages); `include_blocks: true` returns per-block bounding
boxes usable for chunk highlighting and figure cropping; large files use
`POST /v1/files` → signed URL → OCR → `DELETE`.
**Testing**: new unit tests cover the response→sections contract (both
the 2-tuple `naive` path and the typed 3-tuple DAG path), the
position-tag rescale, the HTTP client incl. upload failure/cleanup
paths, `parse_pdf` page-range threading, registry registration, env
config, the suffix normalization, the factory catalog entry, `MistralCV`
registration, and figure-description injection. Verified end to end
against the live Mistral API on real PDFs (table extraction,
page-selector cost avoidance, figure captioning).
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
Fixes#16812
### Problem
In the `rag/flow` ingestion pipeline, when `TitleChunker` feeds
`TokenChunker`, the chapter-aware chunks are silently discarded and the
parser's raw flat json is re-chunked instead.
`TitleChunker` emits `output_format="chunks"` and writes its
chapter-aware output to the `chunks` field
(`rag/flow/chunker/title_chunker/common.py`,
`set_output("output_format", "chunks")`). But `TokenChunker._invoke`
only handles `output_format` in `["markdown", "text", "html"]`, then
falls through to the `# json` path which reads
`from_upstream.json_result`. There is no branch for `"chunks"`, so
`from_upstream.chunks` is never read.
Downstream effects reported in #16812: PageIndex/TOC extraction receives
flat line-level text instead of structured chapter blocks
(incorrect/duplicate/missing chapters), and retrieval quality degrades
because chunks are no longer aligned to document structure.
### Fix
Select the source list based on `output_format`, mirroring the exact
pattern already used in `title_chunker/common.py`:
```python
json_result = (from_upstream.chunks if from_upstream.output_format == "chunks" else from_upstream.json_result) or []
```
`chunks` items share the same dict shape as `json_result` items (both
consumed via `.get("text")`, `.get("doc_type_kwd")`, etc.), so they flow
through the existing token-sizing path unchanged. One-line change, no
behavior change for the `json`/`markdown`/`text`/`html` paths.
### Test
Adds `rag/flow/tests/test_token_chunker.py`, an isolated unit test that
runs the real `TokenChunker._invoke` (heavy deps stubbed; real pydantic
schema used when available) and asserts that with
`output_format="chunks"` the upstream `chunks` are consumed rather than
the raw parser `json`.
Verified RED -> GREEN: the test fails against the current code (reads
the raw json) and passes with the fix.
Signed-off-by: Yash Raj Pandey <yashpn62@gmail.com>
### What problem does this PR solve?
- Clear stale pipeline IDs and generated data when updating documents
without `pipeline_id`.
- Support tree compilation results in pipeline workflows.
- Update compilation templates in place while preserving existing
template IDs.
- Improve duplicate-template validation messages.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
TOC chunks now include a toc field so the agent pipeline logs expose the
data the frontend expects.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Set OpenDataLoader and call in parser and naive
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Force image parser runtime output format to JSON so downstream chunking
reads OCR results from the JSON output and image parser chunks can be
displayed.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Co-authored-by: Wang Qi <wangq8@outlook.com>
fix: restore TitleChunker output for json/chunks upstream formats
## Summary
The refactor commit e194027b (#14247) introduced two regressions that
caused `TitleChunker` to produce zero chunks when the upstream Parser
node outputs `json` or `chunks` format (e.g. PDF parsing).
## Root Cause
### 1. Dead code in `extract_line_records` (critical)
After refactor, when `payload` is `None` (which is the case for `json`
and `chunks` output formats), the method returns an empty list
immediately via `return []`, so no records are ever extracted from
structured upstream output. The original `json`/`chunks` handling code
became unreachable dead code.
### 2. Unconditional overwrite in `build_chunks_from_record_groups`
The `chunks` variable assigned in the `if` branch for markdown/text/html
formats was unconditionally overwritten by the statement below it, due
to a missing `else` keyword.
## Fix
- Remove the premature `return []` so the `json`/`chunks` branch is
reachable again.
- Add `else` branch in `build_chunks_from_record_groups` so the two
format families are handled independently.
## Test Plan
- [x] Verified no lint errors on the changed file
- [ ] Tested with a PDF document parsed via DeepDOC → TitleChunker
pipeline
- [ ] Tested with markdown input through TitleChunker
- [ ] Tested hierarchy and group chunking modes
## Impact
- Fixes the regression where documents parsed with `json`/`chunks`
output format produced no chunks from `TitleChunker`.
- No API or configuration changes. Fully backward compatible.
Signed-off-by: noob <yixiao121314@outlook.com>
### What problem does this PR solve?
Python implementation of the Go-based model_provider API suite.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: bill <yibie_jingnian@163.com>
### What problem does this PR solve?
1. Break huge function into smaller pieces
2. Add unit test for the smaller pieces function
3. Layer-ed design
a. infra layer - task_context.py, recording_context.py,
write_operation_interceptor.py, ...
b. service layer - *_service.py
c. business layer - task_handler.py
4. Default behavior: use "refactor-ed version" - can switch to original
version by change env variable
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
- [x] Performance Improvement
---------
Co-authored-by: Liu An <asiro@qq.com>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
