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### 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 #17056 Closes #5782 Closes #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)
26 lines
1.2 KiB
Python
26 lines
1.2 KiB
Python
import json
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from pathlib import Path
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def test_mistral_ocr_factory_present_and_ocr_tagged():
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repo_root = Path(__file__).resolve().parents[3]
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data = json.loads((repo_root / "conf" / "llm_factories.json").read_text())
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factories = {f["name"]: f for f in data["factory_llm_infos"]}
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assert "Mistral OCR" in factories, "Mistral OCR factory missing"
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fac = factories["Mistral OCR"]
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assert "OCR" in fac["tags"]
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# ships a default OCR model (like the other OCR factories) so it is usable
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# without manually adding one through the model provider page; the llm_name
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# is the real Mistral API id because the parser POSTs it verbatim to /v1/ocr.
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models = {m["llm_name"]: m for m in fac["llm"]}
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assert "mistral-ocr-latest" in models
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assert models["mistral-ocr-latest"]["model_type"] == "ocr"
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assert "OCR" in models["mistral-ocr-latest"]["tags"]
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def test_mistral_ocr_factory_distinct_from_mistral():
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repo_root = Path(__file__).resolve().parents[3]
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data = json.loads((repo_root / "conf" / "llm_factories.json").read_text())
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names = [f["name"] for f in data["factory_llm_infos"]]
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assert "Mistral" in names and "Mistral OCR" in names
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