`_detect_text_encoding()` hands the complete file to `chardet.detect()`. chardet is pure Python and costs roughly 1.3 seconds per megabyte, so uploading a large non-UTF-8 text file stalls for seconds inside encoding detection alone. A 4 MiB Shift-JIS file spends 6.4 seconds there. The UTF-8 fast path above it means only non-UTF-8 files reach this, which in practice are exactly the CJK documents the surrounding code was written to handle, so the slow case and the case that matters are the same case.
Detection does not need the whole file. It needs the bytes that are actually not UTF-8, and `UnicodeDecodeError.start` from the fast-path decode already says where those begin, so this samples a 256 KiB window around that offset.
Two things make that safe rather than merely fast.
Centring the window on the first non-UTF-8 byte instead of the file head is what keeps the common case correct. A plain head sample makes chardet report ascii for a file that is ASCII for its first few hundred KiB and only turns CJK later, and the method then falls through to latin-1 instead of the right codec.
The window still cannot help when a stray byte, a pasted Windows-1252 artifact for example, sits hundreds of KiB ahead of the real payload: the sample is then almost pure ASCII and carries no signal. So when the sample holds almost no non-ASCII bytes and is a strict subset of the file, detection falls back to the whole buffer. That case pays the old cost, which is the right trade, because it is precisely the case where sampling would otherwise be wrong. Without this guard a Cyrillic document with a stray leading byte was detected as ISO-8859-1 rather than windows-1251, which is silent mojibake.
Measured, with the encoding returned identical in every case:
| file | before | after |
|---|---|---|
| shift_jis 4 MiB | 6402ms | 755ms |
| gb18030 4 MiB | 3199ms | 449ms |
| big5 4 MiB | 2926ms | 413ms |
| euc-jp 4 MiB | 2456ms | 413ms |
| euc-kr 4 MiB | 2382ms | 468ms |
| latin-1 4 MiB | 1902ms | 394ms |
| gb18030 1 MiB | 807ms | 376ms |
| ascii head then gb18030 tail | 533ms | 294ms |
| stray byte then cp1251 payload | 496ms | 1051ms |
| any UTF-8 file | 8ms | 0ms |
29 cases, all returning an identical encoding before and after: six encodings at 100 KiB, 1 MiB and 4 MiB, three layouts where the non-UTF-8 bytes only begin beyond the window, four where a stray byte is separated from the payload, plus plain UTF-8, UTF-8 CJK and an empty file. The stray-byte rows are slower than before because they scan twice, once over the window and once over the whole buffer. They are the pathological shape, and correctness wins there.
The residual time is now the decode-and-validate loop below, which walks the file once per candidate codec, and `_has_cjk_characters`, which is a per-character Python loop over the decoded text. Both are the same "full scan for a detection decision" pattern and could take a bounded prefix too. That is left alone here.
When `RAG_DOCUMENT_LOADER_ENGINE` is set to `paddleocr_vl`, the dispatch branch in `Loader._get_loader` checked only the engine name and a non-empty token, so every uploaded file was handed to the PaddleOCR-VL loader regardless of its type. Text based uploads such as `.md`, `.txt` and `.csv` were base64 encoded and posted to the `/layout-parsing` endpoint tagged as PDFs, and the API rejected them with `422 Unprocessable Entity` ("PDFium: Data format error"), so those files never indexed at all.
The loader already knows which extensions it can handle: it tags images with `fileType: 1` and treats everything else as a PDF. That list is now a module level constant, and the dispatch branch gates on `['pdf'] + images`, the same way `mistral_ocr`, `datalab_marker`, `document_intelligence` and `mineru` already limit themselves. Deriving the gate from the loader's own list keeps the two in sync, so a file can never be admitted by the gate and then mislabelled as a PDF on the wire. Everything outside that set falls through to the default loader chain, so `.md` and `.txt` load as text, `.csv` through `CSVLoader`, `.docx` through `Docx2txtLoader`, and so on.
The branch also never checked `PADDLEOCR_VL_BASE_URL`. With the URL cleared, `PaddleOCRVLLoader` raised `ValueError` from its constructor and the upload failed outright instead of falling back. Both settings are now required for the branch to be taken, matching how the other engines guard their own configuration.
Fixes#24988Fixes#26759
Mirrors the ENABLE_FORWARD_USER_INFO_HEADERS pattern already used by
the audio/TTS and external document loader integrations, so the
Mistral OCR backend can identify the requesting user the same way.
Co-authored-by: andrep <vpham@aut.ac.nz>
_upload_file_async built its multipart body with
`aiohttp.streams.FilePayload(...)`, which no longer exists in aiohttp
(payload classes live in aiohttp.payload, and there is no FilePayload).
The reference sits inside a lazy closure, so import succeeds and only the
async Mistral OCR file-upload path blows up at runtime with
AttributeError on every call.
Mirror the working sync path: open the file in a context manager and let
MultipartWriter.append(file_obj, {...}) build a streaming
BufferedReaderPayload, with the POST issued inside the open() block so
the handle stays valid for the whole upload. Preserves the streaming /
memory-efficiency intent.
Found via the dependency-contract test suite (unit/deps/test_aiohttp.py),
which pins aiohttp.streams.FilePayload as absent.
Loader.load() dispatches to the underlying langchain document loaders
(PyMuPDF, Unstructured, python-docx, Tika, …) which are all
synchronous and CPU/IO-bound. process_file() awaited it directly on
the event loop, so parsing a non-trivial PDF/DOCX would freeze the
entire FastAPI app for the duration of the parse — which is what users
experience as "the server hangs whenever I upload a file."
Add an `aload()` async wrapper on Loader that runs the sync load on a
worker thread via asyncio.to_thread, and update process_file() to
await it. The sync API is preserved so existing callers that already
run inside run_in_threadpool (e.g. save_docs_to_vector_db) are
unaffected.
https://claude.ai/code/session_01JSr4NZSskEUQvoJnavVXh8
Co-authored-by: Claude <noreply@anthropic.com>
Replace bare except clauses with except Exception to follow Python best practices and avoid catching unexpected system exceptions like KeyboardInterrupt and SystemExit.
* Adds document intelligence model configuration
Enables the configuration of the Document Intelligence model to be used by the RAG pipeline.
This allows users to specify the model they want to use for document processing, providing flexibility and control over the extraction process.
* Added Titel to Document Intelligence Model Config
Added Titel to Document Intelligence Model Config
Classic298