### What problem does this PR solve?
`is_english()` in `rag/nlp/__init__.py` compiles a **single-character**
regex class and `fullmatch`es it against each item:
```python
pattern = re.compile(r"[`a-zA-Z0-9\s.,':;/\"?<>!\(\)\-]") # no quantifier
...
eng = sum(1 for t in texts if pattern.fullmatch(t.strip()))
```
For a **string** argument the text is first split into single characters
(`texts = list(texts)`), so each `fullmatch` sees one character and
works. But for a **list** argument each item is a whole multi-character
string, and `fullmatch` of a one-character pattern against a
multi-character string always fails — so `is_english()` returns `False`
for **any** list, regardless of content.
```python
is_english("This is English") # True (ok)
is_english(["The quick brown fox jumps.", "Hello world."]) # False (bug — should be True)
is_english(["这是中文。"]) # False (right answer, wrong reason)
```
Many call sites pass lists and were therefore silently always-`False`,
e.g.:
- `rag/llm/chat_model.py:1088`, `rag/llm/cv_model.py:168,1155` —
`is_english([ans])` when an answer is truncated at `max_tokens`, so an
English reply gets the Chinese "······由于长度的原因，回答被截断了，要继续吗？" continuation
suffix instead of the English one.
- `rag/app/book.py` — `remove_contents_table(...,
eng=is_english([...sections...]))`, so English books have their contents
table stripped in Chinese mode.
- `common/doc_store/es_conn_base.py:339`,
`rag/utils/opensearch_conn.py:733` — `is_english(txt.split())` in
highlight handling.
- plus `rag/app/qa.py`, `rag/flow/parser/utils.py`,
`common/doc_store/infinity_conn_base.py`.
### Fix
Add a `+` quantifier so an all-English multi-character item matches:
```python
pattern = re.compile(r"[`a-zA-Z0-9\s.,':;/\"?<>!\(\)\-]+")
```
The string path is unchanged (single characters still match) and
non-English lists still return `False`. Adds
`test/unit_test/rag/test_is_english.py`; the two list cases fail before
this change and pass after.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Used the Claude CLI while working on this.
### What problem does this PR solve?
Table parser metadata aggregation previously only ran when
`table_column_mode` was set to `manual`. In auto mode (default), all
columns default to `"both"` role, meaning they should also be aggregated
into document-level metadata for UI/chat filters. Additionally, the task
snapshot could be stale — `table_column_names` are written to KB
`parser_config` during `chunk()` but the task may have been created
before that.
Changes:
- Renames `aggregate_table_manual_doc_metadata` →
`aggregate_table_doc_metadata`
- Supports both `"manual"` and `"auto"` `table_column_mode` (defaults to
`"auto"`)
- Reloads `table_column_names` from KB DB when missing from task
snapshot
- Removes the manual-only guard in `task_executor` and refactored
`post_processor`
- Updates all tests with new function name and adds auto mode test cases
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Browser parsed sys.query from prompts but never called set_input_value,
so node_finished inputs displayed null in the agent orchestration run
log.
Additionally, Browser’s tenant-model path could trigger unsupported
structured-output modes (response_format/tool_choice) for some
OpenAI-compatible providers (notably DeepSeek thinking models), causing
step failures.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
### What problem does this PR solve?
`RAGFlowExcelParser.html()` iterates `(len(rows) - 1) // chunk_rows + 1`
times. `rows[0]` is the header, so `len(rows) - 1` is the data-row
count. When that count is an exact multiple of `chunk_rows`, the `+ 1`
over-counts by one: the final iteration's data slice is empty, but the
header row is still appended — producing a chunk that contains only the
table header and no data.
This is reachable via `rag/app/naive.py` (`html4excel`, `chunk_rows=12`)
and `rag/app/one.py`. A sheet with 12/24/36… data rows (or 256/512… with
the default `chunk_rows=256`) produces an extra
`<table><caption>…</caption><tr><th>…</th></tr></table>` chunk. It is
non-empty, so it passes the `if _` filter and gets indexed as a real
(empty) chunk.
| data rows (chunk_rows=12) | before | after |
|---|---|---|
| 12 | 2 chunks (1 header-only) | 1 |
| 24 | 3 chunks (1 header-only) | 2 |
| 13 | 2 (unchanged) | 2 |
### Fix
Iterate `ceil(n_data / chunk_rows)` times instead of `n_data //
chunk_rows + 1`. Adds
`test/unit_test/deepdoc/parser/test_excel_parser.py`; the
header-only-chunk cases fail before this change and pass after.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Used the Claude CLI while working on this.
### What problem does this PR solve?
Refine the stream parsing for `<think>` / `</think>` so MiniMax and
DeepSeek-style chunking both flush in the right order without mixing
think and answer buffers.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fixes the OpenSearch side of #10747: hybrid search drops the keyword
(BM25) leg and
ends up doing plain vector search.
When a search has both a text and a vector leg, `OSConnection.search()`
throws the text
query away:
del q["query"]
q["query"] = {"knn": knn_query}
The text clause only stays on as a filter inside the knn query, so it
narrows the
candidate set but doesn't count towards scoring. So hybrid search on
OpenSearch behaves
like plain vector search, unlike the Elasticsearch backend.
What I changed:
- when both legs are present, send a real hybrid query
`{"hybrid": {"queries": [bm25, {"knn": ...}]}}` and let a
normalization-processor
search pipeline score and combine the two legs
- only the actual filters (kb_id, available_int, ...) go in the knn
filter, not the
text must clause
- create the pipeline on startup if it's missing, so there's no separate
provisioning
step. name and weights can be set under `os:` in service_conf.yaml, or
via
`OS_HYBRID_PIPELINE`; defaults are `ragflow_hybrid_pipeline` and `[0.5,
0.5]`
- normalization-processor needs OpenSearch 2.10+. on older clusters, or
when the
pipeline can't be created, log a warning and fall back to vector-only
instead of
pointing at a pipeline that doesn't exist
This is only the hybrid-search fix; `create_doc_meta_idx` is already on
main.
Testing (there's no OpenSearch path in CI): added a unit test
(`test/unit_test/rag/utils/test_opensearch_hybrid_search.py`, no
services needed) that
checks the query built in each case — hybrid + pipeline param for
text+vector, plain knn
for vector-only, plain bool for text-only, the knn filter never carrying
the text
query_string, and the vector-only fallback when the pipeline isn't
available. Also ran
it against a real OpenSearch 2.19.1 container with a doc that matches
the keyword but
sits outside the knn top-k: pure knn returns `['D1','D2','D5']` (keyword
doc missing),
the hybrid query returns `['A','D1','D2','D5']` (keyword doc present).
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Signed-off-by: Danut Matei <matei.danut.dm@gmail.com>
### What problem does this PR solve?
Closes#15428
The hybrid score in `rag/nlp/search.py` (`rerank_by_model`) blends
reranker similarity with token similarity on a fixed `[0, 1]` scale:
```python
return tkweight * np.array(tksim) + vtweight * vtsim + rank_fea # tkweight=0.3, vtweight=0.7
```
The reranker implementations did not agree on that scale. Only three of
roughly 17 providers normalized their output, and `NvidiaRerank`
returned raw, unbounded logits. Weighted at `0.7`, a negative logit
could push a genuinely relevant chunk below pure keyword matches, and
its magnitude swamped `tksim`, which lives in `[0, 1]`. The practical
effect was that the same query produced differently scaled scores
depending on the configured reranker, and logit based providers degraded
retrieval quality instead of improving it.
This PR enforces a single scoring contract in one place:
- `Base.similarity` is now the only public entry point. It
short-circuits empty input and guarantees a normalized result. Each
provider implements its raw scoring in `_compute_rank`, which removes
sixteen duplicated empty input guards and the three scattered
normalization calls.
- Normalization is range aware. Providers that already return calibrated
`[0, 1]` relevance scores (Cohere, Jina, Voyage, and others) keep their
absolute magnitudes, so `similarity_threshold` filtering and the
reported `vector_similarity` stay meaningful. Only out-of-range output
such as NVIDIA logits is min-max rescaled into `[0, 1]`.
- The twelve leftover `[DEBUG ...]` prints in `rerank_by_model`,
introduced in #14231, are removed. They ran on every retrieval, added
per chunk overhead, and leaked queries, keywords, and document content
to stdout and logs.
A new regression suite in
`test/unit_test/rag/llm/test_rerank_normalization.py` covers logit
rescaling (positive, negative, and flat batches), preservation of
already calibrated scores, ordering, empty input handling, and the per
provider HTTP path. It also asserts that no provider overrides
`similarity()`, so the contract cannot silently drift.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Closes#15433
Reranked retrieval drops results and returns short pages once pagination
crosses the first candidate block, for the common page sizes 10 and 30.
In `rag/nlp/search.py`, the candidate window (`RERANK_LIMIT`) is rounded
up to a multiple of `page_size` to keep block based pagination aligned,
and then clamped back to 64:
```python
RERANK_LIMIT = math.ceil(64 / page_size) * page_size if page_size > 1 else 1 # e.g. 70 for page_size=10
RERANK_LIMIT = max(30, RERANK_LIMIT)
if rerank_mdl and top > 0:
RERANK_LIMIT = min(RERANK_LIMIT, top, 64) # clamps back to 64, breaking the multiple
```
`RERANK_LIMIT` is used both as the backend block size (`page =
global_offset // RERANK_LIMIT`) and as the modulus that slices a page
out of a reranked block (`begin = global_offset % RERANK_LIMIT`). When
it stops being a multiple of `page_size`, the block that gets fetched
and the slice taken from it no longer agree. With `page_size=10` and
`top=1024`, page 7 returns only 4 of 10 results and the head of the next
block is never shown on any page. This happens whenever the result set
spans more than one block, which is the default.
**Fix**
The window math is moved into a small reusable helper,
`Dealer._rerank_window`, which:
- targets a pool of about 64 candidates,
- bounds it by `top` when a reranker is active, and
- always rounds to a whole number of pages, so the window stays an exact
multiple of `page_size`.
The call site becomes a single line, and the alignment invariant now
lives in one documented place. Behavior is unchanged on every path that
was already aligned (the non reranked path and any `top` that already
produced a page multiple).
**Verification**
A simulation of the full retrieval path (per block rerank, similarity
threshold filter, and the exact `page // window` and `offset % window`
math) confirms the fix loses nothing where the old code lost real
results:
```
ps=10 top=1024: new window=70 dropped_valid=0 | old window=64 dropped_valid=16
ps=30 top=1024: new window=90 dropped_valid=0 | old window=64 dropped_valid=66
```
New unit tests in `test/unit_test/rag/test_search_pagination.py` cover
the alignment invariant, cross block pagination (every candidate
surfaced once, in order, no gaps, no short interior pages), the reported
regression, and parity with the old window on the previously correct
paths. All 114 cases pass and `ruff check` is clean.
Fixes the reranked deep pagination data loss described above.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Prepend a leading slash and reject `..` segments so scoped OneDrive
delta queries use `root:/path:/delta` instead of `root:path:/delta`.
Fixes#15500
### What problem does this PR solve?
The OneDrive connector builds Microsoft Graph delta URLs from optional
`config.folder_path`. When users enter a path without a leading slash
(e.g. `Documents/Reports` instead of `/Documents/Reports`), the
connector produces a malformed URL such as
`root:Documents/Reports:/delta`. Per [Microsoft Graph path-based
addressing](https://learn.microsoft.com/en-us/graph/onedrive-addressing-driveitems),
the segment after `root:` must start with `/` (e.g.
`root:/Documents/Reports:/delta`). Sync and validation then fail or
return no documents, which is hard to diagnose from the UI because the
optional folder field does not enforce the format.
This PR normalizes `folder_path` at connector construction time (prepend
`/`, trim whitespace and trailing slashes) and rejects `..` segments
before any Graph request is made.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## What
- make `Switch` ignore conditions that have no evaluable items
- add a regression for blank `cpn_id` items falling through to the else
branch
- keep the existing non-empty `and` condition behavior covered
Fixes#15643.
## Verified
- `python -m py_compile agent\component\switch.py
test\unit_test\agent\component\test_switch.py`
- `python -m pytest test\unit_test\agent\component\test_switch.py -q` ->
`2 passed`
- `python -m ruff check agent\component\switch.py
test\unit_test\agent\component\test_switch.py`
- `git diff --check`
I also checked `python -m ruff format --check` on the touched files. It
would reformat pre-existing style in `agent/component/switch.py` beyond
this bug fix, so I kept the patch scoped instead of reformatting the
whole file.
### What problem does this PR solve?
Markdown extraction can split tables row by row when delimiter-based
extraction uses a newline delimiter. That loses table structure during
chunking even though delimiters should still split normally outside
tables.
This PR keeps the follow-up to #15482 intentionally narrow:
- preserve Markdown pipe tables during delimiter-based extraction
- preserve borderless pipe tables during delimiter-based extraction
- preserve multiline HTML tables during delimiter-based extraction
- keep delimiter splitting unchanged outside protected table ranges
Refs #15482
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Testing
- `ruff check deepdoc/parser/markdown_parser.py
test/unit_test/deepdoc/parser/test_markdown_parser.py`
- `python3 run_tests.py -t
test/unit_test/deepdoc/parser/test_markdown_parser.py`
- `git diff --check`
## Summary
- Infer `Content-Type` from the stored document filename on SDK download
routes.
- Covers `GET /api/v1/datasets/<dataset_id>/documents/<document_id>` and
`GET /api/v1/documents/<document_id>`.
- Aligns with REST preview/download via `CONTENT_TYPE_MAP`.
## Test plan
- [x] `pytest
test/testcases/test_http_api/test_file_management_within_dataset/test_doc_sdk_routes_unit.py::TestDocRoutesUnit::test_download_mimetype_from_filename`
- [x] Manual: `curl -sSI` on SDK dataset document download for a PDF;
expect `Content-Type: application/pdf`
Fixes#15112.
## Summary
- keep the native Docling chunking path when it returns usable chunks
- fall back to the standard Docling response parser when a chunked
request gets HTTP 200 but returns no usable chunks
- add a regression test for older Docling servers that accept the
chunking request but return a standard conversion payload
## Why
Older external Docling servers can accept a request containing
`do_chunking: true` and still return the standard conversion response
shape. The current code treats any HTTP 200 from the chunked request as
a native chunk response, finds no chunk entries, and returns zero
sections without trying the standard response parser.
Fixes#15569.
## Validation
- `python -m pytest
test\\unit_test\\deepdoc\\parser\\test_docling_parser_remote.py -q`
- `python -m py_compile deepdoc\\parser\\docling_parser.py
test\\unit_test\\deepdoc\\parser\\test_docling_parser_remote.py`
- `python -m ruff check deepdoc\\parser\\docling_parser.py
test\\unit_test\\deepdoc\\parser\\test_docling_parser_remote.py`
- `git diff --check`
### What problem does this PR solve?
Markdown extraction currently applies custom delimiters before
respecting fenced code blocks. When a delimiter such as a newline is
configured, fenced code can be split into separate chunks, and longer
outer fences can be closed incorrectly by shorter nested fences.
This PR keeps the fix intentionally narrow for the Markdown chunking
discussion in #15482:
- preserve fenced code blocks when delimiter-based extraction is used
- support both backtick and tilde fences
- respect fence length so longer outer fences can contain shorter inner
fences
- keep delimiter splitting unchanged outside fenced blocks
Refs #15482
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Testing
- `ruff check deepdoc/parser/markdown_parser.py
test/unit_test/deepdoc/parser/test_markdown_parser.py`
- `python3 run_tests.py -t
test/unit_test/deepdoc/parser/test_markdown_parser.py`
### What problem does this PR solve?
remove duplicate document preview access check
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix:
- Verify provider with empty llm list in llm_factories.json
- Set search bot's chat_llm_name, use tenant default chat model as
default
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Summary
Restore the `DocumentService.accessible(doc_id, current_user.id)` check
that PR #15146 dropped from the REST document preview handler. Any
authenticated caller could download any tenant's document bytes by
guessing/knowing the `doc_id`.
## Root cause
`api/apps/restful_apis/document_api.py` — the `GET
/documents/<doc_id>/preview` handler called `DocumentService.get_by_id`
and went straight to `File2DocumentService.get_storage_address` +
`STORAGE_IMPL.get`, with no tenant check between the lookup and the
read. The handler's docstring even promises "user must belong to the
tenant that owns the document's knowledge base" — the code didn't
enforce it.
## Fix
- Add `current_user` to the existing `api.apps` import.
- Immediately after `get_by_id`, call
`DocumentService.accessible(doc_id, current_user.id)`; on denial, return
the **same** `get_data_error_result(message="Document not found!")`
shape used for the missing-doc branch. That makes a cross-tenant probe
indistinguishable from a missing-doc probe, preventing ID enumeration
(the issue body calls this out explicitly).
- Emit `logging.warning` with caller user + doc_id for audit.
- Restores symmetry with peer routes that already call
`accessible(doc_id, user_id)` (e.g. `_run_sync` at
`document_api.py:1380`).
## Test plan
Adds
`test/unit_test/api/apps/restful_apis/test_document_preview_accessible.py`:
- **`test_cross_tenant_preview_is_denied`** — owner tenant ≠ caller
tenant; asserts the response shape is `Document not found!` and the
storage backend (`thread_pool_exec(STORAGE_IMPL.get, ...)`) is **never**
invoked.
- **`test_missing_doc_returns_not_found`** — missing-doc behaviour
unchanged.
Stub-loader pattern mirrors
`test/unit_test/api/apps/sdk/test_dify_retrieval.py` (added in #15028,
passing in CI).
## Provenance — how this fix was produced
This PR was authored against a small cited knowledge base committed in
the working tree as a `.vouch/` (see
[vouchdev/vouch](https://github.com/vouchdev/vouch)). The loop used
here:
1. **Grounding first.** Before reading the handler, queried the KB for
prior context: `vouch context "tenant scoped accessible authorization"`
→ retrieved a cited claim distilled from PR #15028 (which restored the
same `accessible()` check on `/dify/retrieval`). The retrieved rule:
> *ragflow REST endpoints that load by tenant-scoped id must call
`<Service>.accessible(id, tenant_id)` after `get_by_id` and before
storage/DB read; deny with code 109 'No authorization.' and log a
warning. Established by PR #15028.*
2. **Applied the pattern with a domain refinement.** For an API/JSON
endpoint, `No authorization.` is the right denial shape. For a
**byte-streaming, browser-facing** endpoint like `/preview`, leaking
*existence* itself enables enumeration — so per the issue's expected
behaviour, this PR denies with `Document not found!` (indistinguishable
from missing) instead. Same auth check, narrower response.
3. **Recorded the refinement back into the KB** as a new cited claim, so
the next IDOR-class issue starts already grounded in both the general
pattern and the byte-route nuance.
Net effect of the workflow: the fix replicates a known-good pattern
instead of reinventing it, *and* the place where the pattern was nuanced
is now retrievable for the next pass. Mechanism is fully independent of
this PR — it's not a runtime dependency, just process discipline.
Closes#15501
## Summary
Fixes#15534 — `update_document_name_only()` crashes with
`AttributeError` when `File2Document` exists but the linked `File` row
was deleted.
`update_document_name_only()` in `document_api_service.py` called
`FileService.get_by_id()` when a `File2Document` row existed, then
accessed `file.id` without checking the lookup result. An orphan
`File2Document` link (file deleted, mapping left behind) caused document
rename via `PATCH /api/v1/datasets/{dataset_id}/documents/{document_id}`
to return HTTP 500.
This PR mirrors guards used in `file2document_api.py` and
`file_api_service.py`: skip the optional file rename when the file is
missing, and still update the document record and search index.
## Changes
- `api/apps/services/document_api_service.py` — check `e and file`
before `FileService.update_by_id`
- `test/unit_test/api/apps/services/test_update_document_name_only.py` —
regression tests (orphan link + happy path)
## Test plan
- [x] `pytest
test/unit_test/api/apps/services/test_update_document_name_only.py -v`
- [ ] Manual: PATCH document `name` when `File2Document` points to a
non-existent `file_id` → 200, document/index renamed, no 500
### What problem does this PR solve?
Closes#15388.
Chat completion routes did not reliably honor per-request generation
settings:
- `/api/v1/chat/completions` copied generation settings with a
truthiness check, so valid zero values such as `temperature: 0`, `top_p:
0`, `frequency_penalty: 0`, `presence_penalty: 0`, and `max_tokens: 0`
were dropped.
- `/api/v1/openai/{chat_id}/chat/completions` did not forward standard
generation settings into the request-specific dialog LLM settings before
calling `async_chat`.
This PR preserves explicitly supplied generation parameters, including
zero values, and merges request-level overrides into existing dialog
settings where appropriate.
The supported generation parameter keys and merge behavior live in a
shared REST API helper to keep both completion routes aligned.
Validation:
- `git diff --check`
- `python3 -m py_compile api/apps/restful_apis/_generation_params.py
api/apps/restful_apis/chat_api.py api/apps/restful_apis/openai_api.py
test/testcases/test_http_api/test_session_management/test_session_sdk_routes_unit.py`
- `uv run ruff check api/apps/restful_apis/_generation_params.py
api/apps/restful_apis/chat_api.py api/apps/restful_apis/openai_api.py
test/testcases/test_http_api/test_session_management/test_session_sdk_routes_unit.py`
- `ZHIPU_AI_API_KEY=dummy uv run pytest
test/testcases/test_http_api/test_session_management/test_session_sdk_routes_unit.py
-q -k generation_params`
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fixes#15286.
When calling `/api/v1/openai/<chat_id>/chat/completions` with `"stream":
true`, the response contains the answer **twice** — the final message
repeats everything that was already streamed.
#### Root cause
RAGFlow's `async_chat` streams the body as incremental `delta.content`
chunks, then emits a terminating `final` event whose `answer` is the
**complete** (decorated) message. The handler re-emitted that full
answer as one more `delta.content` chunk:
```python
if ans.get("final"):
if ans.get("answer"):
full_content = ans["answer"]
response["choices"][0]["delta"]["content"] = full_content # <-- whole answer again
yield ...
```
So a client accumulating `delta.content` ends up with the message
duplicated.
#### Fix
Drop the re-emission. The complete answer from the `final` event is now
surfaced **only** through the trailing chunk's `final_content` and
`reference` fields, which matches OpenAI streaming semantics: deltas are
incremental, and the final chunk carries only `finish_reason` / `usage`
(plus RAGFlow's `reference` / `final_content` extensions).
This matches the expected behavior described in the issue: "The stream
should only yield content chunks once, and the final message should only
contain reference, usage, and finish_reason."
#### Testability refactor
The streaming SSE assembly was a closure inside the request handler, so
it could only be exercised against a live server + real LLM. I extracted
it into a module-level `_stream_chat_completion_sse` async generator
(behavior-preserving) so it can be unit-tested with a fake event stream.
#### Tests
Adds
`test/unit_test/api/apps/restful_apis/test_openai_stream_no_duplicate.py`
(same import-stub pattern as the existing `test_get_agent_session.py`):
- body is streamed exactly once (the regression);
- the complete answer is never re-emitted as a content chunk;
- the terminating chunk has `finish_reason="stop"`, `content=None`, and
correct `usage`;
- `final_content` / `reference` are present on the trailing chunk;
- reasoning (`think`) deltas stream separately and are not duplicated.
> Note: this is unrelated to #15442, which only changes the `stream`
default — it does not touch the duplication logic.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Added test cases
---------
Co-authored-by: Wang Qi <wangq8@outlook.com>
### What problem does this PR solve?
Fixes#15115.
`GET /api/v1/documents/images/<image_id>` returned **Image not found**
when the thumbnail storage object key contained hyphens (e.g.
`page-1.png`). Document APIs build URLs as `{dataset_id}-{thumbnail}`,
but `get_document_image()` used `image_id.split("-")` and required
exactly two segments, so keys like `<kb_id>-page-1.png` were rejected
even though the blob existed.
This PR splits only on the first hyphen (`split("-", 1)`) and sets
`Content-Type` from the object key extension via `CONTENT_TYPE_MAP`
instead of hardcoding `image/JPEG`.
### What problem does this PR solve?
Fixes#15117.
Chunk images are stored with `img_id = f"{bucket}-{objname}"` in
`image2id()` (`rag/utils/base64_image.py`). When loading via
`id2image()`, the code used `image_id.split("-")` and required exactly
two segments. Object keys that contain hyphens (e.g. `page-1.jpg`)
produce more than two segments, so `id2image` returns `None` and chunk
image previews fail even though the blob exists.
This is the same parsing issue as #15115 (HTTP thumbnail route); this PR
fixes the indexing/retrieval path.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
### Test plan
- [x] `pytest test/unit_test/rag/utils/test_base64_image.py`
- [ ] Manual: index a chunk with an `objname` containing hyphens and
confirm `img_id` resolves to an image in retrieval
Fixes#15117.
### Related issues
Closes#15358
<!-- After filing upstream, replace XXXX with your issue number. -->
---
### What problem does this PR solve?
`POST /api/v1/openai/<chat_id>/chat/completions` forwards `messages` to
`async_chat` without normalizing `content`. Downstream, `dialog_service`
assumes string content:
```python
re.sub(r"##\d+\$\$", "", m["content"])
```
OpenAI-compatible clients may send `content` as an **array** of parts
(text, `image_url`, etc.), including text-only arrays. That causes
`TypeError` and HTTP **500** instead of a valid response or a clear
**400**.
`openai_api.py` also reads `messages[-1]["content"]` directly for
`prompt` without handling list-shaped content.
This PR normalizes array `content` to a string (concatenating `type:
text` parts) before calling `async_chat`, matching a minimal
OpenAI-compat path. Image parts can be documented as unsupported or
handled in a follow-up if vision integration is required.
## Summary
Fixes#15245 — `POST /api/v1/chat/completions` with `stream=true`
intermittently returns 500:
```
data:{"code": 500, "message": "failed to encode response: json:
unsupported value: NaN (status code: 500)", "data": {...}}
```
…even though "the same question" works on retry.
## Root cause
The streaming path serialized the answer with bare `json.dumps(...)`
(`api/apps/restful_apis/chat_api.py:1221`). `json.dumps` defaults to
`allow_nan=True` and emits the literal token `NaN` for NaN /
Infinity float values. That is valid Python-flavored JSON but
**invalid per RFC 8259**, so downstream consumers reject it. The
reporter's gateway is Go-based and the error wording
(`failed to encode response: json: unsupported value: NaN`) is
straight from Go's `encoding/json`.
How NaN gets into the payload: retrieval scoring in
`rag/nlp/search.py` runs `np.mean(...)` over aggregations that can
be empty, and similarity denominators can be zero. Reference chunk
fields like `similarity`, `vector_similarity`, `term_similarity`
can therefore be NaN depending on which chunks a given query
retrieves — which is exactly why the failure is intermittent for
the same question.
The non-streaming branch (`get_json_result(data=answer)`,
`chat_api.py:1243`) has the same vulnerability — Quart's `jsonify`
also defaults to `allow_nan=True` and the same retrieval pipeline
feeds both branches.
`agent/tools/exesql.py:88-102` already has the same NaN/Inf guard
for SQL results. This PR brings the chat completions path up to
parity.
## Fix
Add a small `_sanitize_json_floats(obj)` helper near the top of
`api/apps/restful_apis/chat_api.py`. It walks `dict` / `list` /
`tuple` and replaces any `float` that is `NaN` or `±Infinity` with
`None`. Apply it at the two serialization boundaries:
- **Streaming branch** (`stream()`): sanitize the SSE payload before
`json.dumps`.
- **Non-streaming branch**: sanitize the `answer` dict before
`get_json_result(data=...)`.
The terminal `data:True` frame and the `code:500` error frame carry
no scores and are left untouched.
Added `import math` to the existing alphabetical import block.
No change to retrieval logic — replacing NaN with `null` at the
serialization boundary is conservative: clients still parse the
JSON, a missing-score chunk is a strictly better failure mode than
a 500 that kills the whole reply.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fixes#15427.
All LiteLLM-routed chats fail with:
- Anthropic: `litellm.BadRequestError: AnthropicException -
{"type":"invalid_request_error","message":"model_type: Extra inputs are
not permitted"}`
- OpenAI: `litellm.BadRequestError: OpenAIException - Unknown parameter:
'model_type'`
This is a regression from v0.25.4.
#### Root cause
A chat assistant's `llm_setting` is forwarded to the model as
`gen_conf`. `llm_setting` can legitimately carry RAGFlow-internal
metadata such as `model_type` (the chat REST APIs in
`api/apps/restful_apis/` read it back out of `llm_setting`), so that key
ends up inside `gen_conf`.
`Base._clean_conf` (OpenAI-compatible providers) already **whitelists**
the keys it forwards, so direct-OpenAI providers were unaffected.
`LiteLLMBase._clean_conf` only dropped `max_tokens` and passed
everything else straight through to `litellm.acompletion`, which
forwarded `model_type` to the upstream provider — and Anthropic / OpenAI
reject it. Because both Claude and GPT route through LiteLLM, every chat
broke.
#### Fix
- Extract the allowed-key set into a shared `ALLOWED_GEN_CONF_KEYS`
constant and reuse it in `Base._clean_conf`.
- Apply the same whitelist in `LiteLLMBase._clean_conf`, plus the
LiteLLM-specific reasoning params (`thinking`, `reasoning_effort`,
`extra_body`) that the model-family policies inject for reasoning
models.
This covers all four LiteLLM completion paths (`async_chat`,
`async_chat_streamly`, `async_chat_with_tools`,
`async_chat_streamly_with_tools`), since they all route through
`_clean_conf`.
#### Tests
Adds `test/unit_test/rag/llm/test_clean_conf_whitelist.py` covering both
backends: `model_type` (and other stray keys) are dropped, genuine
generation params and `thinking` survive, `max_tokens` is removed, and
the whitelist invariants hold.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Added test cases
## Summary
- Skip MinerU `header`, `footer`, and `page_number` blocks when
converting `content_list.json` into sections.
- Ignore unsupported block types explicitly so future MinerU output
types cannot re-emit the previous text block.
Fixes duplicate text in General/naive chunks when parsing PDFs via
MinerU (reported with repeated page headers and body text in slices).
Closes#15335
## Test plan
- [x] `pytest test/unit_test/deepdoc/parser/test_mineru_parser.py -v`
(4/4 passed)
### What problem does this PR solve?
extend restful api suite
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Other (please describe): test
### What problem does this PR solve?
Closes#15332.
RAGFlow can index Gmail and generic IMAP mailboxes but had no native
connector for Outlook / Microsoft 365 mail. Organisations on Microsoft
365 had no way to bring mailbox content into a knowledge base through
Microsoft Graph.
This PR adds a net-new Outlook data source that:
- Authenticates against Microsoft Graph with the same MSAL
client-credentials flow already used by the SharePoint and Teams
connectors (no new auth primitives).
- Pages over `/users/{id}/mailFolders/{folder}/messages/delta` per
mailbox and persists `@odata.deltaLink` values in
`OutlookCheckpoint.delta_links`, so incremental syncs only fetch changed
messages.
- Supports two scoping modes:
- **Tenant-wide** (default): enumerates every user in the tenant via
`/users` and syncs each mailbox. Requires `User.Read.All`.
- **Targeted**: when `user_ids` is provided (comma-separated UPNs or
object IDs), only those mailboxes are synced. `User.Read.All` is not
needed in this mode.
- Lets the caller pick the mail folder (`inbox`, `sentitems`, `archive`,
...). Defaults to `inbox`.
- Maps each message to a `Document` shaped after the Gmail connector:
one `TextSection` carrying `From/To/Cc/Subject` headers + body, with
HTML bodies stripped to text inline (no extra dependency).
- Surfaces typed errors on the validation probe:
401 → `ConnectorMissingCredentialError`, 403 →
`InsufficientPermissionsError` (with `Mail.Read` / `User.Read.All`
hint), 404 on a configured mailbox → `ConnectorValidationError`, 5xx →
`UnexpectedValidationError`.
- Skips messages flagged `@removed` by the delta semantics and messages
whose `receivedDateTime` is older than `poll_range_start`.
#### Files
| File | Change |
|------|--------|
| `common/data_source/outlook_connector.py` | **New** —
`OutlookConnector` (`CheckpointedConnectorWithPermSync` +
`SlimConnectorWithPermSync`) + `OutlookCheckpoint` + tiny `_strip_html`
helper. |
| `common/data_source/config.py` | `DocumentSource.OUTLOOK = "outlook"`.
|
| `common/constants.py` | `FileSource.OUTLOOK = "outlook"`. |
| `common/data_source/__init__.py` | Export `OutlookConnector`. |
| `rag/svr/sync_data_source.py` | `Outlook(SyncBase)` with `batch_size`
normalisation, CSV/list parsing of `user_ids`; registered in
`func_factory`. |
| `web/src/pages/user-setting/data-source/constant/index.tsx` |
`DataSourceKey.OUTLOOK`, visibility map (`syncDeletedFiles: true`), info
entry, form fields (tenant_id, client_id, client_secret, folder,
user_ids, batch_size), default values. |
| `web/src/locales/en.ts`, `web/src/locales/zh.ts` |
`outlookDescription` + 5 tooltip keys (EN + ZH). |
| `test/unit_test/data_source/test_outlook_connector_unit.py` | **New**
— 19 unit tests (`p1`/`p2`/`p3`) covering auth, validation (tenant-wide
vs specific user vs error paths), checkpoint helpers, user enumeration
pagination, message filtering, HTML body stripping. |
#### Required Azure AD permissions
- `Mail.Read` (Application, admin-granted) — always.
- `User.Read.All` (Application, admin-granted) — only when `user_ids` is
left blank so the connector can enumerate mailboxes.
#### Out of scope
- **Attachment indexing.** The current connector emits message body +
headers; binary attachments are flagged via `metadata.has_attachments`
but not pulled. Adding attachment hydration is straightforward but
scoped out per the issue's "decide whether attachments are indexed in
the first version" note.
- **Delegated (per-user) OAuth.** The connector uses app-only
credentials, consistent with the SharePoint / Teams precedent in this
codebase.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Closes#15180.
`OIDCClient.parse_id_token` in `api/apps/auth/oidc.py` read the JWT
signing
algorithm from the **unverified** JWT header and passed it through to
`jwt.decode(..., algorithms=[alg], ...)` as the trust anchor. This is
the
textbook JWT algorithm-confusion vulnerability (CWE-345 / CWE-347). Any
unauthenticated client capable of reaching the OIDC callback could take
over
an arbitrary account on any RAGFlow deployment with OIDC login enabled:
1. **`alg: "none"`** — present a JWT with `{"alg": "none"}` and no
signature segment → `jwt.decode(..., algorithms=["none"])` → PyJWT's
`NoneAlgorithm` accepts the token without verification → login as any
user.
2. **RSA / HMAC confusion** — fetch the public RSA key from the
provider's
JWKS (it's public), forge a JWT with `{"alg": "HS256"}` HMAC-signed
using the public-key bytes as the secret → `jwt.decode(...,
algorithms=["HS256"], key=public_key)` → verifier accepts → login as
any user. (Modern PyJWT independently refuses to use a PEM-formatted
key as an HMAC secret, which mitigates this leg for PEM key formats;
the fix here is the only mitigation for raw / DER / JWK octet keys and
for older PyJWT versions.)
### What changed
**`api/apps/auth/oidc.py`:**
- New module constants `_ALLOWED_OIDC_SIGNING_ALGS` (asymmetric-only:
`RS*`, `ES*`, `PS*`, `EdDSA` — explicitly excludes `none` and `HS*`)
and `_DEFAULT_OIDC_SIGNING_ALGS = ("RS256",)` (the OIDC Core 1.0 §2
spec default).
- New helper `_resolve_id_token_signing_algs(metadata)` — intersects the
provider's advertised `id_token_signing_alg_values_supported` from
`/.well-known/openid-configuration` with the safe allowlist; falls back
to RS256 when the field is missing or contains only unsafe values.
- `OIDCClient.__init__` now stores the resolved allowlist on
`self.id_token_signing_algs` — pinned once, from a trusted source, at
construction time.
- `parse_id_token` no longer calls `jwt.get_unverified_header` and no
longer reads `alg` from the JWT header. It passes
`self.id_token_signing_algs` to `jwt.decode(..., algorithms=...)`.
`PyJWKClient.get_signing_key_from_jwt` still reads the `kid` from the
header internally for JWKS lookup — that's fine, `kid` is not a
security decision; the signature still proves which key was actually
used.
**`test/testcases/test_web_api/test_auth_app/test_oidc_client_unit.py`:**
- Existing `test_parse_id_token_success_and_error` drops its
`jwt.get_unverified_header` mock (no longer called by `parse_id_token`).
- `_metadata` and `_make_client` helpers grew an optional `signing_algs`
parameter so tests can configure what the discovery document advertises.
- New `TestSSRFValidation` / algorithm-confusion regression block (7
tests):
- `test_id_token_signing_algs_default_to_rs256_when_metadata_missing`
- `test_id_token_signing_algs_intersect_metadata_with_safe_allowlist`
- `test_id_token_signing_algs_fall_back_when_only_unsafe_advertised`
- `test_id_token_signing_algs_ignores_non_string_entries`
- `test_id_token_signing_algs_handles_non_list_metadata_field`
- `test_parse_id_token_passes_pinned_algorithms_to_jwt_decode` —
sabotages `jwt.get_unverified_header` to raise on call, proving the
verification path never consults the unverified header.
- `test_parse_id_token_rejects_alg_none` — uses real PyJWT to encode an
`alg: "none"` token; `parse_id_token` raises `ValueError("Error
parsing ID Token: …")` instead of accepting it.
- `test_parse_id_token_rejects_hs256_when_allowlist_is_asymmetric` —
uses real PyJWT to forge an `alg: "HS256"` token with a non-PEM
shared secret (so PyJWT's incidental PEM-as-HMAC refusal isn't what
blocks it); `parse_id_token` raises because `HS256` is not in the
pinned allowlist.
Sanity-checked end-to-end with real PyJWT outside the project test
runner:
- `alg=none` forged token + `algorithms=["RS256"]` →
`InvalidAlgorithmError` ✓
- `alg=HS256` forged token + `algorithms=["RS256"]` →
`InvalidAlgorithmError` ✓
- Same `alg=HS256` token + `algorithms=["HS256"]` → **accepted**
({'sub': 'admin'})
— confirming the attack path was real before the fix.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
Co-authored-by: galuis116 <contact@duerrimports.com>
## Summary
- Fix `meta_filter()` AND logic so an empty result from an early
condition is not overwritten when a later condition matches.
- Add regression tests for empty-first AND, successful AND intersection,
and OR behavior after an empty first condition.
Fixes incorrect `/retrieval` metadata filtering when multiple AND
conditions are used and the first condition matches no documents.
Closes#15360
## Test plan
- [x] `pytest test/unit_test/common/test_metadata_filter_operators.py
-v` (19/19 passed)
### What problem does this PR solve?
Closes#15330.
RAGFlow had no connector for OneDrive / OneDrive for Business. Users who
store working documents in OneDrive could not index them into a
knowledge base without manually downloading and re-uploading files.
This PR adds a net-new OneDrive data source that:
- Authenticates against Microsoft Graph with the same MSAL
client-credentials flow already used by the SharePoint and Teams
connectors (no new auth primitives).
- Enumerates every drive visible to the service principal and pages
through `/drives/{id}/root/delta`, persisting `@odata.deltaLink` values
per drive so subsequent syncs only fetch changed items.
- Optionally narrows ingestion to a sub-folder (`folder_path`) without
needing a separate code path.
- Surfaces typed errors on the validation probe (`GET /drives?$top=1`):
401 → `ConnectorMissingCredentialError`, 403 →
`InsufficientPermissionsError` (with a `Files.Read.All` hint), 5xx →
`UnexpectedValidationError`.
- Filters folders, soft-deleted items, and unsupported extensions (`.pdf
.docx .doc .xlsx .xls .pptx .ppt .txt .md .csv`).
#### Files
| File | Change |
|------|--------|
| `common/data_source/onedrive_connector.py` | **New** —
`OneDriveConnector` + `OneDriveCheckpoint`. |
| `common/data_source/config.py` | `DocumentSource.ONEDRIVE =
"onedrive"`. |
| `common/constants.py` | `FileSource.ONEDRIVE = "onedrive"`. |
| `common/data_source/__init__.py` | Export `OneDriveConnector`. |
| `rag/svr/sync_data_source.py` | `OneDrive(SyncBase)` with `batch_size`
normalisation; registered in `func_factory`. |
| `web/src/pages/user-setting/data-source/constant/index.tsx` |
`DataSourceKey.ONEDRIVE`, visibility map (`syncDeletedFiles: true`),
info entry, form fields (tenant_id, client_id, client_secret,
folder_path, batch_size), default values. |
| `web/src/locales/en.ts`, `web/src/locales/zh.ts` |
`onedriveDescription` + 4 tooltip keys (EN + ZH). |
| `test/unit_test/data_source/test_onedrive_connector_unit.py` | **New**
— 13 unit tests (`p1`/`p2`) covering auth, validation, checkpoint
helpers, and document filtering. |
#### Required Azure AD permission
`Files.Read.All` (Application, admin-granted).
#### Out of scope
- Interactive end-user OAuth (delegated permissions) — the connector
uses app-only credentials, consistent with the SharePoint / Teams
precedent.
- Binary download of file contents — the sync layer emits `Document`s
carrying `webUrl` + metadata; bytes are hydrated downstream by the parse
pipeline.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
default OpenAI chat completions to non-stream when `stream` is omitted
https://github.com/infiniflow/ragflow/issues/15356
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### 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?
Closes#15191.
RAGFlow shipped a Microsoft Teams connector stub
(`common/data_source/teams_connector.py`) whose document-loading methods
all returned `[]`, `Teams._generate()` was a `pass`, and Teams was
commented out of the data-source settings UI. As a result there was no
way to index Teams channel conversations into a knowledge base.
This PR implements the connector end to end on top of Microsoft Graph
(Office365-REST-Python-Client). It shares the MSAL client-credentials
auth shape with the SharePoint connector.
**Backend**
- `common/data_source/teams_connector.py`
- `load_credentials()` now builds the Graph client using an MSAL
client-credentials **token callback** — the form `GraphClient` actually
expects. (The previous stub passed a raw access-token string to
`GraphClient(...)`, which is not how that client is driven.) Token
acquisition is lazy, so credential loading performs no network call.
- `validate_connector_settings()` lists teams via Graph.
- `load_from_checkpoint()` is now a generator that pages teams →
channels → messages, flattens each top-level post together with its
replies into one blob-based `Document` (`extension` `.txt`/`.html`,
`blob`, `size_bytes`, `doc_updated_at`). Incremental syncs are bounded
by message `lastModifiedDateTime` (falling back to `createdDateTime`).
Per-message errors surface as `ConnectorFailure` instead of aborting the
run.
- `retrieve_all_slim_docs_perm_sync()` yields id-only `SlimDocument`
batches and the checkpoint helpers return proper `TeamsCheckpoint`s.
- ACL → `ExternalAccess` mapping is intentionally left best-effort
(`load_from_checkpoint_with_perm_sync` delegates to the standard load)
because the sync pipeline does not currently persist `ExternalAccess`.
- `rag/svr/sync_data_source.py`
- Implemented `Teams._generate()` using the existing
`CheckpointOutputWrapper` pattern (same shape as Confluence/Jira/Google
Drive), supporting full reindex and incremental polling from
`poll_range_start`.
- `TeamsConnector` is already exported from
`common/data_source/__init__.py`.
**Frontend (`web/`)**
- Enabled the `TEAMS` data-source enum and added its form fields
(`tenant_id`, `client_id`, `client_secret`), default values, display
metadata, and a Teams icon.
- Added `teamsDescription` / `teamsTenantIdTip` to `en.ts` and `zh.ts`.
**Tests**
- `test/unit_test/data_source/test_teams_connector_unit.py`: mock-based
unit tests covering credential loading (incomplete creds raise, happy
path sets the Graph client, fetch-without-creds raises), post/reply
flattening (incl. the HTML vs text extension), incremental
`lastModifiedDateTime` filtering, and slim-doc listing. All 6 pass;
`ruff check` is clean.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Closes#15187.
RAGFlow shipped a Slack connector
(`common/data_source/slack_connector.py`) but it was never usable:
`Slack._generate()` in the sync worker was a `pass` stub, the
connector's document-generating code was incompatible with the current
data model,
and Slack was commented out of the data-source settings UI. As a result,
teams had no way to index Slack channels/threads into a knowledge base.
This PR completes the connector end to end.
**Backend**
- `common/data_source/slack_connector.py`
- Rewrote `thread_to_doc` to produce a blob-based `Document`
(`extension`/`blob`/`size_bytes`). The previous implementation built the
doc with a `sections=[...]` argument and omitted the now-required
`blob`/`extension`/ `size_bytes` fields, so it raised a validation error
against the current `Document` model. Thread messages are now cleaned
and flattened into a single UTF-8 text blob.
- Added `load_from_state()` / `poll_source(start, end)` generators. The
connector's checkpoint interface is a no-op stub, so both full and
incremental syncs run through a single channel-iterating generator built
on the existing module helpers (`get_channels`, `filter_channels`,
`get_channel_messages`, `_process_message`), with per-channel thread
de-duplication.
- `rag/svr/sync_data_source.py`
- Implemented `Slack._generate()`. Credentials are loaded via
`StaticCredentialsProvider` (the connector requires `slack_bot_token`
and does not support `load_credentials`). Supports full reindex and
incremental polling from `poll_range_start`, plus the optional channel
filter. Modeled on the Confluence/Dropbox wrappers.
- `SlackConnector` was already exported from
`common/data_source/__init__.py`.
**Frontend (`web/`)**
- Enabled the `SLACK` data-source enum and added its form fields (Slack
bot token + optional channel filter), default values, display metadata,
and a Slack icon.
- Added `slackDescription` / `slackBotTokenTip` / `slackChannelsTip`
strings to `en.ts` and `zh.ts`.
**Tests**
- `test/unit_test/data_source/test_slack_connector_unit.py`: unit tests
covering credential loading (`load_credentials` raises,
`set_credentials_provider` initializes clients, missing credentials
raises) and document generation (standalone message + flattened thread,
blob/extension/size_bytes/metadata, and the incremental poll time
window). All 5 pass; `ruff check` is clean.
Required Slack scopes: `channels:read`, `channels:history`,
`users:read`.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Closes#15189.
RAGFlow shipped a SharePoint connector stub
(`common/data_source/sharepoint_connector.py`) whose document-loading
methods all returned `[]`, `SharePoint._generate()` was a `pass`, and
SharePoint was commented out of the data-source settings UI. As a result
there was no way to index files stored in SharePoint document libraries.
This PR implements the connector end to end on top of Microsoft Graph
(Office365-REST-Python-Client).
**Backend**
- `common/data_source/sharepoint_connector.py`
- `load_credentials()` now builds the Graph client using an MSAL
client-credentials **token callback** — the form `GraphClient` actually
expects. (The previous stub passed a raw access-token string to
`GraphClient(...)`, which is not how that client is driven.) Token
acquisition is lazy, so credential loading does no network call.
- `validate_connector_settings()` resolves the configured site via
Graph.
- `load_from_checkpoint()` is now a generator that enumerates every
document library under the site, walks folders depth-first, downloads
each file, and yields blob-based `Document` objects (`extension` /
`blob` / `size_bytes` / `doc_updated_at`). Incremental syncs are bounded
by file `lastModifiedDateTime`. Per-file errors are surfaced as
`ConnectorFailure` rather than aborting the run.
- `retrieve_all_slim_docs_perm_sync()` yields id-only `SlimDocument`
batches (no downloads) and the checkpoint helpers return proper
checkpoints.
- ACL → `ExternalAccess` mapping is intentionally left best-effort
(`load_from_checkpoint_with_perm_sync` delegates to the standard load)
because the sync pipeline does not currently persist `ExternalAccess`;
this can be extended once that plumbing exists.
- `rag/svr/sync_data_source.py`
- Implemented `SharePoint._generate()` using the existing
`CheckpointOutputWrapper` pattern (same shape as Confluence/Jira/Google
Drive), supporting full reindex and incremental polling from
`poll_range_start`.
- `SharePointConnector` is already exported from
`common/data_source/__init__.py`.
**Frontend (`web/`)**
- Enabled the `SHAREPOINT` data-source enum and added its form fields
`site_url`, `tenant_id`, `client_id`, `client_secret`), default values,
display metadata, and a SharePoint icon.
- Added `sharepointDescription` / `sharepointSiteUrlTip` to `en.ts` and
`zh.ts`.
**Tests**
- `test/unit_test/data_source/test_sharepoint_connector_unit.py`:
mock-based unit tests covering credential loading (incomplete creds
raise, happy path sets the Graph client, fetch-without-creds raises),
drive traversal + file download, incremental `lastModifiedDateTime`
filtering, and slim-doc listing. All 6 pass; `ruff check` is clean.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
