Replace the RuntimeError with a warning + first-address fallback so a
single email whose From header contains multiple addresses no longer
crashes the entire IMAP sync task. Also add regression tests covering:
- #14963: RFC 5322 quoted display names with commas (e.g. "Schlüter,
Sabine" <s@x>) parsed as one address, not two.
- #14964: multi-address headers warn instead of raising.
Closes#14964
Refs #14963
### What problem does this PR solve?
RuntimeError: Cannot run the event loop while another loop is running
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fix minor code quality issues:
1. Fix typo in assertion error message: "Can't fine" → "Can't find"
2. Remove duplicate line in common/connection_utils.py
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
### What problem does this PR solve?
This PR improves the connector dashboard task management experience and
adds better visibility into connector execution logs.
### Overview:
#### Before
<img width="700" alt="image"
src="https://github.com/user-attachments/assets/e4a8ed6f-2e18-4f0f-8528-41a514550052"
/>
#### Now:
<img width="700" alt="Screenshot from 2026-05-18 16-31-30"
src="https://github.com/user-attachments/assets/d4ca193b-847a-49ae-9e4f-5fbca60ea627"
/>
### 1. Add a new logging page to the connector dashboard
A new logging page has been added so users can view connector task
execution logs directly from the connector dashboard.
### 2. Merge the Resume button into Confirm
The separate **Resume** button has been removed. The **Confirm** button
now represents different actions depending on the current task state:
- **Save**: Save form changes and reschedule tasks.
- **Stop**: Cancel currently scheduled or running tasks.
- **Resume**: Create new scheduled tasks after the previous tasks have
been stopped.
- **Start**: Start tasks when no task has been started yet.
### 3. Separate syncing and pruning tasks
Connector tasks are now separated into **syncing** and **pruning**.
Pruning is controlled by the **Sync deleted files** option:
- When **Sync deleted files** is disabled, only syncing tasks are shown.
- When **Sync deleted files** is enabled, both syncing and pruning tasks
are shown.
**Now: Sync deleted files disabled**
<img width="700" alt="Sync deleted files disabled"
src="https://github.com/user-attachments/assets/dbd9232e-614a-407f-a0b1-c109e5fa567d"
/>
**Now: Sync deleted files enabled**
<img width="700" alt="Sync deleted files enabled"
src="https://github.com/user-attachments/assets/1f527f48-ccb3-4ee8-97ca-086891489296"
/>
### 4. Update logs in backend
<img width="700" alt="image"
src="https://github.com/user-attachments/assets/10a95a3f-98c1-4e67-8afa-ddf6cda5b0b2"
/>
### 5. Remove connector resume API
- Removed: `POST /v1/connectors/<connector_id>/resume`
- Replaced by: `PATCH /v1/connectors/<connector_id>`
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Closes#14858
The `test_db_connection` endpoint in the agent API accepts a
user-supplied `host` and connects to it directly via database drivers
(MySQL/PostgreSQL) without any validation. This allows an attacker to
probe internal network addresses (e.g. `127.0.0.1`, `10.x.x.x`,
link-local, etc.) through the server — a classic Server-Side Request
Forgery (SSRF) vulnerability.
This PR adds an SSRF guard that resolves the host and rejects any
address that is not globally routable before the database connection is
attempted.
**Changes:**
- **`common/ssrf_guard.py`** — Added `assert_host_is_safe()`, a
host-level counterpart of the existing `assert_url_is_safe()`, designed
for non-HTTP protocols (database drivers) where there is no URL to
parse.
- **`api/apps/restful_apis/agent_api.py`** — Call
`assert_host_is_safe(req["host"])` at the top of `test_db_connection` so
that non-public hosts are rejected early with a clear error message.
Fixes#14858
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
## Summary
- Stop pulling chunk vectors (`q_*_vec`) back from Elasticsearch in the
main retrieval path. ES already knows them; shipping them was pure
bandwidth/memory overhead.
- Recover the per-chunk cosine similarity via a second KNN-only ES call
filtered by the candidate chunk ids. The new `_score` is merged with
locally computed term similarity using the user-configured
`vector_similarity_weight`.
- Lazily fetch the chunk embedding only for the chunks
`insert_citations` actually needs.
## Details
**`rag/nlp/search.py`**
- `Dealer.search`: no longer appends `q_*_vec` to the ES select list.
OceanBase still gets it (its rerank path is unchanged).
- New `Dealer._knn_scores(sres, idx_names, kb_ids)`: a `MatchDenseExpr`
over the cached query vector filtered by `id IN sres.ids`, returning
`{chunk_id: cosine_score}` via ES `_score`.
- New `Dealer.rerank_with_knn(...)`: term similarity from
`qryr.token_similarity` plus the ES-supplied KNN score, combined with
`tkweight`/`vtweight` and the existing rank-feature bonus.
- New `Dealer.fetch_chunk_vectors(chunk_ids, tenant_ids, kb_ids, dim)`:
on-demand vector fetch for citation use.
- `Dealer.retrieval` routes Infinity → unchanged, OceanBase → existing
local `rerank`, ES → new KNN-score path.
**`common/doc_store/es_conn_base.py`**
- New `get_scores(res)` helper returning `{_id: _score}` directly from
hit headers (ES doesn't surface `_score` through `get_fields`).
**`api/db/services/dialog_service.py`**
- New top-level `_hydrate_chunk_vectors(...)` helper. On ES it
back-fills `ck["vector"]` from `fetch_chunk_vectors` right before
`insert_citations`. No-op on Infinity / OB (their chunks already carry
vectors).
- Both `decorate_answer` closures became `async` and are `await`-ed at
all call sites in `async_chat` and `async_ask`.
## Backend behavior
| Backend | Returns chunk vec in main search | Sim source | Vectors for
citations |
|---|---|---|---|
| ES | No | second KNN call (`_score`) merged with term sim | fetched on
demand |
| Infinity | No (unchanged) | normalized `_score` | already on chunks |
| OceanBase | Yes (kept) | local hybrid rerank | already on chunks |
## Test plan
Closes#14753
## What changed
| File | Change |
|---|---|
| `pyproject.toml` | `requires-python` → `>=3.13,<3.15`; remove
`strenum==0.4.15` |
| `Dockerfile` | `uv python install 3.13`, `uv sync --python 3.13` |
| `.github/workflows/tests.yml` | `uv sync --python 3.13` on both matrix
legs |
| `CLAUDE.md` | dev setup command + requirements note updated |
| `deepdoc/parser/mineru_parser.py` | `from strenum import StrEnum` →
`from enum import StrEnum` |
| `agent/tools/code_exec.py` | same |
`StrEnum` has been in the stdlib since Python 3.11 — the `strenum`
backport package is no longer needed once the floor is 3.13.
## Why uv.lock is not regenerated
`uv lock --python 3.13` fails because:
1. The infiniflow/graspologic fork pins `numpy>=1.26.4,<2.0.0`
2. `tensorflow-cpu>=2.20.0` (the first release with cp313 wheels)
depends on `ml-dtypes>=0.5.1`, which requires `numpy>=2.1.0`
3. These two constraints are irreconcilable on Python 3.13
The lockfile regeneration requires loosening the `numpy` upper bound in
the `infiniflow/graspologic` fork. Once that fork commit is updated and
the SHA in `pyproject.toml:49` is bumped, `uv lock --python 3.13` will
succeed.
## RFC corrections
Two claims in the original RFC (#14753) did not hold up under code
review:
- **"graspologic hard-blocks 3.13"** — the infiniflow fork at the pinned
commit has no `<3.13` Python constraint. The blocker is the transitive
`numpy<2.0.0` conflict with tensorflow-cpu's test dependency, not a
direct Python version cap.
- **"free-threading throughput gains for I/O-bound workload"** — Python
3.13 free-threading requires a special `--disable-gil` build and
provides no benefit for async I/O code (the GIL is already released
during I/O). The real motivation is forward compatibility and improved
error messages.
### What problem does this PR solve?
When multiple MCP servers expose tools with the same name, the agent
currently registers those tools using their original MCP names. This can
lead to two issues:
- later MCP tools may overwrite earlier ones in the agent tool map
- duplicate function names may be exposed to the LLM
This PR fixes duplicate MCP tool-name handling by applying the same
indexed naming strategy already used for native agent tools. Native
tools are exposed with generated names such as `<tool_name>_<index>` to
avoid collisions, and MCP tools now follow the same convention for
consistency.
Specifically, this PR:
- assigns unique indexed function names to MCP tools exposed to the LLM
- preserves each MCP tool's original server-side name in an
`MCPToolBinding`
- dispatches MCP calls using the original MCP tool name while keeping
the indexed name in the agent tool map
- allows MCP metadata conversion to override only the OpenAI function
name without modifying the original MCP tool metadata
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Validation
The validation was performed using two MCP servers. Both servers exposed
a tool with the same name: `mcp0`. Both tools take no input parameters.
**MCP Server One:**
<img width="1780" height="625" alt="ONE"
src="https://github.com/user-attachments/assets/801a2654-fc10-4b71-b31c-81841fd40c55"
/>
**MCP Server Two:**
<img width="1777" height="624" alt="Second"
src="https://github.com/user-attachments/assets/c095151d-7bdf-47c8-9bfe-6aaf4a01b944"
/>
**Before the fix:**
When invoking `mcp0`, only the `mcp0` tool from the MCP server injected
later could be called successfully. As shown below, both `mcp0` tools
were present, but only the later-registered one was actually invokable.
<img width="694" height="935" alt="Three"
src="https://github.com/user-attachments/assets/3b9d7ab2-1765-492c-b8e0-bf05a69933ca"
/>
**After the fix:**
Both `mcp0` tools can now be invoked correctly.
<img width="737" height="1095" alt="F"
src="https://github.com/user-attachments/assets/6e896627-2b7f-41bb-becc-daa0c73ff58f"
/>
<img width="730" height="1090" alt="six"
src="https://github.com/user-attachments/assets/aba75593-26ae-4e3b-951d-b45ff177fd32"
/>
# feat: Add Generic REST API Connector
## What problem does this PR solve?
RAGFlow supports many specific data source connectors (MySQL, Slack,
Google Drive, etc.), but there was no way to connect an arbitrary REST
API as a data source. Users with custom or third-party APIs had to write
a new connector class for each one.
This PR adds a **generic, configuration-driven REST API connector** that
lets users connect any REST API as a data source entirely through the UI
— no code changes needed per API.
---
## Features
### Core Connector (`common/data_source/rest_api_connector.py`)
- Implements `LoadConnector` and `PollConnector` interfaces for full and
incremental sync
- **Configurable authentication:** None, API Key (custom header), Bearer
Token, Basic Auth
- **Pluggable pagination:** Page-based, Offset-based, Cursor-based, or
None
- Smart page-size inference from user's query parameters to avoid
duplicate/conflicting params
- Configurable request delay between pages to prevent API rate limiting
- Auto-detection of the items array in JSON responses (`items`,
`results`, `data`, `records`, or first list found)
- **Advanced field mapping** with dot-notation (`country.name`), array
wildcards (`newsType[*].name`), type hints, and default values
- Optional content template rendering (`"Title: {title}\nBody: {body}"`)
- HTML stripping for content fields
- Stable document IDs via `hash128` from a configurable ID field or
auto-generated from item content
- Pydantic configuration schema with automatic coercion of UI string
inputs to dicts/lists
### Backend Registration (`rag/svr/sync_data_source.py`,
`common/constants.py`, `common/data_source/config.py`)
- `REST_API` sync class wired into RAGFlow's `func_factory`
- Full sync (`load_from_state`) and incremental polling (`poll_source`)
support
- Credentials and config passed from task to connector following
existing patterns (MySQL, SeaFile, etc.)
### Test Connection Endpoint (`api/apps/connector_app.py`)
- `POST /v1/connector/<id>/test` validates config schema,
authentication, and API connectivity without triggering a sync
- Clear error messages for auth failures vs. config issues
### Frontend UI (`web/src/pages/user-setting/data-source/constant/`)
- **Postman-style configuration:** Base URL, Query Parameters (key=value
per line), Auth, Content Fields, Metadata Fields, Pagination Type
- Auth-type-aware form: fields for API key header/value, Bearer token,
or Basic username/password appear only when relevant
- **Advanced Settings** toggle for: Custom Headers, Max Pages, Request
Delay, Poll Timestamp Field, Request Body (POST)
- Connector icon (SVG) and i18n strings (English)
- **"Test Connection"** button to validate before syncing
---
## Controls & Safety
- Configurable max pages safety cap (default: 1000, adjustable in UI)
- Configurable request delay between pages (default: 0.5s, adjustable in
UI)
- Auth errors (401/403) fail immediately without retries; transient
errors retry with exponential backoff
- Diagnostic logging: auth setup confirmation, request details on
failure, content field extraction status
---
## Type of change
- [x] New Feature (non-breaking change which adds functionality)
##Visual Screenshots of Features
<img width="482" height="510" alt="Screenshot 2026-03-11 at 5 19 52 PM"
src="https://github.com/user-attachments/assets/dcb7ab4a-1622-44f3-bb02-d6f0527314c4"
/>
(Connector can be configured within the external data sources tab)
Configuration Parameters:
<img width="661" height="682" alt="Screenshot 2026-03-11 at 5 20 46 PM"
src="https://github.com/user-attachments/assets/5e154e71-4ab5-4872-bfb2-04f02b73c18a"
/>
<img width="661" height="682" alt="Screenshot 2026-03-11 at 5 20 54 PM"
src="https://github.com/user-attachments/assets/00cb14b7-0bcf-4b94-9d71-34e93369ecb2"
/>
Connection can be tested before attaching to dataset:
<img width="981" height="681" alt="Screenshot 2026-03-11 at 5 21 40 PM"
src="https://github.com/user-attachments/assets/aaa6eeeb-89a7-4349-bc34-2423bf8be9ee"
/>
Ingestion tested with API connector (works perfectly fine):
<img width="1062" height="705" alt="Screenshot 2026-03-11 at 5 22 30 PM"
src="https://github.com/user-attachments/assets/afcd0d58-cadd-4152-badc-d2f14d96fbec"
/>
Search & Retrieval works as well with metadata flow:
<img width="1062" height="705" alt="Screenshot 2026-03-11 at 5 23 05 PM"
src="https://github.com/user-attachments/assets/d41ee935-dcf7-4456-b317-22a76ca032c0"
/>
---------
Co-authored-by: Ahmad Intisar <ahmadintisar@Ahmads-MacBook-M4-Pro.local>
Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
### What problem does this PR solve?
Fixes#14570. On OpenSearch backends (`DOC_ENGINE=opensearch`) every
document-metadata write failed with `'OSConnection' object has no
attribute 'create_doc_meta_idx'`, so both `PATCH
/api/v1/datasets/{ds}/documents/{doc}` with `meta_fields` and `POST
/api/v1/datasets/{ds}/metadata/update` were unusable while every other
document operation (retrieval, parsing, name update, chunk management)
worked correctly on the same OpenSearch cluster.
The bug runs deeper than the missing method name in the error message
suggests. `DocMetadataService` also reached into
`settings.docStoreConn.es.*` directly for the index refresh, the
scripted partial update, and the count call, which means that even after
adding `create_doc_meta_idx` to `OSConnection` the very next call in the
same metadata flow would still raise `AttributeError` because
`OSConnection` exposes `self.os` rather than `self.es`. Fixing only the
reported symptom would have moved the failure one line down without
restoring the feature.
This PR adds a uniform document-metadata dispatch surface to both
connection classes so they present the same abstract API, and routes the
service layer through that surface via `getattr` guards instead of
poking at backend-specific attributes. The four new methods on
`OSConnection` and `ESConnectionBase` are `create_doc_meta_idx`,
`refresh_idx`, `count_idx`, and `replace_meta_fields`.
`OSConnection.create_doc_meta_idx` reuses the existing
`conf/doc_meta_es_mapping.json` schema in the OpenSearch `body=` form
because OpenSearch and Elasticsearch share the same index-creation
payload, and `replace_meta_fields` emits a full scripted assignment
(`ctx._source.meta_fields = params.meta_fields`) on both backends so
removed keys actually disappear instead of being preserved by deep-merge
semantics.
The `getattr`-guarded dispatch in `DocMetadataService` keeps the
existing fall-through paths intact for Infinity and OceanBase, which
continue to rely on their search-based count fallback and on the
delete-then-insert metadata replacement they used before, so this change
is strictly additive for those two backends.
Verification: `pytest
test/unit_test/rag/utils/test_opensearch_doc_meta.py` runs 16 new unit
tests that pass locally and pin the `OSConnection` dispatch surface, the
`create_doc_meta_idx` short-circuit when the index already exists, the
mapping-file payload routing, the `IndicesClient.create` failure path,
the `refresh_idx` and `count_idx` success and error sentinels, and the
full-assignment script emitted by `replace_meta_fields`. The test module
stubs `common.settings` and `rag.nlp` at import time so the suite runs
without the heavy backend SDKs that the rest of the repository pulls in
transitively.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: tmimmanuel <tmimmanuel@users.noreply.github.com>
### What problem does this PR solve?
S3-family connector syncs currently re-download every in-window object
just so we can compute `xxhash128(blob)` and compare against
`Document.content_hash`. Anything that bumps `LastModified` without
changing bytes (`aws s3 cp` touches, bucket re-encryption, etc.) pays
full bandwidth and re-parses files that didn't actually change. #14628
covers the broader incremental-ingestion redesign; this PR is the first
slice.
The fix is a pre-listing short-circuit. `BlobStorageConnector` (S3 / R2
/ GCS / OCI / S3-compat) now implements a new `FingerprintConnector`
interface: `list_keys()` paginates `list_objects_v2` and yields
`KeyRecord(key, fingerprint)` where `fingerprint = xxhash128(ETag)`. The
orchestrator joins those against the connector's existing `{doc_id:
content_hash}` map and only calls `get_value(key)` when the fingerprint
differs. Unchanged keys are skipped entirely — no `GetObject`, no
re-parse.
No DDL. xxhash128(ETag) is 32 hex chars and reuses the existing
`Document.content_hash` column per @yingfeng's suggestion; the connector
decides at listing time whether to populate it. Local uploads and
connectors that don't opt in fall through to the existing post-download
`xxhash128(blob)` path with no behavior change.
This is PR-1 of a 4-PR series — full design lives on #14628. Subsequent
PRs extend tier 1 to local FS / WebDAV / Dropbox / Seafile / RDBMS
(PR-2), wire up tier 2 cursor connectors with `SyncLogs.next_checkpoint`
(PR-3), and unify deletion via `KeyRecord(deleted=True)` reconciliation
(PR-4). Holding those back keeps this PR additive and reviewable on its
own.
#### Files touched
- `common/data_source/models.py` — new `KeyRecord`; optional
`fingerprint` on `Document`
- `common/data_source/interfaces.py` — `IncrementalCapability` enum,
`FingerprintConnector` ABC
- `common/data_source/blob_connector.py` — `BlobStorageConnector`
implements `FingerprintConnector`; per-object download factored into
`_build_document_from_obj()` so `_yield_blob_objects`, `list_keys`,
`get_value` all share it
- `rag/svr/sync_data_source.py` —
`_BlobLikeBase._fingerprint_filtered_generator` does the bypass loop;
`_run_task_logic` plumbs `doc.fingerprint` into the upload dict
- `api/db/services/document_service.py` —
`list_id_content_hash_map_by_kb_and_source_type()` helper
- `api/db/services/connector_service.py` + `file_service.py` —
fingerprint flows through `duplicate_and_parse → upload_document` and
lands in `content_hash`
- `test/unit_test/common/test_blob_connector_fingerprint.py` — 14 tests
covering ETag normalization (single-part, multipart, quoted, empty),
`list_keys()` not calling `GetObject`, `get_value()` materializing with
fingerprint, deterministic/stable fingerprints, and the bypass loop
asserting `GetObject` is *not* called on a match
#### Worth flagging for review
Old `_BlobLikeBase._generate` called `poll_source(start, now)` with a
`LastModified` window when `poll_range_start` was set. New code uses
`_fingerprint_filtered_generator` (full bucket listing + fingerprint
compare) outside of explicit `reindex=1`. Strictly better for
unchanged-bucket cases since it skips `GetObject`, but it does mean
every sync now does a full `list_objects_v2` paginate. Should still be
cheap for most buckets — flagging in case anyone has a very large bucket
where the time-window filter was meaningful.
On migration: existing rows have `content_hash = xxhash128(blob)` from
the old code. The first sync after this lands sees ETag-derived
fingerprints that don't match, re-fetches every object once, and writes
the new fingerprint. From the second sync onward the bypass works as
expected. "Slow day one, fast every day after." A `fingerprint_backfill:
trust` opt-out is sketched in the design doc but not in this PR.
#### Test plan
- [x] `uv run ruff check` — clean on all 8 touched files
- [x] `uv run pytest
test/unit_test/common/test_blob_connector_fingerprint.py -v` — 14 passed
- [x] Broader unit-test suite — no regressions in anything I touched
- [ ] Manual smoke against a real S3 bucket — configure a connector, run
sync twice, expect the second sync to log `bypassed=N, fetched=0` and no
`GetObject` calls in CloudTrail / bucket access logs
- [ ] Manual smoke with `reindex=1` — confirm the full re-download path
still works
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
---------
Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
### What problem does this PR solve?
Fixes#14412.
`common.metadata_utils.meta_filter` evaluates user-defined metadata
conditions in Python after `DocMetadataService.get_flatted_meta_by_kbs`
loads the entire `meta_fields` table into memory. Past a few thousand
documents per knowledge base this becomes a memory bottleneck and a
wasted ES round-trip — every filter request currently fetches up to
10000 metadata rows even when the resulting `doc_ids` list is tiny.
This PR adds an ES push-down path that translates the same filter
language into a `bool` query and returns just the matching document IDs.
**Changes**
- `common/metadata_es_filter.py` *(new)*: pure-Python translator from
the RAGflow filter list to ES DSL. Covers every operator the in-memory
path supports (`=`, `≠`, `>`, `<`, `≥`, `≤`, `in`, `not in`, `contains`,
`not contains`, `start with`, `end with`, `empty`, `not empty`) with
`case_insensitive: true` on `prefix` and `wildcard` for parity with the
existing lower-cased Python comparisons. User wildcard metacharacters
are escaped before being injected into `wildcard` patterns. Negative
operators (`≠`, `not in`, `not contains`, ranges) are wrapped with an
`exists` guard so they do not accidentally match documents missing the
key, matching the legacy `if k not in metas` behaviour.
- `api/db/services/doc_metadata_service.py`: new
`DocMetadataService.filter_doc_ids_by_meta_pushdown(kb_ids, filters,
logic)` that returns the doc IDs ES matched, or `None` to signal the
caller should fall back to the in-memory path. Returns `None` when the
active doc store is Infinity (`meta_fields` is a JSON column, not a
dotted-object mapping), when any filter cannot be expressed in DSL
(`UnsupportedMetaFilter`), or when the ES request or metadata index
lookup errors.
- `common/metadata_utils.py`: `apply_meta_data_filter` accepts an
optional `kb_ids` argument. When supplied, conditions go through
push-down first via a new `_try_meta_pushdown` helper; on `None` the
function falls back to the original `meta_filter` call. Default
behaviour is unchanged for callers that don't pass `kb_ids`.
- Updated all four callers (`agent/tools/retrieval.py`,
`api/db/services/dialog_service.py` ×2,
`api/apps/services/dataset_api_service.py`, `api/apps/sdk/session.py`)
to forward `kb_ids` so the push-down path is exercised in production.
- `test/unit_test/common/test_metadata_es_filter.py` *(new)*: 35 unit
tests covering every operator's DSL shape, value coercion
(`ast.literal_eval`, lowercasing, ISO-date pass-through), wildcard
escaping, OR-logic wrapping that protects negative clauses, and the
doc-ID extractor.
**Behaviour preserved**
- The in-memory `meta_filter` is untouched and still services every
fallback case (Infinity backend, unknown operators, ES outages).
- The eligibility / credibility / issue-multiplier semantics described
in the LLM-driven `auto` and `semi_auto` modes still hand the LLM the
full in-memory `metas` dict to choose conditions from. Only the
*evaluation* of those generated conditions is pushed down.
- Existing tests in
`test/unit_test/common/test_metadata_filter_operators.py` continue to
pass (14/14).
**Test plan**
- `pytest test/unit_test/common/test_metadata_es_filter.py` — 35 passed.
- `pytest test/unit_test/common/test_metadata_filter_operators.py` — 14
passed.
- `ruff check` clean on every modified file.
- Reviewer please validate the ES query shapes against a live cluster —
particularly `case_insensitive` on `wildcard` and `prefix` (requires ES
7.10+) and the `exists` + `must_not` pairing for `≠`.
**Notes**
- The first cut caps each push-down request at 10000 results, matching
the existing `get_flatted_meta_by_kbs` limit, and logs a warning when
the cap is hit. A `search_after` follow-up would let us drop the cap
entirely once the push-down path is validated.
- Operator parity with the in-memory path is exact for the canonical
unicode operators (`≥`, `≤`, `≠`) used internally; the ASCII aliases
(`>=`, `<=`, `!=`) are normalised by `convert_conditions` before they
reach the translator.
### Type of change
- [x] Performance Improvement
---------
Co-authored-by: sxxtony <sxxtony@users.noreply.github.com>
Fixes#14551
### What problem does this PR solve?
The Moodle connector did not let the sync runner clean up indexed
documents that were deleted from the source. Other connectors such as
dropbox, seafile, webdav, and rss already do this through a slim
snapshot pass. This PR adds the same support for Moodle.
When `sync_deleted_files` is on, the runner now asks the Moodle
connector for a lightweight list of every module id that could be
indexed. The runner then compares this list with the index and removes
any indexed document whose id is not in the list.
The slim pass does not download files. It only goes through courses and
modules and yields ids. The id format matches the ids that the loader
produces, so the match is exact.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### Notes
- `MoodleConnector` now also implements `SlimConnectorWithPermSync`.
- New `retrieve_all_slim_docs_perm_sync` yields slim docs with the same
ids the loader uses (`moodle_resource_<id>`, `moodle_forum_<id>`,
`moodle_page_<id>`, `moodle_book_<id>`, `moodle_assign_<id>`,
`moodle_quiz_<id>`).
- The `Moodle` sync class now returns `(document_generator, file_list)`
so the runner can do the cleanup. If the slim snapshot fails,
`file_list` is set back to `None` and the run continues without cleanup.
- The web data source map exposes `syncDeletedFiles` for Moodle so the
option shows up in the UI.
### How was this tested?
- `ruff check` passes on the changed Python files.
- Manual review of the produced slim ids against the ids the loader
builds in `_process_resource`, `_process_forum`, `_process_page`,
`_process_book`, and `_process_activity`.
- Behavior parity with the merged dropbox (#14476), seafile (#14499),
webdav (#14491), and rss (#14493) PRs.
### What problem does this PR solve?
Since secret key get and set logic is updated, the go server also need
to update.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
Feat: enable sync deleted files for RDBMS & fix remove last file issue
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
A and B, two API servers and a REDIS server.
If A and REDIS restart, B will hold the obsolete secret key and will
lead to error.
TODO:
app.config['SECRET_KEY'] and app.secret_key still hold obsolete secret
key.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
### What problem does this PR solve?
This PR fixes a bug where `layout_recognize="<name>@OpenDataLoader"` was
misrouted and then failed during parsing in the naive parser path. It
now routes correctly to OpenDataLoader and avoids passing unsupported
arguments that caused runtime errors. fixes#14572
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
### Type of change
- [v] Bug Fix (non-breaking change which fixes an issue)
Co-authored-by: wiratama <dafa.wiratama@bankraya.co.id>
## Summary
- normalize string items for list-valued metadata filters in
`meta_filter`
- fix `in` / `not in` case asymmetry when document metadata is
lowercased but filter list values are not
- add regression tests that cover the original issue scenario using
uppercase list values
## Validation
- `PYTHONPATH=external/ragflow pytest
external/ragflow/test/unit_test/common/test_metadata_filter_operators.py
-q`
## Notes
- I commented on #14389 before opening this PR to claim the issue.
- The new tests use `value=["F2", "F11"]` so they fail on the old
implementation and pass with this fix.
- This also benefits other non-comparison operators that flow through
the same normalization path.
Co-authored-by: copizza <copizza@users.noreply.github.com>
Co-authored-by: Wang Qi <wangq8@outlook.com>
### What problem does this PR solve?
add IMAP deleted document sync
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Incremental DingTalk AI Table (Notable) sync did not reconcile rows
removed on the remote side with documents already in the knowledge base.
This follows the coordinated datasource work in #14362 (“sync deleted
files”).
This PR adds a **full slim snapshot**
(`retrieve_all_slim_docs_perm_sync`) that lists **current record IDs for
all sheets** without building document blobs, using the same logical
document IDs as full ingest
(`dingtalk_ai_table:{table_id}:{sheet_id}:{record_id}`). When
**`sync_deleted_files`** is enabled on incremental runs,
`DingTalkAITable._generate` returns **`(document_generator,
file_list)`** so **`SyncBase`** can run
**`cleanup_stale_documents_for_task`** and remove KB rows that no longer
exist remotely.
Design notes:
- **`_document_id`** centralizes the ID string so slim snapshots and
**`_convert_record_to_document`** stay aligned with
**`hash128(doc.id)`** semantics used during ingestion/cleanup.
- **`end_ts`** is captured before building **`file_list`**, then
**`poll_source`** uses the same upper bound (consistent with other
Dropbox-style connectors).
- **`batch_size`** from connector config is coerced to a positive
**`int`** before constructing the connector.
- Slim snapshot failures are caught in **`_generate`**; **`file_list`**
is set to **`None`** so cleanup is skipped rather than running on
partial/error state.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### Files changed (summary)
| Area | Change |
|------|--------|
| `common/data_source/dingtalk_ai_table_connector.py` |
`SlimConnectorWithPermSync`, `retrieve_all_slim_docs_perm_sync`,
`_document_id` shared with document conversion |
| `rag/svr/sync_data_source.py` | `DingTalkAITable._generate`: slim
snapshot + tuple return; `batch_size` validation; shared `end_ts` with
`poll_source` |
| `web/src/pages/user-setting/data-source/constant/index.tsx` |
`syncDeletedFiles` for DingTalk AI Table in
`DataSourceFeatureVisibilityMap` |
Closes / relates to: #14362
Concurrent CREATE TABLE / CREATE INDEX / DROP TABLE on the same Infinity
instance can race on the catalog counter (e.g. db|1|next_table_id) and
fail with error 9003 "Resource busy" instead of waiting on a lock. Two
users creating a knowledge base at the same instant, or any deployment
with multiple backend workers behind one Infinity, can hit it.
Wrap the metadata paths in create_idx, create_doc_meta_idx, and
delete_idx with exponential backoff + jitter (5 attempts, 50ms base).
The wrapped operations already use ConflictType.Ignore, so retrying is
idempotent — worst case the second attempt is a no-op against an
already-created table. Tunable via INFINITY_META_RETRY_MAX /
INFINITY_META_RETRY_BASE_DELAY_MS.
Repro: stress 30 concurrent POST /api/v1/datasets against a 4-worker
backend → ~50% of requests fail without the patch (Resource busy from
the second worker that hits the counter), 100% succeed with it. At 100
concurrent requests, all 100 succeed in ~1.2s; the retry budget never
exhausted in our tests.
Scope is limited to metadata paths only — data-path operations (INSERT
chunks, SELECT for retrieval) go through per-table code paths and don't
share the contended counter.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: yoan sapienza <Yoan Sapienza yoan.sapienza@orange.fr Yoan Sapienza zappy@macbookpro.home>
### What problem does this PR solve?
Partially addresses #14362.
This PR enables syncing deleted files for RSS data sources.
Previously, RSS incremental sync only returned feed entries whose
timestamps were inside the poll window. If an entry was removed from the
RSS feed, RAGFlow had no full current RSS snapshot to pass into the
shared stale-document cleanup path, so the deleted remote entry could
remain in the knowledge base.
This PR:
- adds `retrieve_all_slim_docs_perm_sync()` to `RSSConnector`
- reuses the same `rss:<md5(stable_key)>` document ID derivation used by
normal RSS ingest
- returns `(document_generator, file_list)` for incremental RSS sync
when `sync_deleted_files` is enabled
- captures the poll end timestamp before snapshot/poll so cleanup does
not race against the same sync window
- adds start/end logs around RSS slim snapshot collection
- exposes the deleted-file sync toggle for RSS in the data source UI
Per maintainer request on related datasource PRs, this PR contains no
test-case changes. Local verification was run with an external script.
Validation:
- `uv run ruff check common/data_source/rss_connector.py
rag/svr/sync_data_source.py`
- `uv run pytest test/unit_test/rag/test_sync_data_source.py -q`
- `./node_modules/.bin/eslint
src/pages/user-setting/data-source/constant/index.tsx`
- `git diff --check`
- `uv run python /tmp/verify_rss_deleted_sync.py --repo
/root/74/ragflow`
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## What problem does this PR solve?
Incremental WebDAV sync only ingested files whose modification time fell
inside the poll window; documents removed on the WebDAV server were
never removed from the knowledge base. This aligns with
[#14362](https://github.com/infiniflow/ragflow/issues/14362)
(coordinated datasource “sync deleted files” work).
This PR adds a **full-tree slim snapshot**
(`retrieve_all_slim_docs_perm_sync`) that enumerates current remote
paths **without downloading file contents**, using the same logical
document IDs as full ingest (`webdav:{base_url}:{file_path}`). When
**`sync_deleted_files`** is enabled on incremental runs, sync returns
**`(document_generator, file_list)`** so **`SyncBase`** runs
**`cleanup_stale_documents_for_task`** and removes KB rows no longer
present remotely.
Design notes:
- **`_list_files_recursive`** gains **`filter_by_mtime`**: snapshot
passes **`filter_by_mtime=False`** (full tree under **`remote_path`**);
**`poll_source`** keeps mtime-window filtering as before.
- Slim snapshot applies the same **extension** and **`size_threshold`**
rules as **`_yield_webdav_documents`** so retain IDs match what would be
indexed.
- **`end_ts`** is captured before building **`file_list`**, then
**`poll_source`** uses the same upper bound (consistent with
Dropbox-style connectors).
## Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Files changed
| Area | Change |
|------|--------|
| `common/data_source/webdav_connector.py` |
`SlimConnectorWithPermSync`, `retrieve_all_slim_docs_perm_sync`,
`filter_by_mtime` on `_list_files_recursive` |
| `rag/svr/sync_data_source.py` | WebDAV `_generate`: `file_list` +
tuple return; pass **`batch_size`** from connector config |
| `web/src/pages/user-setting/data-source/constant/index.tsx` |
`syncDeletedFiles` for WebDAV in `DataSourceFeatureVisibilityMap` |
### What problem does this PR solve?
Refs #14362.
This PR enables syncing deleted files for Zendesk data sources.
Previously, Zendesk incremental sync never returned a slim remote
snapshot to the shared stale-document cleanup path, so deleted remote
Zendesk records could remain in RAGFlow. The existing Zendesk slim
snapshot also included records that ingestion intentionally skips, such
as draft articles, articles without bodies, skipped-label articles,
empty-body articles, and tickets with `status == "deleted"`.
This PR:
- exposes the deleted-file sync option for Zendesk in the data source UI
- returns Zendesk slim snapshots during incremental sync when
`sync_deleted_files` is enabled
- reuses Zendesk indexability rules so cleanup compares against the same
records ingestion can materialize
- adds start/end logs around Zendesk slim snapshot collection for
operational visibility
Per maintainer request, this PR contains no test-case changes. Manual
verification recording will be provided separately.
Validation:
- `uv run ruff check common/data_source/zendesk_connector.py
rag/svr/sync_data_source.py`
- `uv run pytest test/unit_test/rag/test_sync_data_source.py -q`
- `./node_modules/.bin/eslint
src/pages/user-setting/data-source/constant/index.tsx`
### Type of change
- [ ] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
- [ ] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
### What problem does this PR solve?
Partially addresses #14362.
Adds deleted-file sync support for the Asana data source. Asana already
indexes task attachments as documents, but it did not provide the slim
document snapshot required by stale-document reconciliation, and the
sync wrapper never returned a `file_list` for cleanup.
This PR:
- adds `retrieve_all_slim_docs_perm_sync()` to `AsanaConnector`
- builds slim IDs with the same `asana:{task_id}:{attachment_gid}`
format used by indexed documents
- avoids downloading attachment blobs during the snapshot
- aborts the snapshot if Asana API errors occur, preventing partial
snapshots from deleting valid local docs
- captures the incremental poll end time before snapshotting and makes
`poll_source()` respect that boundary
- exposes the deleted-file sync toggle for Asana in the data source UI
Per maintainer request, this PR contains no test-case changes. Manual
verification recording will be provided separately.
Validation:
- `uv run ruff check common/data_source/asana_connector.py
rag/svr/sync_data_source.py`
- `uv run pytest test/unit_test/rag/test_sync_data_source.py -q`
- `./node_modules/.bin/eslint
src/pages/user-setting/data-source/constant/index.tsx`
- `git diff --check`
### Type of change
- [x] New Feature
### What problem does this PR solve?
Incremental Seafile sync only ingests files whose modification time
falls in the poll window; documents removed in Seafile were never
removed from the knowledge base. This contributes to
[#14362](https://github.com/infiniflow/ragflow/issues/14362) (datasource
“sync deleted files” coordination).
This PR adds a **slim snapshot** (`retrieve_all_slim_docs_perm_sync`)
that enumerates current remote file IDs **without downloading content**,
using the same logical IDs as full ingest
(`seafile:{repo_id}:{file_id}`). When **`sync_deleted_files`** is
enabled on incremental runs, **`SeaFile._generate`** returns
**`(document_generator, file_list)`** so **`SyncBase`** can run
**`cleanup_stale_documents_for_task`** and remove stale KB documents.
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What changed
- **`common/data_source/seafile_connector.py`**: `SeaFileConnector`
implements **`SlimConnectorWithPermSync`**;
**`_list_files_recursive(..., filter_by_mtime=...)`** supports full-tree
listing for snapshots; **`retrieve_all_slim_docs_perm_sync()`** reuses
the same library/root scan as ingest and applies the same **size**
ceiling; logging for snapshot start/end and counts.
- **`rag/svr/sync_data_source.py`**: **`SeaFile._generate`** validates
**`batch_size`**, captures **`end_ts`** before snapshot +
**`poll_source`**, wraps slim retrieval in **`try`/`except`** (
**`file_list = None`** on failure so ingest continues), returns
**`(generator, file_list)`**.
- **`web/src/pages/user-setting/data-source/constant/index.tsx`**:
**`syncDeletedFiles`** for Seafile in
**`DataSourceFeatureVisibilityMap`**.
### What problem does this PR solve?
Feat: enable sync deleted file for Discord
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Partially addresses #14362 by adding deleted-file sync support for the
Dropbox data source.
Dropbox previously did not provide the slim current-file snapshot
required by stale document reconciliation, and its sync runner returned
only document batches. As a result, enabling deleted-file sync could not
remove local documents that had been deleted from Dropbox.
This PR:
- Adds `retrieve_all_slim_docs_perm_sync()` to `DropboxConnector`.
- Reuses Dropbox metadata traversal to collect current remote file IDs
without downloading file contents.
- Wires incremental Dropbox sync to return `(document_generator,
file_list)` when `sync_deleted_files` is enabled.
- Enables the deleted-file sync toggle for Dropbox in the data source
settings UI.
- Adds regression coverage for slim snapshots, nested folders, paginated
listings, duplicate filenames, and full reindex behavior.
Tests:
- `uv run pytest test/unit_test/common/test_dropbox_connector.py -q`
- `uv run pytest test/unit_test/rag/test_sync_data_source.py -q`
- `uv run pytest test/unit_test/common/test_dropbox_connector.py
test/unit_test/rag/test_sync_data_source.py -q`
- `uv run ruff check common/data_source/dropbox_connector.py
rag/svr/sync_data_source.py
test/unit_test/common/test_dropbox_connector.py
test/unit_test/rag/test_sync_data_source.py`
- `./node_modules/.bin/eslint
src/pages/user-setting/data-source/constant/index.tsx`
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Feat: enable sync deleted files in gitlab
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Feat: enable sync deleted files for Gmail && fix google drive issues
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: bill <yibie_jingnian@163.com>
Co-authored-by: balibabu <assassin_cike@163.com>
### What problem does this PR solve?
Feat: sync deleted files in Bitbucket
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
Fix: enable sync deleted file in airtable
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Feat: enable sync delted files for connectors
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Summary
Fixes case-asymmetric matching for manual `meta_data_filter` when using
**`in`** / **`not in`** with a **list** `value`. Document metadata
strings were lowercased, but list elements were not, so values like
`"F2"` failed to match `["F2", "F11"]` even though **`=`** behaved
correctly.
Closes#14389
## Changes
- **`common/metadata_utils.py`**: For **`in`** / **`not in`**, normalize
string elements when `value` and/or `input` is a list, consistent with
scalar string lowercasing.
- **`test/unit_test/common/test_metadata_filter_operators.py`**:
Regression tests for list `value` case-insensitivity and **`not in`**.
## Type of change
- [x] Bug fix (non-breaking)
### What problem does this PR solve?
preserve infinity available_int zero filter
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fixes#14196
## Problem
When using DeepDOC to parse large PDFs (over 1000 pages), the parser
silently truncated processing at 300 pages due to a hardcoded default
`page_to=299` in `RAGFlowPdfParser.__images__()`. This caused:
- **Errors** on pages beyond the limit
- **Poor image quality** as the parser attempted to compensate with
missing page data
- **Inconsistent chunk splitting** between full PDF imports and partial
imports
Additionally, the codebase scattered magic numbers (`299`, `600`,
`10000`, `100000`, `100000000`, `10000000000`, `10**9`) across 22 files
as sentinel values for "parse all pages", making future maintenance
error-prone.
## Root Cause
```python
# deepdoc/parser/pdf_parser.py (before)
def __images__(self, fnm, zoomin=3, page_from=0, page_to=299, callback=None):
# Only the first 300 pages were rendered; everything beyond was silently dropped
```
While most callers in `rag/app/*.py` correctly passed `to_page=100000`,
the base class `RAGFlowPdfParser.__call__()` and `parse_into_bboxes()`
invoked `__images__` **without** forwarding `page_from`/`page_to`,
falling back to the restrictive default of 299.
## Solution
### 1. Define constants in `common/constants.py`
```python
MAXIMUM_PAGE_NUMBER = 100000 # Used by the parsing layer
MAXIMUM_TASK_PAGE_NUMBER = MAXIMUM_PAGE_NUMBER * 1000 # Used by the task/DB layer
```
### 2. Replace all hardcoded sentinel values
| Layer | Files Changed | Old Values | New Value |
|---|---|---|---|
| **Deepdoc parsers** | `pdf_parser.py`, `mineru_parser.py`,
`docling_parser.py`, `opendataloader_parser.py`, `paddleocr_parser.py`,
`docx_parser.py` | `299`, `600`, `10**9`, `100000000` |
`MAXIMUM_PAGE_NUMBER` |
| **Chunk parsers** | `naive.py`, `book.py`, `qa.py`, `one.py`,
`manual.py`, `paper.py`, `presentation.py`, `laws.py`, `resume.py`,
`email.py`, `table.py` | `100000`, `10000`, `10000000000` |
`MAXIMUM_PAGE_NUMBER` |
| **Task/DB layer** | `db_models.py`, `task_service.py`,
`document_service.py`, `file_service.py` | `100000000` |
`MAXIMUM_TASK_PAGE_NUMBER` |
### 3. Fix `parse_into_bboxes()` missing parameters
Added `from_page`/`to_page` parameters to `parse_into_bboxes()` so that
the `rag/flow/parser/parser.py` DeepDOC path no longer falls back to the
restrictive default.
## Files Changed (22)
- `common/constants.py`
- `deepdoc/parser/pdf_parser.py`
- `deepdoc/parser/mineru_parser.py`
- `deepdoc/parser/docling_parser.py`
- `deepdoc/parser/opendataloader_parser.py`
- `deepdoc/parser/paddleocr_parser.py`
- `deepdoc/parser/docx_parser.py`
- `rag/app/naive.py`
- `rag/app/book.py`
- `rag/app/qa.py`
- `rag/app/one.py`
- `rag/app/manual.py`
- `rag/app/paper.py`
- `rag/app/presentation.py`
- `rag/app/laws.py`
- `rag/app/resume.py`
- `rag/app/email.py`
- `rag/app/table.py`
- `api/db/db_models.py`
- `api/db/services/task_service.py`
- `api/db/services/document_service.py`
- `api/db/services/file_service.py`
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
---------
Signed-off-by: noob <yixiao121314@outlook.com>
### What problem does this PR solve?
The POST /upload_info?url=<url> endpoint accepted a user-supplied URL
and passed it directly to AsyncWebCrawler without any validation. There
were no restrictions on URL scheme, destination hostname, or resolved IP
address. This allowed any authenticated user to instruct the server to
make outbound HTTP requests to internal infrastructure — including RFC
1918 private networks, loopback addresses, and cloud metadata services
such as http://169.254.169.254 — effectively using the server as a proxy
for internal network reconnaissance or credential theft.
This PR adds an SSRF guard (_validate_url_for_crawl) that runs before
any crawl is initiated. It enforces an allowlist of safe schemes
(http/https), resolves the hostname at validation time, and rejects any
URL whose resolved IP falls within a private or reserved network range.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Blob storage sync was downloading unsupported files first and rejecting
them later, which wasted bandwidth and made sync slower. This PR skips
unsupported extensions before download and applies `allow_images` in
blob sync. fixes#14338
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
## Summary
Fixes#5939
Entity names containing single quotes (e.g., `投影直线L'`) caused SQL syntax
errors when building filter conditions for Infinity queries, due to
unescaped string interpolation in `equivalent_condition_to_str`.
## Changes
In `common/doc_store/infinity_conn_base.py`, added `.replace("'", "''")`
escaping for string values in two branches of
`equivalent_condition_to_str` where it was missing:
1. **`field_keyword` branch with non-list value** (line 190): The list
branch already escaped single quotes on line 183, but the single-string
branch did not.
2. **Plain string value branch** (line 209): Direct f-string
interpolation `{k}='{v}'` was vulnerable to unescaped quotes.
Both fixes use the same SQL-standard escape pattern (`'` → `''`) already
applied elsewhere in this method.
## How to Test
1. Upload a document containing entity names with single quotes.
2. Enable Knowledge Graph (GraphRAG) in the parsing configuration.
3. Initiate document parsing — it should complete without SQL syntax
errors.
## Note
The original issue also reported a typo (`dge_graph_kwd` instead of
`knowledge_graph_kwd`), which has already been fixed in the current
codebase.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: noob <yixiao121314@outlook.com>
### What problem does this PR solve?
Resolve#14137 .
### Problem
Graph resolution succeeds (nodes/edges merged, pagerank updated), but
the subsequent burst of Infinity write operations in `set_graph`
exhausts the connection pool with `TOO_MANY_CONNECTIONS` errors. Root
causes:
1. **Hardcoded pool size** — `infinity_conn_pool.py` hardcoded
`ConnectionPool(max_size=4)` on initial creation and `max_size=32` on
refresh. Operators cannot tune this without patching code.
2. **No retry on transient failures** — a single `TOO_MANY_CONNECTIONS`
on edge deletes or chunk inserts kills the entire resolution+community
pipeline with no retry.
### Changes
#### `common/doc_store/infinity_conn_pool.py`
- Read `ConnectionPool` `max_size` from the `INFINITY_POOL_MAX_SIZE`
environment variable (default: `4`), applied consistently to both
initial creation and refresh paths.
- Log the actual pool size on startup for easier debugging.
#### `rag/graphrag/utils.py` — `set_graph()`
- **Edge deletes**: add exponential-backoff retry (3 attempts, 1s/2s/4s
delays) so transient `TOO_MANY_CONNECTIONS` errors are retried instead
of failing the entire job. Concurrency continues to be gated by the
existing `chat_limiter`.
- **Batch inserts**: add exponential-backoff retry (3 attempts, 1s/2s/4s
delays) for the same reason.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Signed-off-by: noob <yixiao121314@outlook.com>
Closes#6541
### What problem does this PR solve?
Add content validation to `update_chunk` (SDK and non-SDK) to reject
empty or whitespace-only content before it reaches the embedding model.
**Before:** Calling `update_chunk` with space-only content (like `" "`,
`""`, `"\n"`) bypassed validation and was sent directly to the embedding
model, which returned an error. This was the same bug previously fixed
for `add_chunk` in #6390, but `update_chunk` was missed.
**After:** Empty/whitespace-only content is caught by validation and
returns an error: `` `content` is required ``
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Summary
- remove eval-based parsing from retrieval rank feature scoring
- validate `tag_feas` at write time in chunk APIs and SDK routes
- add regression tests for safe parsing and malicious payload rejection
## Details
`tag_feas` is intended to be structured rank-feature data, but the
retrieval ranking path was evaluating stored values as Python
expressions. This change treats `tag_feas` strictly as data.
### What changed
- replace `eval()` in `rag/nlp/search.py` with safe parsing via
`json.loads()` and optional `ast.literal_eval()` compatibility for
legacy Python-dict strings
- strictly filter parsed values down to `dict[str, finite number]`
- reject invalid `tag_feas` payloads at write time in web chunk routes
and SDK document chunk routes
- add focused regression tests to prove executable strings are ignored
and invalid payloads are rejected
## Validation
- `python -m pytest test/unit_test/common/test_tag_feature_utils.py
test/unit_test/rag/test_rank_feature_scores.py -q`
---------
Co-authored-by: unknown <zhenglinkai@CCN.Local>
Co-authored-by: Yingfeng Zhang <yingfeng.zhang@gmail.com>
## Summary
Fixes#13996
Replace `json.load(open(...))` with `with open(...) as f: json.load(f)`
in two files to ensure file descriptors are properly closed.
**Affected files:**
- `common/doc_store/infinity_conn_base.py` — schema loading for Infinity
doc store
- `api/db/init_data.py` — agent template loading at startup
## Why this matters
In a long-running server process like RAGFlow, leaked file descriptors
from `json.load(open(...))` can accumulate over time. While CPython's
refcounting usually cleans these up, it's not guaranteed (especially
under memory pressure or with alternative Python runtimes), and can lead
to `OSError: [Errno 24] Too many open files`.
## Test plan
- [ ] Verify Infinity doc store schema loading still works correctly
- [ ] Verify agent templates load correctly on startup
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Refactor**
* Improved file handling in internal data processing to ensure proper
resource cleanup.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
Co-authored-by: easonysliu <easonysliu@tencent.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Closes https://github.com/infiniflow/ragflow/issues/13939
## What problem does this PR solve?
The Google Drive connector fails to detect new files after the initial
sync (#13939). The root cause is that `generate_time_range_filter()`
applies a strict `modifiedTime > poll_range_start` cutoff when querying
the Google Drive API. Files uploaded to Google Drive that retain their
original `modifiedTime` (common behavior) get silently excluded if their
timestamp predates the last sync's cutoff.
Unlike the Confluence and Jira connectors which use a configurable time
buffer (`CONFLUENCE_SYNC_TIME_BUFFER_SECONDS`) to offset
`poll_range_start` backward, the Google Drive connector had no such
mechanism — resulting in a razor-sharp timestamp boundary with zero
tolerance for overlap.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Summary
* **New Features**
* Added a configurable time buffer for Google Drive synchronization to
address timing delays and improve sync reliability.
* Improved file detection logic to include recently created files
alongside modified ones, reducing missed synchronizations.
Fixes#13823
## Problem
When querying with words like `cat`, RAGFlow's query expansion system
looks up synonyms via WordNet, which can return terms containing single
quotes (e.g., `cat-o'-nine-tails`). When using Infinity as the document
store, these unescaped single quotes in the query string cause a
`TokenError` because Infinity's lexer treats `'` as a string delimiter.
```
TokenError: Error tokenizing ' OR "big cat" OR "computerized tomography")^0.7)': Missing ' from 1:531
```
## Solution
Strip single quotes from synonym terms before they are inserted into
query expressions, consistent with how single quotes are already
stripped from the input query text (line 51 of `query.py`):
- **`common/query_base.py`**: In `sub_special_char()`, strip `'` before
escaping other special characters. This fixes the Chinese text
processing path and the `paragraph()` method.
- **`rag/nlp/query.py`**: In the English text path, strip `'` from
tokenized synonym terms.
- **`memory/services/query.py`**: Same fix for the memory query English
text path.
## Testing
The fix can be verified by:
1. Using Infinity as the document store (`DOC_ENGINE=infinity`)
2. Creating a dataset and running a retrieval test with the keyword
`cat`
3. Confirming no `TokenError` is raised and results are returned
normally
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Bug Fixes**
* Enhanced special character handling in query processing and synonym
expansion by properly sanitizing single quotes before text processing.
* Simplified OCR detection output by removing timing metadata while
preserving core detection accuracy.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: ximi <octo-patch@github.com>
