## Summary
Fixes the confirmed asyncio anti-patterns from #14755. Only the three
verified bugs are addressed; patterns already correctly using
`asyncio.new_event_loop()` in a fresh thread are left untouched.
### Changes
**`api/apps/restful_apis/tenant_api.py` — fire-and-forget
`send_invite_email`**
`asyncio.create_task()` was called without storing the `Task` reference.
CPython's GC can collect an unfinished task, silently cancelling it and
swallowing exceptions. Fixed by storing the task in a module-level
`_background_tasks: set[Task]` with a `done_callback` to discard it on
completion — the standard Python idiom for safe background tasks.
**`api/apps/restful_apis/agent_api.py` — fire-and-forget
`background_run`**
Same root cause in the webhook "Immediately" execution path. Same fix
applied.
**`rag/llm/chat_model.py` (`LocalLLM._stream_response`) —
`asyncio.get_event_loop()` on running loop**
`asyncio.get_event_loop()` returns Quart's running event loop when
called from an async context.
Calling `loop.run_until_complete()` on it raises `RuntimeError`.
Replaced with `asyncio.new_event_loop()` so the generator
uses a dedicated fresh loop, closed in a `finally` block.
## What was NOT changed
- `llm_service._sync_from_async_stream` and
`evaluation_service._sync_from_async_gen`: both already correctly use
`asyncio.new_event_loop()` inside a fresh thread.
- `llm_service._run_coroutine_sync`: only caller is `rag/app/resume.py`
(sync context), so `thread.join()` is correct there.
- `requests` in agent tools: sync methods dispatched through thread
pools; httpx migration is a separate, larger refactor.
## Test plan
- [ ] Invite a team member and confirm the email is sent with no task
warnings in logs.
- [ ] Trigger a webhook agent in "Immediately" mode; confirm canvas
state is persisted after background run.
- [ ] Verify `LocalLLM` (Jina backend) chat and streaming work
end-to-end.
Closes#14755
---------
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
### What problem does this PR solve?
1. Enhance retry and timeout, and adjust the default timeout
2. NER: spacy do not batch chunks
3. extract _has_cancel_and_exit
4. enhance log messages
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
## Summary
Fixes 10 unguarded `response.choices[0]` accesses that cause
`IndexError` or `AttributeError` when the LLM returns an empty `choices`
list — the scenario described in #14711.
- `rag/llm/cv_model.py`
- `rag/llm/chat_model.py`
Each access site is now guarded with:
```python
if not response.choices:
raise ValueError("LLM returned empty response")
```
## Verification
Detected and verified by [pact](https://github.com/qizwiz/pact) — a
sheaf-cohomological LLM contract checker using Z3 as a local theory
solver.
**pact sheaf-cohomological proof status after fix:**
| File | Ȟ¹ (after) | Z3 |
|------|-----------|-----|
| `rag/llm/cv_model.py` | 0 | UNSAT ✓ |
| `rag/llm/chat_model.py` | 0 | UNSAT ✓ |
All access sites proven safe (Z3 UNSAT certificate).
The checker was also used to verify the autogen streaming-None fix in
[microsoft/autogen#7711](https://github.com/microsoft/autogen/pull/7711).
## Test plan
- [ ] Existing test suite passes
- [ ] Manually test with a provider that returns empty `choices` under
load (e.g. Vertex AI)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Signed-off-by: Jonathan Hill <jonathan.f.hill@gmail.com>
### What problem does this PR solve?
Fixes#14997.
RAPTOR builds on the Infinity backend have been broken since v0.25.2
introduced the `extra` field in code (`rag/svr/task_executor.py:1011`)
without declaring it in `conf/infinity_mapping.json`. Every RAPTOR job
fails with:
```
infinity.common.InfinityException: (3013, 'Fail to bind the expression: extra@src/planner/expression_binder_impl.cpp:99')
```
The auto-migration in
`common/doc_store/infinity_conn_base.py:_migrate_db()` adds any columns
it finds in the mapping JSON to existing tables — so the only thing
standing between users and a working RAPTOR build is that one missing
declaration. OceanBase, ES, and OpenSearch were unaffected because they
store `extra` as a native JSON type; only Infinity (which has a strict
`varchar`/`integer`/`float` schema) needed the addition.
### The fix
Two-part change:
1. **`conf/infinity_mapping.json`**: declare `"extra": {"type":
"varchar", "default": ""}`. On next startup, `_migrate_db()` adds the
column to all existing chunk tables — no manual DDL needed for upgrading
installations.
2. **`rag/utils/infinity_conn.py` `insert()`**: serialize the `extra`
dict to a JSON string at write time, since Infinity's `varchar` can't
store a Python dict directly. Modelled on the existing `chunk_data`
handling a few lines above.
The read path (`rag/utils/raptor_utils.py:_as_extra_dict`) already
normalises both dict and JSON-string inputs, so no read-side change is
needed. Other backends are untouched — `task_executor.py` still writes
the dict, and the OceanBase/ES/OpenSearch insert paths handle dicts
natively.
### Verification
Tested on a v0.25.4 deployment with the Infinity backend by applying the
same two changes via mounted-volume override:
- Confirmed `_migrate_db()` adds the `extra` column to all pre-existing
chunk tables on startup (column visible via Infinity's
`show_columns()`).
- Triggered RAPTOR builds on four datasets (~21k chunks total) via `POST
/api/v1/datasets/<id>/index?type=raptor`.
- All four progressed past the previously-failing
`get_raptor_chunk_methods()` call into actual entity-extraction and
clustering work without the (3013) error.
- GraphRAG builds (which can trigger the same path indirectly via
`task_executor.py:857`) also progressed cleanly.
### Type of change
- [X] Bug Fix (non-breaking change which fixes an issue)
## Summary
- Adds a lightweight `@tool` decorator and `FunctionToolSession` adapter
in `rag/llm/tool_decorator.py` that let callers register plain Python
functions as LLM tools without hand-writing OpenAI function schemas or
building an MCP-style session.
- Refactors `Base.bind_tools` and `LiteLLMBase.bind_tools` in
`rag/llm/chat_model.py` to accept either the new decorator form
`bind_tools(tools=[fn1, fn2])` or the existing `(toolcall_session,
tools_schemas)` form, so existing agent/dialog call-sites in
`agent/component/agent_with_tools.py`, `api/db/services/llm_service.py`,
and `api/db/services/dialog_service.py` are unaffected.
- Adds 8 unit tests in `test/unit_test/rag/llm/test_tool_decorator.py`
covering schema shape, required/optional inference, sync + async
dispatch, and bad-input rejection.
## Usage
```python
from rag.llm.tool_decorator import tool
@tool
def get_weather(city: str) -> str:
"""Get current weather for a city.
:param city: City name to look up.
"""
return f"{city}: 21 C, partly cloudy"
chat_mdl.bind_tools(tools=[get_weather])
ans, tk = await chat_mdl.async_chat_with_tools(system, history)
```
The decorator introspects `inspect.signature` + type hints + the
docstring (`:param name:` style) and attaches an OpenAI-format
`openai_schema` to the callable. `FunctionToolSession` duck-types the
existing `ToolCallSession` protocol, dispatching async callables
directly and sync ones through `thread_pool_exec` so the event loop is
never blocked.
## Design notes
- `tool_decorator.py` deliberately does **not** live inside
`rag/llm/__init__.py` to avoid forcing every consumer through the heavy
provider auto-discovery loop and to sidestep a circular import
(`__init__.py` imports `chat_model`, which would otherwise need symbols
from `__init__.py`).
- `FunctionToolSession` is duck-typed against
`common.mcp_tool_call_conn.ToolCallSession` rather than explicitly
inheriting from it, so importing the decorator doesn't pull the MCP
client SDK into the import graph.
- Docstring parsing is intentionally minimal (`:param name:` only) to
keep this dependency-free; Google/NumPy styles can be added later via
`docstring_parser` if needed.
## Test plan
- [x] `python -m pytest test/unit_test/rag/llm/test_tool_decorator.py
-v` — 8 passed
- [x] `python -m pytest test/unit_test/rag/llm/
--ignore=test/unit_test/rag/llm/test_perplexity_embed.py` — 11 passed
(the ignored test has a pre-existing `numpy` import that's unrelated)
- [ ] Reviewer: smoke-test the new path end-to-end with a live model via
`chat_mdl.bind_tools(tools=[my_fn])` to confirm the OpenAI-format
schemas pass through unchanged
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
### What problem does this PR solve?
Feat: add local & ssh provider in admin panel
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Summary
Closes#14869.
Adds VLM-based semantic descriptions to **image chunks produced by the
MinerU parser**, closing a long-standing parity gap with the deepdoc
parser's `VisionFigureParser`. A maintainer flagged this in #13342
("We may add the VLM enhancement to MinerU parser as well") and an
earlier proposal exists in #13824; this PR lands the change end-to-end
inside the existing parser plumbing.
## Why
Today the MinerU parser returns image chunks containing only the
native `image_caption` and `image_footnote` strings from MinerU's
JSON. When neither is present (or when both are sparse), the chunk
carries effectively no searchable content for the figure and
retrieval misses it entirely. Users who configured a local VLM
(reporter's case: Gemma-4-31B) had to post-process MinerU's
`tmp/*.json` themselves.
The deepdoc parser already solves this via
[`VisionFigureParser`](deepdoc/parser/figure_parser.py): when the
tenant has an `IMAGE2TEXT` model configured, each figure gets a
semantic description merged into its chunk. This PR brings the same
behavior to MinerU.
## What changed
### `deepdoc/parser/mineru_parser.py`
- **New method `_enhance_images_with_vlm(outputs, vision_model,
callback=None)`** —
collects every `IMAGE` block with a readable `img_path`, runs
`rag.app.picture.vision_llm_chunk` in a 10-worker
`ThreadPoolExecutor` using the existing
`vision_llm_figure_describe_prompt`, and writes the result back as
`vlm_description`. Per-image failures are logged and skipped — they
never abort the run.
- **`_transfer_to_sections` (IMAGE branch)** — folds
`vlm_description` into the section text alongside caption +
footnote, so the description becomes part of the chunk and is
searchable / retrievable.
- **`parse_pdf`** — after `_read_output`, calls
`_enhance_images_with_vlm(outputs, vision_model, callback=callback)`
when a `vision_model` kwarg is supplied. Wrapped in `try / except`
so a VLM outage cannot break parsing.
### `rag/app/naive.py` (`by_mineru`)
After successfully resolving the MinerU OCR parser, also resolves the
tenant's default `LLMType.IMAGE2TEXT` model via
`get_tenant_default_model_by_type`, wraps it in an `LLMBundle`, and
injects it as `kwargs["vision_model"]` before delegating to
`parse_pdf`.
## Behavior
| Tenant config | Behavior |
|---|---|
| `IMAGE2TEXT` model configured | MinerU image chunks contain `caption +
footnote + VLM description`. Retrieval against figures now actually
works. |
| No `IMAGE2TEXT` model configured | Exact same output as today (caption
+ footnote only). Lookup fails silently with an info log; no error, no
regression. |
| VLM call fails for a single image | That image silently falls back to
caption + footnote; other images proceed. |
| Caller already passes `vision_model` in kwargs | We don't override it
— `if "vision_model" not in kwargs` guards the lookup. |
## Files
- `deepdoc/parser/mineru_parser.py` (+56)
- `rag/app/naive.py` (+13)
## What problem does this PR solve?
Closes#12017.
TTS output is deterministic for a given `(model, text)` pair, so
re-running the same text through the same TTS model produces the same
bytes — yet `Canvas.tts` and `dialog_service.tts` re-synthesized on
every request. That's slow and wastes provider quota whenever the same
assistant response is replayed, shared across users, or repeated within
a session.
### Change
New helper `rag/utils/tts_cache.py` with `synthesize_with_cache(tts_mdl,
cleaned_text)`:
- **Key:** `tts:cache:{model_id}:{sha256(text)}` — separate namespace
per model, identical cleaned text reuses a single entry across both call
sites.
- **Value:** the hex-encoded audio blob both call sites already
returned. No format change for downstream consumers.
- **TTL:** 7 days by default, configurable via
`RAGFLOW_TTS_CACHE_TTL_SECONDS`.
- **Failure modes:** a Redis hiccup falls back to direct synthesis; a
failed synthesis still returns `None` (existing contract preserved).
[`Canvas.tts`](https://github.com/infiniflow/ragflow/blob/main/agent/canvas.py#L683-L724)
and
[`dialog_service.tts`](https://github.com/infiniflow/ragflow/blob/main/api/db/services/dialog_service.py#L1367-L1380)
now route through the helper; the per-file bytes-accumulation/hex-encode
loop has been removed in favor of one shared implementation.
## Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Test plan
- [ ] **Cache hit, chat path:** Configure a dialog with TTS enabled, ask
the same question twice with `stream=false`. Verify the second response
returns the same `audio_binary` and that the second invocation doesn't
hit the TTS provider (e.g., observe provider-side logs / usage counters;
check no `LLMBundle.tts can't update token usage` log line on the second
run).
- [ ] **Cache hit, agent path:** Same exercise via a Conversational
Agent that includes a Message component playing back the answer.
- [ ] **Cache isolation per model:** Switch tenant's `tts_id` between
two models, run the same text against each — confirm the second model's
first synthesis still happens (no cross-model hits).
- [ ] **TTL override:** Set `RAGFLOW_TTS_CACHE_TTL_SECONDS=120`, confirm
the entry expires after 2 minutes.
- [ ] **Redis unavailable:** Stop Redis (or break the connection).
Verify the TTS endpoint still works — synthesis falls back to direct
calls, with a `TTS cache lookup failed` / `TTS cache store failed`
warning logged.
- [ ] **Failure path:** Configure a TTS model with an invalid API key,
ensure the response still returns successfully with `audio_binary=None`
(no regression vs. current behavior).
### 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?
1. expose batch_chunk_token_size for configuration
2. retrieve chunks when build subgraph for the doc, not retreive all
docs chunks at the begining
3. get all chunks for a document, used to be hard coded 10000
4. delete not used method run_graphrag
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Refactoring
Follow on: #14617
## 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
## RAG Optimization Description
Optimize the core `BaseTitleChunker` in
`rag/flow/chunker/title_chunker/common.py` to improve RAG document
chunking quality and retrieval accuracy.
## Key Changes
1. **Format-branched text processing**: Preserve original whitespace &
indentation for Markdown/HTML payloads to maintain document semantics
and chunk fidelity; only perform full whitespace cleaning on plain text
content.
2. **Empty chunk filtering**: Thoroughly filter invalid pure-blank lines
to reduce noisy data in vector database.
3. **Code deduplication**: Unified markdown/text/html payload extraction
logic, removed redundant repeated code blocks.
4. **None serialization fix**: Avoid converting `None` value into
literal `"None"` string in chunk text fields.
5. **Production logging**: Added input/output line count logging for
filter logic, observable in online environment.
6. **100% backward compatible**: No changes to chunking hierarchy rules,
output format and all existing workflows.
## RAG Business Value
- Preserves document format fidelity for structured Markdown/HTML files
- Reduces invalid noisy chunks → improves RAG retrieval precision
- Cleans plain text data → optimizes vector embedding quality
- Improves code maintainability with no breaking changes
- Provides observable logging for chunk filtering behavior
## Compatibility
- ✅ No API changes
- ✅ No chunk logic modifications
- ✅ All document parsing/chunking workflows unaffected
- ✅ All pre-checks passed, no code conflicts
### Type of change
- [x] Refactoring
- [x] Performance Improvement
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.
## Summary
- Rename misspelled attribute `model_speciess` to `model_species` across
4 files
- The extra `s` is a typo — `species` is already plural
## Test plan
- [ ] Verify PDF parsing with laws/manual/paper parser types still works
correctly
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: yuj <yuj@ztjzsoft.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Fixes#13975
## Problem
The GitHub data source connector had both `include_pull_requests` and
`include_issues` defaulting to `false` in both the frontend form and the
backend sync code. This meant that with the default configuration, **no
content was synced at all** from a GitHub repository — silently
producing zero results.
Additionally, the form field labels contained a typo: "Inlcude" instead
of "Include".
## Solution
- Changed `include_pull_requests` default from `false` to `true` in the
frontend form fields and default values
- Changed `include_issues` default from `false` to `true` in the
frontend form fields and default values
- Changed both backend defaults in `sync_data_source.py` from `False` to
`True`
- Fixed label typos: "Inlcude Pull Requests" → "Include Pull Requests"
and "Inlcude Issues" → "Include Issues"
This makes the GitHub connector consistent with the GitLab connector,
which already defaults `include_mrs`, `include_issues`, and
`include_code_files` all to `true`.
## Testing
- The connector now syncs both pull requests and issues by default when
a new GitHub data source is created
- Users who want to exclude PRs or issues can uncheck the corresponding
checkboxes in the form
Co-authored-by: octo-patch <octo-patch@github.com>
## Summary
- Fixes **Tongyi-Qianwen** (`QWenEmbed`) text embeddings when the
configured `base_url` points at DashScope **international**
(`dashscope-intl.aliyuncs.com`) or **China** (`dashscope.aliyuncs.com`)
hosts, including values copied from Model Studio that use the
**OpenAI-compatible** path (`.../compatible-mode/v1`).
- The `dashscope` Python SDK (`TextEmbedding.call`) expects the
**native** HTTP root (`https://<host>/api/v1`), not the
OpenAI-compatible base URL. Without mapping, international accounts
could hit the wrong host or path.
## Implementation
- Added `_dashscope_native_http_api_url()` to normalize known DashScope
hosts to `.../api/v1`, and wired `QWenEmbed` to set
`dashscope.base_http_api_url` before each embedding call (document and
query).
## Notes
- In-code comments document the Tongyi-Qianwen / DashScope intl vs CN
behavior for future maintainers.
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
## Description
This PR fixes critical bugs and improves the robustness of the RAG
reranking module while maintaining **100% backward compatibility** with
all existing functionality and providers.
## Key Changes
1. **Network Stability**: Added 30s timeout to all API requests to
prevent service blocking
2. **Boundary Protection**: Added empty query/text validation for all
rerank models
3. **Response Fault Tolerance**: Replaced hardcoded key access with
`.get()` to avoid KeyError crashes
4. **Bug Fixes**:
- Fixed `Ai302Rerank` (completely non-functional before)
- Fixed `GPUStackRerank` incorrect exception catching
- Fixed `_normalize_rank` empty array crash
5. **Code Specification**: Added type annotations, standardized
unimplemented class prompts
## Compatibility
- ✅ No changes to any class/method names
- ✅ All rerank providers (Jina/Cohere/NVIDIA/HuggingFace etc.) work as
before
- ✅ No breaking changes, zero impact on existing workflows
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
# 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?
add new testing suite for the new restful api endpoints meant to replace
http and web api tests
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
- [x] Other (please describe): test
## Problem
When parsing DOCX files with many tables, DeepDOC generates chunks
containing only empty HTML table tags, such as:
```html
<table><tr><td></td></tr><tr><td></td></tr><tr><td></td></tr><tr><td></td></tr></table>
```
After the regex cleanup at `task_executor.py:584`, this becomes `" "`
(whitespace only).
The guard at line 585 (`if not c`) only catches empty strings `""`, but
whitespace strings are truthy in Python and pass through. When sent to
Zhipu `embedding-3` API, it rejects them with error 1213:
`未正常接收到prompt参数`.
## Root Cause
```python
c = re.sub(r"</?(table|td|caption|tr|th)( [^<>]{0,12})?>", " ", c)
if not c: # ← only catches "", not " " / "\n" / "\t"
c = "None"
```
Verified with Zhipu `embedding-3`:
| Input | Result |
|---|---|
| `""` | error 1213 |
| `" "` | error 1213 |
| `"\n"` | error 1213 |
| `"None"` | OK |
## Fix
```diff
- if not c:
+ if not c.strip():
c = "None"
```
## Testing
Reproduced with a 678KB DOCX file (166 tables, 270 chunks). Chunk #89 is
the empty table above. After fix, `"None"` is sent instead and embedding
succeeds.
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
What problem does this PR solve?
In rag/app/audio.py, the supported audio extensions list contains
duplicate entries: .wav appears twice (positions 3 and 5) and .aac
appears twice (positions 6 and 14). While this does not affect runtime
behavior, it is redundant and makes the code harder to maintain.
This PR removes the duplicate entries to keep the list clean and
consistent.
Type of change
- [X] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Closes#14674.
This PR improves RAPTOR configuration and tree construction while
preserving the existing RAPTOR behavior as the default.
RAPTOR currently builds summary layers with the original UMAP + GMM
clustering path. This PR keeps that default path, and adds:
- A hidden backend tree-builder option:
- `tree_builder="raptor"`: default, existing RAPTOR behavior.
- `tree_builder="psi"`: rank-aware Psi-style tree builder using original
embedding-space cosine ranking.
- A user-facing clustering method option for the default RAPTOR builder:
- `clustering_method="gmm"`: existing default.
- `clustering_method="ahc"`: agglomerative hierarchical clustering path.
- A RAPTOR UI setting for `Clustering method` and `Max cluster`.
### What changed
#### Backend
- Added `tree_builder` support for RAPTOR/Psi.
- Added `clustering_method` support for GMM/AHC.
- Kept existing RAPTOR + GMM as the default.
- Added Psi tree building from original-space cosine similarity.
- Added bucketed Psi building controls for large inputs:
- `raptor.ext.psi_exact_max_leaves`
- `raptor.ext.psi_bucket_size`
- Added method-aware RAPTOR summary metadata using existing
`extra.raptor_method`.
- Avoided adding a dedicated DB schema field for experimental method
tracking.
- Added cleanup/migration logic to avoid mixing stale RAPTOR summary
trees.
- Added defensive checks for Psi tree construction and summary failures.
#### Frontend/UI
- Added `Clustering method` in RAPTOR settings with `GMM` and `AHC`.
- Added/kept `Max cluster` in RAPTOR settings.
- Enlarged max cluster UI limit to `1024`, matching backend validation.
- Kept AHC editable even when a RAPTOR task has already finished.
- Fixed the UI save payload so `clustering_method` and `tree_builder`
are serialized through `parser_config.raptor.ext`, avoiding backend
validation errors for extra top-level RAPTOR fields.
Example saved RAPTOR config:
```json
{
"raptor": {
"max_cluster": 317,
"ext": {
"clustering_method": "ahc",
"tree_builder": "raptor"
}
}
}
Co-authored-by: CaptainTimon <CaptainTimon@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?
The document parse status was set to DONE before the document chunks
were actually retrievable from Elasticsearch/Opensearch because it did
not wait for the index refresh. This meant that it was possible that the
document parse status returned by the API was DONE but when trying to
retrieve chunks there were none. Since the index refreshes every 1
second this was quite likely to happen when wait for document parsing by
polling with a short interval and then immediately trying to retrieve
chunks once the status was DONE.
I fixed this bug and added a test case that would have caught it.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
pending_cell_images should be scoped by sheet
### What problem does this PR solve?
_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
GraphRAG feature - Part 1 - add spacy to extract entity and relation
<img width="1621" height="1288" alt="image"
src="https://github.com/user-attachments/assets/aadeddad-94da-46c6-adad-9c3784181f61"
/>
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Summary
- Wrap the `ThreadPoolExecutor` instances in `FileService.parse_docs`
and `FileService.get_files` with `with ... as exe:` blocks for
deterministic cleanup
- Replace the `concurrent.futures.ThreadPoolExecutor` in
`do_handle_task` with `asyncio.create_task(asyncio.to_thread(build_TOC,
...))`, preserving the existing parallelism with chunk insertion while
leveraging the surrounding async context
- Drop the now-unused `import concurrent` and the
`executor.shutdown(wait=False)` call in the `finally` block
Closes#14622.
No behavioral change, no public API change. Net diff: ~19 insertions /
25 deletions across two files.
## Test plan
- [ ] `uv run ruff check api/db/services/file_service.py
rag/svr/task_executor.py` passes
- [ ] Upload a multi-file batch through the chat/file endpoint and
confirm `FileService.parse_docs` still returns combined parsed text
- [ ] Trigger `FileService.get_files` via the chat reference flow with a
mix of image and non-image files; verify both `raw=True` and `raw=False`
paths return correctly
- [ ] Run a `naive`-parser document task with `toc_extraction: true` and
confirm the TOC chunk is generated and inserted exactly as before
- [ ] Run a `naive`-parser document task with `toc_extraction: false`
and confirm the path with `toc_thread = None` is unaffected
- [ ] Cancel a running task to exercise the `finally` block and confirm
cleanup still works without the executor shutdown call
---------
Co-authored-by: web-dev0521 <jasonpette1783@gmail.com>
Co-authored-by: Wang Qi <wangq8@outlook.com>
## Summary
This PR fixes the `message_fit_in()` truncation bug reported in #13607.
Changes:
- fix the user-message truncation branch to reserve room for the system
prompt token budget
- guard the zero-token edge case to avoid dividing by zero in the
truncation ratio check
- add focused regression tests covering both the user-dominant
truncation path and the zero-token boundary case
## Validation
```bash
pytest -q --noconftest test/unit_test/rag/prompts/test_generator_message_fit_in.py
```
Result: `2 passed`
Closes#13607
## Summary
This PR fully addresses all CodeRabbit review feedback and enhances the
robustness of the reranking module with 100% backward compatibility.
## Key Fixes
1. Fixed JinaRerank hardcoded base_url to support subclass endpoint
overrides
2. Corrected GPUStackRerank exception handling to use proper requests
exceptions and preserve stack traces
3. Added 30s timeout to all API calls to prevent service hanging
4. Added empty input validation for all rerank providers
5. Replaced direct dict key access with .get() to eliminate KeyError
crashes
6. Fixed _normalize_rank edge case for empty arrays
7. Implemented missing functionality for Ai302Rerank
8. Standardized type hints and fixed typo issues
## Compatibility
- No breaking changes to any existing functionality
- All rerank providers work as originally intended
- Fully compatible with existing configurations and workflows
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring
---------
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
## What
Widen the keyword delimiter in `rag/svr/task_executor.py`:
both `build_chunks` (LLM `keyword_extraction` cache parsing) and
`run_dataflow` (chunk-level `keywords` ingestion) now split on
`, ， ; ； 、 \r \n` instead of only ASCII comma.
## Why
`rag/prompts/keyword_prompt.md` instructs the LLM:
> The keywords are delimited by ENGLISH COMMA.
In practice, Chinese-leaning models (Qwen / Tongyi-Qianwen, GLM,
etc.) frequently ignore this instruction when the source content is
Chinese and emit Chinese commas (`，`) instead. Result:
`cached.split(",")` sees the full LLM output as a *single* keyword.
Repro: `auto_keywords>=4` + Chinese docs + `qwen-plus@Tongyi-Qianwen`.
We observed entries in `important_kwd` like
`"功能介绍，配置说明，参数详解，问题排查"` — one bucket instead of four.
## Impact
- Silent data-quality bug; no exception thrown.
- BM25 `important_kwd^30` boost effectively stops firing — the
indexed term is the whole list, never matches user query tokens.
- Any downstream aggregating `important_kwd` (tagging, analytics,
candidate-keyword review UIs) sees garbage.
## Compatibility
- Pure widening of the splitter; ASCII-comma-only outputs continue
to work identically.
- No schema / API change.
## Test plan
Manually verified against `qwen-plus@Tongyi-Qianwen` with
`auto_keywords=10` on Chinese .txt files:
- Before: `important_kwd` contains one element per chunk that is the
full LLM string with `，`-separated phrases inside.
- After: `important_kwd` contains N elements, one per phrase, as the
LLM intended.
### What problem does this PR solve?
`retrieval_by_children()` in `rag/nlp/search.py` crashes with a
`TypeError: 'NoneType' object is not subscriptable` when a parent
("mom") chunk referenced by child chunks is missing from the index.
This happens when the index is in an inconsistent state — for example
after a partial re-index, a document deletion that didn't clean up all
children, or a race condition during ingestion. `dataStore.get()`
returns `None` for the missing parent, and the subsequent access to
`chunk["content_with_weight"]` raises a `TypeError`.
**Stack trace:**
```
TypeError: 'NoneType' object is not subscriptable
File "rag/nlp/search.py", line 792, in retrieval_by_children
"content_with_weight": chunk["content_with_weight"],
```
### Type of change
- [x] Bug Fix
### Fix
When `dataStore.get()` returns `None` for a parent chunk, fall back to
using the child chunks directly and continue processing the remaining
parents. This preserves retrieval results for all other chunks rather
than aborting the entire query with an exception.
```python
chunk = self.dataStore.get(id, idx_nms[0], [ck["kb_id"] for ck in cks])
if chunk is None:
chunks.extend(cks)
continue
```
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
### What problem does this PR solve?
Multiple `requests.post()` calls across the LLM integration layer lack a
`timeout` parameter. Without a timeout, a single unresponsive upstream
service can block the calling thread **indefinitely**, eventually
exhausting the thread pool and degrading the entire system.
This is a well-known issue — Python's `requests` library defaults to
`timeout=None` (infinite wait), and [the library docs explicitly
recommend](https://requests.readthedocs.io/en/latest/user/advanced/#timeouts)
always setting a timeout.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Change
Added `timeout` to all `requests.post()` calls missing it:
| File | Calls fixed | Timeout |
|------|-------------|---------|
| `rag/llm/rerank_model.py` | 9 | 30s |
| `rag/llm/embedding_model.py` | 8 | 30s |
| `rag/llm/cv_model.py` | 3 | 60s |
| `rag/llm/tts_model.py` | 2 | 60s |
| `rag/llm/sequence2txt_model.py` | 2 | 60s |
Embedding/rerank calls use 30s (lightweight API calls). Vision, TTS, and
audio transcription use 60s (heavier workloads with file uploads).
Note: other files in the codebase (e.g. `check_minio_alive`,
`check_ragflow_server_alive`) already use `timeout=10`, so this PR
brings the LLM layer in line with existing practice.
Signed-off-by: Ricardo-M-L <Sibyl_Hartmanbnb@webname.com>
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
## Summary
- `CvModel["Bedrock"]` was absent from `rag/llm/cv_model.py`, causing
`model_instance()` to return `None` when a Bedrock model was used as a
PDF parser — even after correct model resolution.
- This PR adds `BedrockCV`, enabling Bedrock vision models (e.g.
`amazon.nova-pro-v1:0`, `anthropic.claude-3-5-sonnet`) to be used as PDF
parsers.
## What problem does this PR solve?
When a Bedrock model is selected as the PDF parser in a knowledge base,
ingestion failed with:
```
'LiteLLMBase' object has no attribute 'describe_with_prompt'
```
The root cause: `LiteLLMBase` (the Bedrock chat implementation) was the
only registered handler for the Bedrock factory. It does not implement
`describe_with_prompt`. `CvModel` had no Bedrock entry, so
`model_instance()` returned `None` for `image2text` requests.
## Type of change
- [x] New Feature (non-breaking change which adds functionality)
## Changes
**`rag/llm/cv_model.py`**
Adds `BedrockCV(Base)` with `_FACTORY_NAME = "Bedrock"`:
- Uses `litellm.completion` with the `bedrock/` prefix (consistent with
`LiteLLMBase`)
- Parses AWS credentials from the JSON key assembled by `add_llm`
(`auth_mode`, `bedrock_ak`, `bedrock_sk`, `bedrock_region`,
`aws_role_arn`)
- Supports three auth modes: `access_key_secret`, `iam_role` (via STS
`assume_role`), and default credential chain (IRSA, instance profile)
- Implements `describe_with_prompt` and `describe`
## Test plan
- [ ] Configure a Bedrock vision model (e.g. `amazon.nova-pro-v1:0`)
with valid AWS credentials
- [ ] Select it as PDF parser in a knowledge base
- [ ] Verify ingestion of a PDF document completes without errors
- [ ] Verify `CvModel["Bedrock"]` resolves to `BedrockCV`
🤖 Generated with [Claude Code](https://claude.ai/claude-code)
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
### What problem does this PR solve?
The table file parser (CSV/Excel) currently treats all columns
identically — every column is both vectorized (embedded in chunk text)
and stored as filterable metadata. There's no way for users to control
which columns should be searchable by semantic meaning versus which
should only be filterable attributes.
For example, when ingesting a news articles CSV with columns like title,
content, country, category, source, etc., the embedding includes
metadata fields like country: Brazil and source: Reuters in the chunk
text, which dilutes the semantic quality of the embedding without adding
retrieval value.
The RDBMS connector (MySQL/PostgreSQL) already supports content_columns
/ metadata_columns, but this capability was missing for file-based table
ingestion.
This PR adds column-level control (vectorize / metadata / both) for the
table file parser, following RAGFlow's existing patterns.
Backward compatible: Datasets without table_column_roles or with
table_column_mode: auto behave exactly as before (all columns = both).
### Type of change
- [x] New Feature (non-breaking change which adds functionality)
### What problem does this PR solve?
HuggingfaceRerank.post() unconditionally prepends `http://` to base_url,
which already contains a protocol. This creates invalid URLs like
http://http://127.0.0.1:8080/rerank, breaking all requests. The fix
normalizes URL handling to match the rest of the codebase, removing
redunant `http://`.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Related Issues
- #7318
- #7796
---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@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?
Update mapping.json to treat id as a keyword.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What
19 methods across `rag/llm/chat_model.py` and `rag/llm/cv_model.py`
declare `gen_conf={}` (or `gen_conf: dict = {}`) as a parameter default
and then mutate `gen_conf` in place — typically `del
gen_conf["max_tokens"]`, `gen_conf["penalty_score"] = ...`, or
`gen_conf.pop(...)` as part of provider-specific normalization.
### The two bugs in this pattern
**1. Mutable default argument (Python footgun).** Python evaluates
default values **once** at function-definition time, so the single `{}`
dict is *shared* across every caller that doesn't pass `gen_conf`. The
first such call's mutations leak into the default seen by every
subsequent call.
```python
# Before
def chat_streamly(self, system, history, gen_conf={}, **kwargs):
if "max_tokens" in gen_conf:
del gen_conf["max_tokens"] # mutates the SHARED default dict
...
```
After call N with `max_tokens` set, call N+1 that omits `gen_conf` no
longer sees `max_tokens` — even though the caller never touched it.
**2. Caller-dict pollution.** When the caller *does* pass a `gen_conf`
dict, the same in-place mutations modify the caller's dict. A reused
`gen_conf` (very common for chat-loop callers that build the config once
and pass it on every turn) silently loses `max_tokens`,
`presence_penalty`, etc. after the first round.
### The fix
In every affected method:
- Change `gen_conf={}` (or `gen_conf: dict = {}`) → `gen_conf=None`.
- Add `gen_conf = dict(gen_conf or {})` as the first statement of the
body so all subsequent mutations operate on a fresh local copy.
```python
# After
def chat_streamly(self, system, history, gen_conf=None, **kwargs):
gen_conf = dict(gen_conf or {})
if "max_tokens" in gen_conf:
del gen_conf["max_tokens"] # local copy — safe
...
```
This is byte-for-byte identical provider-side behavior for callers that
already pass a fresh `gen_conf` per call. The new `dict(...)` copy is
O(small constant) per call.
### Files changed
- `rag/llm/chat_model.py` — 17 methods
- `rag/llm/cv_model.py` — 2 methods
### Tests
Adds `test/unit_test/rag/llm/test_gen_conf_no_mutable_default.py` — an
`ast`-based regression guard that walks both modules and asserts no
parameter named `gen_conf` ever has a mutable literal (`{}` or `[]`) as
its default. The test caught **five additional `gen_conf: dict = {}`
sites** that an initial `gen_conf={}` text grep had missed (annotated
parameters with whitespace), and would fail again if the pattern is ever
reintroduced.
```
$ pytest test/unit_test/rag/llm/test_gen_conf_no_mutable_default.py -v
============================== 3 passed in 0.04s ===============================
```
`ruff check` passes on all touched files.
### Notes
- This PR is intentionally focused on **just** the `gen_conf` default +
copy fix. There's a related (but separate) `history.insert(0, ...)`
pattern in the same files that mutates the caller's history list in 12
places — left for a follow-up so this PR stays mechanical and easy to
review.
### Latest revision (`700bb54a7`) — addresses CodeRabbit review
- Type annotation: `gen_conf: dict = None` → `gen_conf: dict | None =
None` (5 occurrences in `chat_model.py`). The old annotation was a
static-checker mismatch since `None` isn't a `dict`.
- Regression test: the AST check accessed `default.keys` directly.
`ast.List` has no `.keys` attribute — a future `gen_conf=[]` would crash
with `AttributeError` instead of being caught. Use `getattr` for both
`.keys` (Dict) and `.elts` (List). Manually verified the updated check
correctly catches both `gen_conf={}` and `gen_conf=[]` while ignoring
`gen_conf=None` and non-empty literals.
---------
Co-authored-by: Ricardo <ricardo@example.com>
## Summary
- When KB retrieval fails (e.g. ES `AssertionError` on empty
`index_names`), `kbinfos` falls back to a dict without a `total` key
- `_async_update_chunk_info` then iterates over `chunk_info.keys()`
(which includes `total`) and tries `kbinfos['total']`, raising a
`KeyError`
- This error surfaces when using Tavily web retrieval in a chat with no
knowledge base attached
## Changes
- Add `'total': 0` to all default `kbinfos` dicts in
`_retrieve_information`
- Add `setdefault('total', 0)` guard after successful KB retrieval to
handle cases where the retrieval result omits the key
- Accumulate `total` correctly in the merge branch of
`_async_update_chunk_info`
## Test plan
- [ ] Start a chat with Tavily configured and no knowledge base
- [ ] Verify no `KeyError: 'total'` is raised
- [ ] Verify Tavily results are returned correctly
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Fixes#14360
## Problem
When the same blob storage bucket is connected to multiple knowledge
bases (each through a different data source connector), the sync
pipeline hashes only the blob path
(`bucket_type:bucket_name:object_key`) to derive the document ID. Every
connector pointing at the same bucket therefore produces **identical
IDs** for the same object. The collision guard in
`FileService.upload_document` then fires for the second knowledge base:
```
Existing document id collision with another knowledge base; skipping update.
```
This makes it impossible to index the same bucket into more than one KB
simultaneously.
## Solution
Include `connector_id` in the hash input so that each connector produces
a distinct document ID even when the underlying blob path is identical:
```python
# Before
"id": hash128(doc.id),
# After
"id": hash128(f"{task['connector_id']}:{doc.id}"),
```
Because each KB connection uses its own connector (with a unique
`connector_id`), documents are now namespaced per connector and no
collision occurs.
**Note:** This is a breaking change for existing synced data sources.
After upgrading, a re-sync will create new documents with the updated ID
format. Old documents (indexed under the previous format) will remain in
the database but can be manually deleted or cleaned up via a re-sync
with reindex enabled.
## Testing
- Verified that the one-line change produces unique IDs for two
connectors pointing at the same S3 path.
- Existing unit test
`test_upload_document_skips_cross_kb_document_id_collision` continues to
pass — the collision guard in `FileService` is still valid for genuinely
colliding IDs from other sources.
---------
Co-authored-by: octo-patch <octo-patch@github.com>
### What problem does this PR solve?
Do not bypass threshold for rerank when metadata filter is enabled
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### What problem does this PR solve?
Fixes#14651.
`kb_prompt()` in `rag/prompts/generator.py` crashes with
`AttributeError: 'NoneType' object has no attribute 'items'` during
agent citation generation when a retrieved chunk carries
`document_metadata: null`.
**Root cause.** The crash happens at `rag/prompts/generator.py:132-133`:
```python
meta = ck.get("document_metadata", {})
for k, v in meta.items():
```
`dict.get(key, default)` only returns the default when the key is
*missing*. When the key is present with an explicit `None` value,
`.get()` returns `None`, and `.items()` crashes.
**How the chunk gets `None`.** It's a round-trip inside RAGFlow itself,
not bad input from retrieval:
1. The agent stores retrieved chunks via `agent/canvas.py:814`, which
routes them through `chunks_format()`.
2. `rag/prompts/generator.py:61` canonicalizes the field with
`chunk.get("document_metadata")` (no default), so chunks without
metadata become `{"document_metadata": None, ...}`.
3. `agent/component/agent_with_tools.py:314` feeds those canonicalized
chunks back into `kb_prompt()` for citation generation, and
`.get("document_metadata", {})` no longer protects us.
**Fix.** One-line change at `rag/prompts/generator.py:132`: use
`ck.get("document_metadata") or {}` so an explicit `None` is also
coerced to `{}`.
The line-61 `None` is intentionally part of the API/UI contract — the
frontend handles it via optional chaining
(`web/src/components/markdown-content/index.tsx:184`,
`web/src/pages/next-search/search-view.tsx:217`) — so the fix belongs at
the consumer, not the producer.
### 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):
## Summary
Fixes file collision between different datasets when using Azure Blob
storage (SPN or SAS authentication).
## Bug
azure_spn_conn.py and zure_sas_conn.py ignored the ucket parameter
entirely, storing all files flat with just the filename. This caused
files with the same name from different datasets (knowledge bases) to
overwrite each other.
## Fix
Prepend bucket/ as a path prefix in all methods (put,
m, get, obj_exist, get_presigned_url, health) to match the behavior of
MinIO and S3 implementations.
## Changes
- **rag/utils/azure_spn_conn.py**: Added {bucket}/ prefix to file paths
in all operations
- **rag/utils/azure_sas_conn.py**: Same fix applied for consistency
(also noted in the original issue)
## Testing
Manual verification: files from different datasets now stored under
distinct bucket/ prefixes, preventing collisions.
Fixes#14159
Co-authored-by: Hunter <hunter@yitong.ai>
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
