## Summary
- Preserve request-scoped system variables such as files and user IDs
during Canvas execution.
- Persist conversation history, turn counts, and tool memory in the
session DSL across turns.
- Parse agent uploads into `sys.files` and align system variable
rendering with Python.
## Testing
- `bash build.sh --test ./internal/agent/...`
- `bash build.sh --test ./internal/service/...`
<img width="1896" height="1232" alt="image"
src="https://github.com/user-attachments/assets/b420cd97-53c3-470f-a3e1-d39cea26a213"
/>
### What problem does this PR solve?
Await Response incorrectly consumed the initial `sys.query` as its
input, so the first user interaction was skipped.
This change makes Await Response wait for actual user input while
preserving the existing initial-query behavior for the Begin node.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
## Summary
- Add `GetInputForm()` for `ListOperationsComponent` to expose `Query`
input field in debug UI
- Add `GetInputForm()` for `VariableAggregatorComponent` to expose
`Variables` input field in debug UI
## Test
- Verify input form fields render correctly for both components in the
debug UI
Both backends serve GET /api/v1/language. Frontend calls it once and
caches. By this way, front end can know the backend is go or python and
thus can determine which part of logic to load.
---------
Co-authored-by: Claude <noreply@anthropic.com>
## What
`meta_filter()`'s in-memory `filter_out()` helper has two related bugs
in how it coerces `input`/`value` for comparison operators (`=`, `≠`,
`>`, `<`, `≥`, `≤`):
**1. Asymmetric commit on partial `literal_eval` failure.** The original
code:
```python
input = ast.literal_eval(input)
value = ast.literal_eval(value)
```
runs as two separate statements inside one `try`. If the first succeeds
and the second raises, the first assignment already committed — `input`
and `value` end up as different types, and the subsequent `.lower()`
case-folding silently no-ops for whichever side didn't get lowered as a
string. Concretely: metadata cell `"None"` is a valid Python literal
(`ast.literal_eval("None")` → `None`), but a query value `"none"`
(lowercase) is not — so `status = "none"` never matches a cell whose
value is `"None"`, even though the intended semantics are
case-insensitive.
**2. `value` mutated in place, reused across dict entries.**
`filter_out(v2docs, operator, value)` loops over every `(input, docids)`
pair in `v2docs` and coerces `value` inside the loop body without
resetting it — so once one entry's `literal_eval(value)` succeeds and
rebinds `value` to a non-string, every later entry in the same call
compares against that already-coerced leftover instead of the original
filter value.
## Fix
- Commit both `literal_eval` results together via tuple assignment
(`input, value = ast.literal_eval(input), ast.literal_eval(value)`), so
a failure on either side leaves both operands in their pre-coercion form
instead of a mismatched mix.
- Save the original `value` before the loop and reset it at the top of
each iteration, so per-entry coercion never leaks into the next entry.
## Testing
Added 3 tests to
`test/unit_test/common/test_metadata_filter_operators.py` covering both
symptoms (case-insensitive match against a metadata cell that's a Python
keyword literal, both `=` and `≠`; a numeric `>` comparison unaffected
by an earlier dict entry having coerced the query value). Confirmed red
on `common/metadata_utils.py` at HEAD (`git stash` the fix, tests fail
with the exact symptom described above), green after. Full existing
`test_metadata_filter_operators.py` suite (22/22, including the 3 new
tests) passes. `ruff check` and `ruff format --check` clean on both
touched files.
Sandbox note: this environment has no network access to install
`pytest`/`pytest-asyncio`, so tests were run by importing the test
module and invoking each `test_*` function directly (same approach as
prior PRs from this account against this repo, e.g. #16949).
`test_apply_semi_auto_meta_data_filter.py` (the other file exercising
`meta_filter` indirectly through `apply_meta_data_filter`) needs
`pytest-asyncio` + heavier mocking and wasn't run, but it exercises
`apply_meta_data_filter`'s async/LLM-filter-generation path, not
`filter_out`'s coercion logic touched here.
Cross-referenced open PR #16833 (also touches
`common/metadata_utils.py`) — confirmed via `gh pr diff` it only touches
`convert_conditions`/operator-alias normalization and
`apply_meta_data_filter`'s `None`-vs-`["-999"]` sentinel logic, not
`filter_out`'s comparison-coercion code path. No overlap.
---
This PR was drafted with AI assistance (Claude); I reviewed the change,
independently reproduced both symptoms, and take responsibility for it.
Signed-off-by: chuenchen309 <48723787+chuenchen309@users.noreply.github.com>
### What problem does this PR solve?
When a table dataset's `field_map` is missing or stale,
`aggregate_table_doc_metadata` falls back to probing chunk dictionaries
for each column's Elasticsearch field key. It currently performs that
probe only once, against the first dictionary chunk, and caches `(None,
"none")` if the field is absent there.
Sparse table rows commonly omit empty columns. If the first row has no
`notes` field but a later row contains `notes_raw`, the cached miss
causes every later row to be skipped and the document-level `notes`
metadata is silently lost. The result depends only on row order:
```python
chunks = [{}, {"notes_raw": "Handle with care"}]
aggregate_table_doc_metadata(chunks, task) # before: {}
aggregate_table_doc_metadata(list(reversed(chunks)), task)
# before: {"notes": ["Handle with care"]}
```
This was also identified in CodeRabbit's review of the merged
table-metadata implementation in #15780, but remained unfixed after that
PR merged:
https://github.com/infiniflow/ragflow/pull/15780#pullrequestreview-4448490676
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
### Fix
When the initial lookup found no key for a column, retry the existing
`_resolve_es_chunk_field_key` against the current chunk. Cache the first
successful resolution so subsequent rows retain the existing fast path.
Field-map-backed columns and columns found in the first chunk are
unchanged.
### Testing
- Added `test_aggregate_auto_mode_probes_later_sparse_chunks` with an
empty first row and a populated second row.
- Confirmed red→green: before the fix the assertion received `{}`; after
the fix it receives `{"notes": ["Handle with care"]}`.
- Full existing `test_table_metadata_aggregation.py`: **15 passed**.
- `ruff check` and `ruff format --check`: clean.
- `compileall` for both changed files: clean.
The local test environment did not contain the repository's full service
dependency set and had a corrupt pre-existing NLTK `wordnet.zip`. The
test module does not use those services or corpora, so the run stubbed
only `common.settings` engine flags, `json_repair`, and the global
conftest's NLTK resource lookup; the production module and aggregation
tests themselves ran unchanged.
### Duplicate-work check
Checked all currently open PRs (including changed file paths) and found
none touching `rag/utils/table_es_metadata.py` or its aggregation test.
The earlier #15780 review is historical context, not active competing
work.
### Disclosure
AI-assisted (Codex): the candidate came from an AI-assisted review
queue. I independently reproduced the order-dependent data loss against
the real module, checked the historical review and all open PR file
paths, and ran the regression plus full existing test file before
submitting.
Signed-off-by: chuenchen309 <48723787+chuenchen309@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
### Summary
fix: lefthook deps serialization
Extract npm ci guard into web-deps job; use native deps for
serialization instead of a mkdir mutex that could leave stale locks on
interrupted commits. Also use single quotes in echo to work around
lefthook v2.1.10 stripping double quotes on Windows.
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
## What
Adds a reranker connector for the **Bedrock** factory, which previously
offered
chat/embedding/CV models but no reranker — selecting a Bedrock rerank
model
raised `Factory not in rerank model`.
## How
`BedrockRerank` calls the `bedrock-agent-runtime` Rerank API. It reuses
the same
JSON key protocol as `BedrockEmbed` (`auth_mode` / `bedrock_region` /
`bedrock_ak` / `bedrock_sk`, with `access_key_secret` / `iam_role` /
`assume_role` modes). Documents are truncated to the model window
(Cohere Rerank
v3.5 ~2k of its shared 4k window, Amazon Rerank v1 8k) on top of
Bedrock's own
internal truncation. Scores are returned in `[0, 1]`, so the shared
`Base.similarity` normalization applies unchanged.
Verified against `amazon.rerank-v1:0` and `cohere.rerank-v3-5:0` in
`eu-central-1`.
> Note: this PR adds the connector only. Bedrock rerank models can be
selected by
> adding the relevant entries to `conf/llm_factories.json` under the
Bedrock
> provider; that catalog change is intentionally left out of this PR.
## Tests
`test/unit_test/rag/llm/test_bedrock_rerank.py` — boto3 is mocked (no
AWS call):
score-by-index mapping, per-model document truncation, model ARN
construction,
auth-mode validation and the empty-input short-circuit. `pytest` green
alongside
the existing reranker normalization suite.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
### Summary
1. Remove dead code (replaced by builtin ingestion pipeline)
2. Refactor (move document parsing progress from http api into ingestion
executor)
## What this PR does
Removes the self-concatenation of the vision model response in the video
parsing path, so each generated video description is tokenized and
indexed exactly once.
A focused regression test exercises the public `picture.chunk` video
path with a mocked vision model and asserts that the returned
description is passed to `tokenize` once without duplication.
## Root cause
The original video parsing implementation used:
```python
ans += "\n" + ans
tokenize(doc, ans, ...)
```
This duplicates the same model response. The adjacent image path
combines two distinct values (`OCR text + vision description`); the
video path has only the model response, so concatenating it with itself
is an unintended copy/paste error from that image logic.
## Impact
Before this fix, every successfully parsed video stored repeated text,
increasing token and embedding input and potentially distorting indexed
chunk content and retrieval scoring.
## Compatibility
The change affects only the video branch in `rag/app/picture.py`. Image
parsing, model invocation, prompts, callbacks, and error handling remain
unchanged.
## Validation
- `pytest --confcutdir=test/unit_test/rag/app
test/unit_test/rag/app/test_picture_video.py -q`: 1 passed
- Ruff check: passed
- Ruff format check for the new test: passed
- `git diff --check`: passed
Closes#16846.
---------
Co-authored-by: openhands <openhands@all-hands.dev>
## What this PR does
Adds support for Alibaba Cloud's hosted Fun-ASR-Flash snapshots to the
existing Tongyi-Qianwen speech-to-text provider.
- registers `fun-asr-flash-2026-06-15` as a speech-to-text model;
- routes only `fun-asr-flash*` models to the documented workspace-native
multimodal-generation endpoint;
- supports local audio through size-checked data URIs as well as
URL/data-URI inputs;
- uses the documented SSE response mode for incremental streaming
transcription;
- closes the streamed HTTP response on completion, failure, or early
consumer cancellation;
- preserves the existing `dashscope.MultiModalConversation` path for all
other Qwen audio models;
- keeps RAGFlow's existing synchronous and streaming adapter interfaces.
## Why
Fun-ASR-Flash does not use the legacy Qwen audio request shape currently
used by `QWenSeq2txt`. Its synchronous API expects `input_audio` at:
`/api/v1/services/aigc/multimodal-generation/generation`
Without a narrowly scoped adapter path, the hosted model cannot be
selected successfully through RAGFlow's Tongyi-Qianwen speech-to-text
provider.
Closes#16843.
## Compatibility
The new behavior is gated by the `fun-asr-flash` model-name prefix.
Existing Qwen audio models continue through the original code path
unchanged.
## Validation
- `pytest test/unit_test/rag/llm/test_sequence2txt_model.py`: 10 passed
- Ruff check: passed
- Ruff format check: passed
- `llm_factories.json` validation: passed
- Real hosted-API validation with WAV audio
- Real RAGFlow upload/indexing validation with MP3 audio
The unit tests cover the native Fun-ASR-Flash request, regression
behavior for the legacy Qwen path, SSE streaming, and early response
cleanup.
## Documentation
- https://help.aliyun.com/document_detail/2979031.html
- https://help.aliyun.com/document_detail/2869541.html
### Why a dedicated adapter path is necessary (official evidence)
Alibaba Cloud's [Fun-ASR RESTful API
reference](https://help.aliyun.com/en/model-studio/fun-asr-recorded-speech-recognition-http-api)
makes the incompatibilities with RAGFlow's existing Qwen audio path
explicit:
| Adapter change | Official API requirement | Why the existing path is
insufficient |
| --- | --- | --- |
| Call the workspace-native HTTP endpoint | The Fun-ASR-Flash
synchronous section states that SDK calls are not supported and
specifies `POST /api/v1/services/aigc/multimodal-generation/generation`.
| The existing adapter calls `dashscope.MultiModalConversation`, so a
direct HTTP path is required. |
| Use the `input_audio` message shape | `input.messages`, `content`,
`type: input_audio`, `input_audio`, and `input_audio.data` are
documented as required for an audio request. | The existing Qwen path
sends the legacy `audio` content shape, which does not match this API
contract. |
| Send `parameters.format` | The request schema marks `parameters` and
`format` as **Required**, and says the value must match the actual audio
format. | The legacy request has no Fun-ASR-Flash `parameters.format`
field, so the adapter must derive and send it. |
| Encode local files as Data URIs | `input_audio.data` accepts either a
public URL or a Base64 Data URI; the reference gives the exact
`data:{MIME_TYPE};base64,...` form. | RAGFlow supplies local file paths,
which the remote API cannot read directly. |
| Parse `output.text` | The documented non-streaming response returns
the accumulated transcription in `output.text`. | The legacy Qwen
response parser reads `output.choices[].message.content`, so a separate
response parser is required. |
| Enforce the Base64 input limit | The reference requires the
Base64-encoded audio to remain within the 10 MB input limit. | The
adapter checks encoded size before reading/sending local audio and
directs oversized inputs to the existing public-URL path. |
| Use SSE for streaming | The reference specifies `X-DashScope-SSE:
enable` and documents intermediate and final SSE events. | The adapter
parses those events instead of wrapping one blocking response as a
synthetic stream. |
| Release streamed responses | Streaming responses must be closed when
iteration completes or stops early. | A `finally` cleanup releases the
HTTP response on completion, errors, and consumer cancellation. |
`sample_rate` is documented as **Optional**. The implementation omits it
instead of declaring a fixed value that may not match remote or
compressed audio.
The [official speech-to-text model
list](https://help.aliyun.com/en/model-studio/asr-model/) separately
confirms that `fun-asr-flash-2026-06-15` is an offline HTTP model with a
five-minute audio limit.
---------
Signed-off-by: LauraGPT <LauraGPT@users.noreply.github.com>
Co-authored-by: openhands <openhands@all-hands.dev>
Co-authored-by: LauraGPT <LauraGPT@users.noreply.github.com>
