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de18313f97
### What problem does this PR solve?
`POST /api/v1/datasets/{dataset_id}/documents/stop`
(`stop_parse_documents`) cancels parsing tasks and sets `run` to
`CANCEL`, but it does **not** remove chunks already indexed in the doc
store or reset `progress` / `chunk_num`. REST callers can end up with a
“cancelled” document that still returns partial chunks in `GET
.../chunks` and in retrieval.
Legacy `DELETE /api/v1/datasets/{dataset_id}/chunks` (`stop_parsing`)
already performs full cleanup: it resets counters and calls
`docStoreConn.delete`. This PR aligns the newer stop endpoint with that
behavior so both paths leave the dataset consistent.
Fixes [#15788](https://github.com/infiniflow/ragflow/issues/15788).
### 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):
### Changes
- Update `stop_parse_documents` in `document_api.py` to reset `progress`
and `chunk_num` to `0` and delete partial chunks via
`docStoreConn.delete` after `cancel_all_task_of`.
- Add unit test `test_stop_parse_documents_cleans_partial_chunks` to
assert counters reset and doc store delete is invoked.
### Test plan
- [x] Unit test: `pytest
test/testcases/test_http_api/test_file_management_within_dataset/test_doc_sdk_routes_unit.py::TestDocRoutesUnit::test_stop_parse_documents_cleans_partial_chunks
-v`
- [ ] Manual: upload a slow document, start parse, call `POST
.../documents/stop` while `RUNNING`, verify `GET .../chunks` returns
zero chunks and UI `chunk_count` is 0
- [ ] Control: legacy `DELETE .../chunks` behavior unchanged
---------
Co-authored-by: Wang Qi <wangq8@outlook.com>
(1). Deploy RAGFlow services and images
https://ragflow.io/docs/build_docker_image
(2). Configure the required environment for testing
Install Python dependencies (including test dependencies):
uv sync --python 3.13 --only-group test --no-default-groups --frozen
Activate the environment:
source .venv/bin/activate
Install SDK:
uv pip install sdk/python
Modify the .env file: Add the following code:
COMPOSE_PROFILES=${COMPOSE_PROFILES},tei-cpu
TEI_MODEL=BAAI/bge-small-en-v1.5
RAGFLOW_IMAGE=infiniflow/ragflow:v0.25.6 #Replace with the image you are using
Start the container（wait two minutes）:
docker compose -f docker/docker-compose.yml up -d
(3). Test Elasticsearch
a) Run sdk tests against Elasticsearch:
export HTTP_API_TEST_LEVEL=p2
export HOST_ADDRESS=http://127.0.0.1:9380 # Ensure that this port is the API port mapped to your localhost
pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
b) Run http api tests against Elasticsearch:
pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api
(4). Test Infinity
Modify the .env file:
DOC_ENGINE=${DOC_ENGINE:-infinity}
Start the container:
docker compose -f docker/docker-compose.yml down -v
docker compose -f docker/docker-compose.yml up -d
a) Run sdk tests against Infinity:
DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_sdk_api
b) Run http api tests against Infinity:
DOC_ENGINE=infinity pytest -s --tb=short --level=${HTTP_API_TEST_LEVEL} test/testcases/test_http_api