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### What problem does this PR solve? Closes #16418. `scholarly.search_pubs(...)` returns a **lazy generator**, but `agent/tools/googlescholar.py` treated it as a re-iterable, bounded list: ```python scholar_client = scholarly.search_pubs(kwargs["query"], ...) # lazy generator self._retrieve_chunks(scholar_client, ...) # (1) iterates -> exhausts it self.set_output("json", list(scholar_client)) # (2) already empty -> [] ``` 1. **`json` output was always empty.** `_retrieve_chunks` iterates `scholar_client`, exhausting the generator; `list(scholar_client)` then returns `[]`. 2. **`top_n` was never applied.** Unlike `ArXiv` (`max_results=self._param.top_n`), the unbounded generator was passed straight to `_retrieve_chunks`, which has no internal limit — so the tool kept paginating well past Top N (until an error, rate-limit/block, or `COMPONENT_EXEC_TIMEOUT`). ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) ### Changes - Materialize at most `top_n` results once with `itertools.islice`, and reuse that list for both `_retrieve_chunks` and the `json` output. - Add regression tests (`test/unit_test/agent/component/test_googlescholar.py`, stubbing `scholarly.search_pubs`) covering the `top_n` bound, the non-empty `json` output, and the empty-query short-circuit. Verified: against `main` the new tests fail with `assert 30 == 5` (top_n ignored) and `assert 0 == 5` (empty json); with this fix all pass. Backend-only. --------- Co-authored-by: Zhichang Yu <yuzhichang@gmail.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=${COMPOSE_PROFILES},tei-cpu
TEI_MODEL=BAAI/bge-small-en-v1.5
RAGFLOW_IMAGE=infiniflow/ragflow:v0.26.2 #Replace with the image you are using
Start the container（wait two minutes）:
docker compose -f docker/docker-compose.yml up -d
(3). Test Elasticsearch
a) Run sdk tests against Elasticsearch:
export HTTP_API_TEST_LEVEL=p2
export HOST_ADDRESS=http://127.0.0.1:9380 # Ensure that this port is the API port mapped to your localhost
pytest -s --tb=short --level=${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