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### What problem does this PR solve? Implements automatic adjustment of knowledge base chunk recall weights based on user feedback (upvotes/downvotes). When users upvote or downvote a response, the system locates the corresponding knowledge snippets and adjusts their recall weight to improve future retrieval quality. **Closes #12670** **How it works:** 1. User upvotes/downvotes a response via `POST /thumbup` 2. System extracts chunk IDs from the conversation reference 3. For each referenced chunk: - Reads current `pagerank_fea` value from document store - Increments (+1) for upvote or decrements (-1) for downvote - Clamps weight to [0, 100] range - Updates chunk in ES/Infinity/OceanBase 4. Future retrievals score these chunks higher/lower based on accumulated feedback **Files changed:** - `api/db/services/chunk_feedback_service.py` - New service for updating chunk pagerank weights - `api/apps/conversation_app.py` - Integrated feedback service into thumbup endpoint - `test/testcases/test_web_api/test_chunk_feedback/` - Unit tests ### Type of change - [x] New Feature (non-breaking change which adds functionality) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Chat message feedback now updates per-chunk relevance weights (feature-flag gated), with configurable weighting and atomic updates across storage backends. * **Bug Fixes** * Stricter validation for message feedback inputs and more robust handling of feedback transitions. * **Tests** * Expanded test coverage for chunk-feedback behavior, weighting strategies, storage backends, and thumb-flip scenarios. * **Chores** * CI workflow extended to run the new chunk-feedback web API tests. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: mkdev11 <YOUR_GITHUB_ID+MkDev11@users.noreply.github.com> Co-authored-by: mkdev11 <MkDev11@users.noreply.github.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 3.12 --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=${COMPOSE_PROFILES},tei-cpu
TEI_MODEL=BAAI/bge-small-en-v1.5
RAGFLOW_IMAGE=infiniflow/ragflow:v0.24.0 #Replace with the image you are using
Start the container（wait two minutes）:
docker compose -f docker/docker-compose.yml up -d
(3). Test Elasticsearch
a) Run sdk tests against Elasticsearch:
export HTTP_API_TEST_LEVEL=p2
export HOST_ADDRESS=http://127.0.0.1:9380 # Ensure that this port is the API port mapped to your localhost
pytest -s --tb=short --level=${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