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### 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)
(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=${COMPOSE_PROFILES},tei-cpu
TEI_MODEL=BAAI/bge-small-en-v1.5
RAGFLOW_IMAGE=infiniflow/ragflow:v0.25.2 #Replace with the image you are using
Start the container（wait two minutes）:
docker compose -f docker/docker-compose.yml up -d
(3). Test Elasticsearch
a) Run sdk tests against Elasticsearch:
export HTTP_API_TEST_LEVEL=p2
export HOST_ADDRESS=http://127.0.0.1:9380 # Ensure that this port is the API port mapped to your localhost
pytest -s --tb=short --level=${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