2026-04-07 11:33:05 +08:00
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#
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# Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""Unit tests for api.apps.sdk.doc_validation module."""
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from unittest.mock import Mock
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2026-05-28 11:13:01 +08:00
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import pytest
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from pydantic import ValidationError
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from api.utils.pagination_utils import REST_API_MAX_PAGE_SIZE, validate_rest_api_page_size
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2026-04-07 11:33:05 +08:00
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from api.utils.validation_utils import (
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2026-05-28 11:13:01 +08:00
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ListDatasetReq,
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ListFileReq,
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Feature/table parser column roles (#13710)
### 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)
2026-05-11 07:06:04 +05:00
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ParserConfig,
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UpdateDocumentReq,
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validate_chunk_method,
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2026-04-07 11:33:05 +08:00
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validate_document_name,
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Feature/table parser column roles (#13710)
### 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)
2026-05-11 07:06:04 +05:00
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validate_immutable_fields,
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2026-04-07 11:33:05 +08:00
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)
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from api.constants import FILE_NAME_LEN_LIMIT
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from api.db import FileType
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from common.constants import RetCode
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2026-05-28 11:13:01 +08:00
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def test_rest_api_page_size_rejects_values_above_100():
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assert validate_rest_api_page_size(REST_API_MAX_PAGE_SIZE) == REST_API_MAX_PAGE_SIZE
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with pytest.raises(ValueError, match="page_size must be less than or equal to 100"):
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validate_rest_api_page_size(REST_API_MAX_PAGE_SIZE + 1)
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with pytest.raises(ValidationError, match="page_size must be less than or equal to 100"):
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ListDatasetReq(page_size=REST_API_MAX_PAGE_SIZE + 1)
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with pytest.raises(ValidationError, match="page_size must be less than or equal to 100"):
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ListFileReq(page_size=REST_API_MAX_PAGE_SIZE + 1)
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2026-04-07 11:33:05 +08:00
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def test_validate_immutable_fields_no_changes():
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"""Test when no immutable fields are present in request."""
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update_doc_req = UpdateDocumentReq()
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 0.5
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg is None
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assert error_code is None
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def test_validate_immutable_fields_chunk_count_matches():
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"""Test when chunk_count matches the document's chunk_num."""
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update_doc_req = UpdateDocumentReq(chunk_count=10)
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 0.5
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg is None
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assert error_code is None
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def test_validate_immutable_fields_token_count_matches():
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"""Test when token_count matches the document's token_num."""
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update_doc_req = UpdateDocumentReq(token_count=100)
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 0.5
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg is None
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assert error_code is None
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def test_validate_immutable_fields_progress_matches():
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"""Test when progress matches the document's progress."""
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update_doc_req = UpdateDocumentReq(progress=0.5)
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 0.5
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg is None
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assert error_code is None
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def test_validate_immutable_fields_chunk_count_mismatch():
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"""Test when chunk_count doesn't match the document's chunk_num."""
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update_doc_req = UpdateDocumentReq(chunk_count=15)
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 0.5
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg == "Can't change `chunk_count`."
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assert error_code == RetCode.DATA_ERROR
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def test_validate_immutable_fields_token_count_mismatch():
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"""Test when token_count doesn't match the document's token_num."""
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update_doc_req = UpdateDocumentReq(token_count=150)
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 0.5
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg == "Can't change `token_count`."
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assert error_code == RetCode.DATA_ERROR
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def test_validate_immutable_fields_progress_mismatch():
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"""Test when progress doesn't match the document's progress."""
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update_doc_req = UpdateDocumentReq(progress=0.75)
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 0.5
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg == "Can't change `progress`."
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assert error_code == RetCode.DATA_ERROR
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def test_validate_immutable_fields_progress_boundary_values():
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"""Test progress with boundary values (0.0 and 1.0)."""
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# Test with 0.0
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update_doc_req = UpdateDocumentReq(progress=0.0)
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 0.0
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg is None
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assert error_code is None
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# Test with 1.0
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update_doc_req = UpdateDocumentReq(progress=1.0)
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 1.0
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg is None
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assert error_code is None
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def test_validate_immutable_fields_none_values():
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"""Test when request fields are None."""
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update_doc_req = UpdateDocumentReq(chunk_count=None, token_count=None, progress=None)
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 0.5
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg is None
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assert error_code is None
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2026-06-25 16:59:12 +05:30
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@pytest.mark.p2
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def test_validate_immutable_fields_zero_values_must_match():
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"""Regression: falsy zero values must still be validated, not skipped."""
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update_doc_req = UpdateDocumentReq(chunk_count=0, token_count=0, progress=0.0)
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doc = Mock()
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doc.chunk_num = 10
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doc.token_num = 100
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doc.progress = 0.5
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg == "Can't change `chunk_count`."
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assert error_code == RetCode.DATA_ERROR
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@pytest.mark.p2
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def test_validate_immutable_fields_zero_token_count_mismatch_when_chunk_count_matches():
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update_doc_req = UpdateDocumentReq(chunk_count=0, token_count=0, progress=0.0)
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doc = Mock()
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doc.chunk_num = 0
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doc.token_num = 100
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doc.progress = 0.0
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg == "Can't change `token_count`."
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assert error_code == RetCode.DATA_ERROR
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@pytest.mark.p2
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def test_validate_immutable_fields_zero_progress_mismatch_when_counts_match():
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update_doc_req = UpdateDocumentReq(chunk_count=0, token_count=0, progress=0.0)
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doc = Mock()
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doc.chunk_num = 0
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doc.token_num = 0
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doc.progress = 0.5
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg == "Can't change `progress`."
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assert error_code == RetCode.DATA_ERROR
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@pytest.mark.p2
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def test_validate_immutable_fields_zero_values_matching_doc():
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update_doc_req = UpdateDocumentReq(chunk_count=0, token_count=0, progress=0.0)
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doc = Mock()
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doc.chunk_num = 0
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doc.token_num = 0
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doc.progress = 0.0
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error_msg, error_code = validate_immutable_fields(update_doc_req, doc)
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assert error_msg is None
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assert error_code is None
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2026-04-07 11:33:05 +08:00
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def test_validate_document_name_valid():
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"""Test valid document name update."""
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req_doc_name = "new_document.pdf"
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doc = Mock()
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doc.name = "old_document.pdf"
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docs_from_name = []
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error_msg, error_code = validate_document_name(req_doc_name, doc, docs_from_name)
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assert error_msg is None
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assert error_code is None
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def test_validate_document_name_attr_error():
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"""Test valid document name update."""
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req_doc_name = 0
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doc = Mock()
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doc.name = "old_document.pdf"
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docs_from_name = []
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error_msg, error_code = validate_document_name(req_doc_name, doc, docs_from_name)
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assert error_msg == f"AttributeError('{type(req_doc_name).__name__}' object has no attribute 'encode')"
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assert error_code == RetCode.EXCEPTION_ERROR
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def test_validate_document_name_exceeds_byte_limit():
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"""Test when name exceeds byte limit."""
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long_name = "a" * (FILE_NAME_LEN_LIMIT + 1)
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doc = Mock()
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doc.name = "old_document.pdf"
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docs_from_name = []
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error_msg, error_code = validate_document_name(long_name, doc, docs_from_name)
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assert f"File name must be {FILE_NAME_LEN_LIMIT} bytes or less." in error_msg
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assert error_code == RetCode.ARGUMENT_ERROR
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def test_validate_document_name_different_extension():
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"""Test when extension is different from original."""
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req_doc_name = "new_document.docx"
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doc = Mock()
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doc.name = "old_document.pdf"
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docs_from_name = []
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error_msg, error_code = validate_document_name(req_doc_name, doc, docs_from_name)
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assert "The extension of file can't be changed" in error_msg
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assert error_code == RetCode.ARGUMENT_ERROR
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def test_validate_document_name_duplicate():
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"""Test when name already exists in the same dataset."""
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req_doc_name = "duplicate.pdf"
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doc = Mock()
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doc.name = "original.pdf"
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duplicate_doc = Mock()
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duplicate_doc.name = "duplicate.pdf"
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docs_from_name = [duplicate_doc]
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error_msg, error_code = validate_document_name(req_doc_name, doc, docs_from_name)
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assert "Duplicated document name in the same dataset." in error_msg
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assert error_code == RetCode.DATA_ERROR
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def test_validate_document_name_case_insensitive_extension():
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"""Test that extension check is case-insensitive."""
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req_doc_name = "new_document.PDF"
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doc = Mock()
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doc.name = "old_document.pdf"
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docs_from_name = []
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error_msg, error_code = validate_document_name(req_doc_name, doc, docs_from_name)
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assert error_msg is None
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assert error_code is None
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def test_validate_chunk_method_valid():
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"""Test with a valid chunk method."""
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doc = Mock()
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doc.type = FileType.PDF
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doc.name = "document.pdf"
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error_msg, error_code = validate_chunk_method(doc)
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assert error_msg is None
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assert error_code is None
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def test_validate_chunk_method_visual_not_supported():
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"""Test that visual file types are not supported."""
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doc = Mock()
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doc.type = FileType.VISUAL
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doc.name = "image.jpg"
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|
error_msg, error_code = validate_chunk_method(doc)
|
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|
|
assert "Not supported yet!" in error_msg
|
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|
assert error_code == RetCode.DATA_ERROR
|
|
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|
def test_validate_chunk_method_ppt_not_supported():
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|
"""Test that PPT files are not supported."""
|
|
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|
doc = Mock()
|
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|
|
doc.type = FileType.PDF
|
|
|
|
|
doc.name = "presentation.ppt"
|
|
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|
|
|
|
|
|
|
error_msg, error_code = validate_chunk_method(doc)
|
|
|
|
|
assert "Not supported yet!" in error_msg
|
|
|
|
|
assert error_code == RetCode.DATA_ERROR
|
|
|
|
|
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|
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|
|
def test_validate_chunk_method_pptx_not_supported():
|
|
|
|
|
"""Test that PPTX files are not supported."""
|
|
|
|
|
doc = Mock()
|
|
|
|
|
doc.type = FileType.PDF
|
|
|
|
|
doc.name = "presentation.pptx"
|
|
|
|
|
|
|
|
|
|
error_msg, error_code = validate_chunk_method(doc)
|
|
|
|
|
assert "Not supported yet!" in error_msg
|
|
|
|
|
assert error_code == RetCode.DATA_ERROR
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
def test_validate_chunk_method_pages_not_supported():
|
|
|
|
|
"""Test that Pages files are not supported."""
|
|
|
|
|
doc = Mock()
|
|
|
|
|
doc.type = FileType.PDF
|
|
|
|
|
doc.name = "document.pages"
|
|
|
|
|
|
|
|
|
|
error_msg, error_code = validate_chunk_method(doc)
|
|
|
|
|
assert "Not supported yet!" in error_msg
|
|
|
|
|
assert error_code == RetCode.DATA_ERROR
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def test_validate_chunk_method_other_extensions_still_valid():
|
|
|
|
|
"""Test that other file extensions are still valid."""
|
|
|
|
|
doc = Mock()
|
|
|
|
|
doc.type = FileType.PDF
|
|
|
|
|
doc.name = "document.docx"
|
|
|
|
|
|
|
|
|
|
error_msg, error_code = validate_chunk_method(doc)
|
|
|
|
|
assert error_msg is None
|
Feature/table parser column roles (#13710)
### 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)
2026-05-11 07:06:04 +05:00
|
|
|
assert error_code is None
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def test_parser_config_normalizes_legacy_vectorize_table_column_role():
|
|
|
|
|
p = ParserConfig(
|
|
|
|
|
table_column_roles={"title": "vectorize", "country": "metadata", "x": "both"},
|
|
|
|
|
)
|
|
|
|
|
assert p.table_column_roles == {
|
|
|
|
|
"title": "indexing",
|
|
|
|
|
"country": "metadata",
|
|
|
|
|
"x": "both",
|
2026-05-28 11:13:01 +08:00
|
|
|
}
|