What the Data for This Category Looks Like
Medical aesthetics financial report data mainly comes from public quarterly and annual reports of listed medical aesthetics enterprises, plus internal monthly operation reports from chain medical aesthetics institutions. Most documents combine structured tables and paragraphs. They include detailed fields such as medical aesthetics service revenue, consumable procurement costs, per-project customer unit price, and store operation expenses. Some documents also attach compliance disclosure notes. Data updates follow public report cycles: quarterly or annually. Internal operation data is updated monthly. Units include pieces (consumables), ten thousand yuan (revenue, costs), person-times (service volume), and other detailed measurement dimensions.
What Constraints These Characteristics Impose on Document Parsing and Chunking
Medical aesthetics financial reports have a high proportion of structured tables, many detailed fields, and diverse measurement dimensions. The parsing process must first identify associated data within tables, to avoid splitting cross-field table content into scattered text. Detailed revenue and cost fields must be chunked by business dimension, instead of using pure paragraph chunking. This ensures that data from the same type of business is grouped into the same chunk unit. When batch parsing multiple monthly or quarterly financial reports across cycles, the system must adapt to format differences across documents, to prevent cross-cycle data from being mixed together. Nested disclosure content in notes must retain its hierarchical structure, to avoid incorrect merging of different compliance clauses.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | Medical aesthetics financial reports contain a large number of structured tables, and the association between fields and data within tables must be preserved |
CHUNK_SIZE | 800–1200 characters | Medical aesthetics financial reports have many detailed fields. This length can accommodate complete data for a single business segment, avoiding splitting cross-business content |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Single quarterly financial report documents have a relatively long length, so sufficient parsing processing time must be reserved |
BATCH_PARSE_MAX_COUNT | 20 documents per batch | Balances batch parsing efficiency and single-batch service load, adapting to batch processing needs for monthly financial reports |
KEEP_UNIT_WITH_FIELD | Enabled | Medical aesthetics financial reports include multiple types of measurement units, so the binding relationship between business fields and their corresponding units must be preserved |
PARSE_NESTED_CONTENT | Enabled | Financial report notes have nested disclosure structures, so the hierarchical association of content must be retained |
CHUNK_INDEX_ENABLE | Enabled | Generates chunk index data, supporting subsequent calls to corresponding chunk content by index |
The parameter values provided on this page are common recommendations that serve as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When calling the document parsing API of FastGPT 4.8.10, the returned result does not include the associated fields for chunk indexes. Cause: The
CHUNK_INDEX_ENABLEparameter is not enabled, so the parsing process does not generate chunk index data. - Symptom: When clicking to copy parsing results in the streaming interaction sample project, a prompt pops up saying "Unable to use browser automatic copy, please manually copy the content below". Cause: The project deployment domain name is inconsistent with the FastGPT service domain name, triggering the browser same-origin policy restriction, which causes the clipboard API call to fail.
- Symptom: When configuring the Playwright MCP service for document parsing, the service call returns a
504timeout error code, while the Amap MCP service can be called normally. Cause: The deployment address of the Playwright MCP service is not correctly configured as a network address accessible to FastGPT. The temporary address used for local debugging cannot be connected in the deployment environment.
How to Confirm Proper Configuration
- Upload a single medical aesthetics quarterly financial report document, view the parsed chunk list, and confirm that detailed data from the same business segment is grouped into the same chunk unit.
- Call the APIs related to document parsing, check whether the returned results include fields related to chunk indexes, and confirm that the index generation logic is effective.
- Batch upload multiple medical aesthetics financial report documents from the same cycle, view the execution status of parsing tasks, and confirm that no timeout exceptions occur.
- View the parsed document content, and confirm that the association relationship between each business field and its corresponding measurement unit is preserved.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.