What the data for this category looks like
Daily documents for this category include adaptation lists, compatibility test reports, migration plans, and issue ledgers. Data sources include existing system lists, domestic software and hardware directories, test environments, and vendor materials. Data update frequency is per project or quarterly. Single project document sizes range from a few pages to hundreds of pages. Adaptation lists contain fields for software and hardware model, version number, and adaptation status. Compatibility test reports contain fields for test items, test environment, and result judgment. The overall structure primarily uses structured entries and segmented reports.
What constraints these characteristics impose on the document parsing and chunking link
Documents for domestic independent innovation replacement adaptation are mostly structured entries or standardized reports. Chunking must avoid splitting associated information from the same adaptation item or test result, to ensure key fields are not truncated. Data updates are organized per project, so high-frequency incremental parsing is not needed, and full parsing mode can be used. Most document sources are official materials and test outputs provided by vendors. The integrity of original fields must be preserved, to avoid losing key compliance information such as adaptation status and version number during parsing. Parsing failures or timeouts will directly affect migration progress, so parsing duration and accuracy must be strictly controlled.
How to configure the settings
The following configurations apply to FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Matches the paragraph and entry length of domestic independent innovation documents, avoids splitting associated information from the same adaptation item |
chunkSplitter | Switch based on document type | Use title-based splitting for adaptation lists, and paragraph-based splitting for compatibility reports, to match the structural characteristics of different documents |
customPdfParse | Enabled | Domestic documents may have non-standard formatting; custom parsing preserves key compliance fields such as adaptation status and version number |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Matches the parsing duration of large compatibility test reports, avoids parsing failure due to timeout |
chunkSettingMode | Manual configuration | The structure of domestic independent innovation adaptation documents is consistent, but varies significantly between projects; manual configuration allows precise control of chunking logic |
Three common mistakes
- Symptom: After uploading a domestic adaptation list in simple mode, the large language model does not trigger file parsing and returns an empty context. Cause:
chunkSettingModeis not configured with rules for domestic independent innovation documents, and the default chunking logic does not recognize dedicated fields, resulting in unassociated parsing results. - Symptom: When using
customPdfParseto parse a large PDF compatibility test report, FastGPT reports a timeout, but the parsing service returns success. Cause: The value set forPARSE_FILE_TIMEOUT_SECONDSis less than the actual parsing duration, and insufficient processing time is reserved. - Symptom: After uploading an Excel-format migration plan in a workflow, the software and hardware version information in the table cannot be read. Cause:
chunkSplitteris not configured to split by table rows/columns, and the default chunking does not extract structured table data.
How to confirm the configuration is correct
- Upload a single adaptation list document, and check if the parsed text contains all key fields such as software and hardware model, version number, and adaptation status.
- Upload a compatibility test report with more than 100 pages, wait for parsing to complete, and check if any timeout-related errors are triggered.
- Add a file reading node to the workflow, upload an Excel-format migration plan, and confirm that structured content in the table can be extracted.
- Switch between different large language models, and verify that all can call the parsed document content to answer questions.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.