What the data for this category looks like
Commercial vehicle research report data mainly comes from monthly production and sales briefings released by domestic commercial vehicle industry associations, in-depth research reports released by securities firms’ research institutes, quarterly operating reports publicly released by vehicle manufacturers, and vehicle registration data from transportation departments. Monthly production and sales data is updated monthly, in-depth research reports are released irregularly following industry milestones, and policy documents are updated simultaneously as policies are issued. Most documents are multi-chapter PDFs, containing sections such as industry overview, segmented vehicle model data, cost analysis, market forecasts, etc. They embed a large number of structured tables and plain text paragraphs, and some documents contain raw data tables in scanned form. Fields include vehicle category, production batch, total registration volume, component unit price, etc. Production and registration data is measured in units, and component unit prices are measured in yuan.
What constraints do these characteristics impose on the document parsing and chunking link
Multi-source document formats (PDF, Word, scanned format tables) require parsing tools to support both native text extraction and OCR recognition, covering research reports in different release formats. The high proportion of structured tables and merged cells requires the parsing process to accurately identify table structures, avoiding data misalignment caused by splitting merged cells. Document chapters are clearly divided, but internal paragraph lengths vary greatly. Some chapters contain a large number of data tables, so chunking must avoid making individual chunks too long or too short while ensuring semantic completeness. Some documents have digital signatures, which restrict native text extraction permissions, requiring additional processing of the signature verification link to fully extract content.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Some commercial vehicle research report documents are long and contain a large number of tables, resulting in long parsing times. 600 seconds covers the parsing needs of most documents |
DOC_PARSE_OCR_ENABLE | Enabled | Some research reports release raw data tables in scanned form. Enabling OCR can extract text and table information from scanned content |
maxChunkSize | 800–1200 characters | The complete semantic length of single-segment data in commercial vehicle research reports, such as single vehicle model production and sales data, usually falls within this range, avoiding cross-semantic chunking |
chunkOverlap | 100–150 characters | Correlated data across paragraphs exists in research reports, such as monthly and quarterly data for the same vehicle model. Overlapping chunks can retain contextual associations |
PARSE_TABLE_ENABLE | Enabled | Structured tables account for a high proportion in commercial vehicle research reports. Enabling table parsing preserves the row and column structure of raw data, avoiding data loss caused by text-based splitting |
UPLOAD_FILE_MAX_SIZE | 500 MB | Some in-depth research report PDF files are large in size. 500 MB covers the size of most commercial vehicle manufacturer research reports and industry association reports |
The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Scenario: After uploading a commercial vehicle research report PDF with a digital signature, the parsing result only extracts a small amount of text or is completely empty. Cause: Digital signatures lock the native text extraction permissions of the PDF, and conventional parsing processes cannot bypass signature verification to read complete content.
- Scenario: FastGPT V4.9.1 calls the Doc2x tool and returns a file read failure error, with an error message containing format restriction prompts such as
Only support .txt, .m. Cause: The Doc2x tool in this version has limited support for some encrypted PDFs and compressed archive format research report files, and cannot complete parsing. - Scenario: When clicking the document reading link URL returned by the knowledge base, a 403 status code is returned. Cause: The temporary access link generated by the parsing tool is not configured with correct permission validity periods or cross-domain rules, and cannot be accessed externally.
How to confirm the configuration is set correctly
- Upload a commercial vehicle research report PDF with a digital signature, check whether the parsing result extracts complete text and table information, and confirm that OCR and signature-related configurations are correctly enabled.
- Upload a PDF file of typical size for commercial vehicle research reports, check whether the upload and parsing processes are completed within the configured timeout and size limits, and confirm that the upload and timeout configurations are appropriate.
- View the parsed chunk list, check whether structured tables exist as complete entries, and confirm that the table parsing configuration is enabled.
- Initiate a search for keywords related to commercial vehicle research reports, check whether the returned chunk content covers complete chapter information, and confirm that the chunk length and overlap configurations are adapted to the document structure.
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.