What the data for this category looks like
Construction machinery investment research data mainly comes from industry association public reports, annual reports and product manuals of listed construction machinery manufacturers, government bidding announcements, industry exhibition materials, and third-party research documents. Update rhythms vary across content types. Industry dynamic content is updated weekly. Listed company annual reports are released quarterly or annually. Product parameters are updated irregularly along with manufacturers' technology iterations. Bidding announcements are updated in real time along with project progress. Document structures are mixed. They include pure text industry trend analysis, technical parameter documents with nested tables, structured equipment sales data tables, and technical manuals with marked working condition parameters. Fields and units have clear targeted attributes. Equipment parameter fields include rated power, maximum breakout force, working radius, etc. Corresponding units are kW, kN, m respectively. Project fields include budget amount, construction period. Corresponding units are ten thousand yuan, days.
What constraints these characteristics impose on the document parsing and chunking link
The mixed structural characteristics of construction machinery investment research data require the parsing link to handle both plain text paragraphs and nested tables. It must avoid splitting table content into unrelated fragmented text. The diverse update rhythms require the chunking link to adapt to parsing time requirements of different documents. It must avoid parsing timeout for short documents or incomplete parsing for long documents. The targeted fields and units require that the binding relationship between fields and units be retained after chunking. It must avoid loss of parameter information. Long text analysis reports account for a large proportion of data. Chunk length must be controlled to fit large model context windows. It must also avoid semantic fragmentation across chapters.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
enable_advanced_pdf_parse | Enabled | Most construction machinery documents contain nested tables and technical parameters. Enhanced parsing preserves table structure and field associations |
chunk_size | 800–1200 characters | Construction machinery documents include long technical paragraphs and structured parameters. This range preserves semantic integrity of single segments and fits common context windows |
chunk_overlap | 100–150 characters | Equipment parameter tables and analysis paragraphs are closely connected. Setting overlap avoids semantic fragmentation |
table_vector_support | Enabled | Construction machinery documents contain numerous equipment parameter tables. When enabled, tables are split into structured vector entries |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large annual reports and bidding documents take longer to parse. This duration covers most scenarios |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Large industry report PDFs have significant file size. This threshold supports complete uploads |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require targeted analysis. It is recommended to test on samples relevant to the actual use case before finalizing settings.
Three common mistakes
- Symptom: The
enable_advanced_pdf_parseconfiguration item cannot be found in the plugin management interface, and table content is garbled after PDF parsing. Cause: The minerU dependency package is not installed during local deployment, so the enhanced parsing module is not loaded. - Symptom: After enabling
table_vector_support, the parsed equipment parameter tables only retain numerical values, and field names and units such as rated power and bucket capacity are lost. Cause:enable_advanced_pdf_parseis not enabled at the same time. Basic parsing cannot extract the header semantics of tables. - Symptom: After pulling Feishu multi-dimensional table data via API, the parsed result has no structured field associations. Cause: The mapping rules for table vector support are not configured, so column names and numerical values of the multi-dimensional table are not bound.
How to confirm the configuration is correct
- Upload a PDF of a construction machinery product manual containing nested tables, and check whether table headers and cell content are fully associated in the parsing result.
- Enter the settings page of the target knowledge base, and confirm that both the
enable_advanced_pdf_parseandtable_vector_supportswitches are in the enabled state. - Upload a large industry report document, and check whether the execution duration of the parsing task matches the configured timeout threshold.
- Call the knowledge base parsing API to upload a small equipment parameter table, and confirm that the returned vector data contains field names and unit information.
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.