Ledger Summary and Report Generation for Construction Machinery: Document Parsing and Chunking

What Data for This Category Looks Like

Data for construction machinery ledgers and reports comes primarily from equipment factory specification documents, monthly operation and maintenance inspection records, lease and purchase contract attachments, and annual equipment inventory reports. Data update frequency aligns with business milestones: new basic equipment ledgers are added when purchasing and putting into storage, operation logs are updated daily or weekly, and annual inventory reports are generated monthly or quarterly. Document formats include editable PDFs, structured Excel tables, and scanned paper reports. Fields include unique equipment ID, model, rated operating hours, fuel consumption, usage location, maintenance time, and more. Units use engineering-specific measurement standards such as hours, liters, and units. Reference only; verify against the target FastGPT release and deployment documentation.

What Constraints These Characteristics Impose on Document Parsing and Chunking

Multi-source heterogeneous formats of construction machinery ledgers require the parsing step to support both editable text and scanned image extraction. Otherwise, key parameters in operation records will be lost. Strong association between fields such as equipment ID and usage location requires that chunking does not destroy the integrity of single-item data. Otherwise, field matching gaps will occur during retrieval. Long documents and large packaged files will increase parsing time. Failing to adjust timeout settings may trigger parsing failures. Filter fields in Excel ledgers (such as usage location) that are truncated during chunking will prevent complete matching results from being retrieved later.

How to Configure Settings

Reference only; verify against the target FastGPT release and deployment documentation.

| PARSE_FILE_TIMEOUT_SECONDS | 600–900 seconds | Annual ledger PDFs for a single device can be dozens of pages long, and OCR parsing takes a long time. Default timeout settings will easily trigger errors |

| UPLOAD_FILE_MAX_SIZE | 1000 MB | Some large-scale projects have large packaged batch ledger files, which need to adapt to storage and parsing requirements for multi-file batch uploads |

| chunkSize | 800–1200 characters | Single ledger chunk for construction machinery needs to cover continuous operation records or complete parameter groups for a single device, to avoid splitting that breaks data associations |

| chunkSplitter | Split by device ID + paragraph | Ledgers are grouped by device. Splitting purely by paragraph may cross device boundaries. Splitting by device ID ensures unified data attribution within each chunk |

| chunkSettingMode | Manual configuration | Chunking rules need to be adjusted for ledgers in different formats (PDF/Excel). Automatic mode cannot adapt to detailed field structures |

Three Common Misconfigurations

• Symptom: A timeout error is triggered when uploading large packaged ledger files, and a connection timeout status code is returned. Cause: The PARSE_FILE_TIMEOUT_SECONDS parameter is not adjusted, and the default short timeout setting is used, which cannot complete multi-file batch parsing.
• Symptom: When searching for equipment with overseas usage locations in Excel ledgers, the number of returned results is far less than the actual number of entries. Cause: The associated metadata of the usage location field is not retained during chunking, or complete data rows containing this field are truncated during chunking.
• Symptom: After uploading scanned factory equipment specification pages, the extracted text lacks fields such as equipment model and rated operating hours. Cause: OCR mode for customPdfParse is not enabled, so text embedded in ledger parameter images cannot be recognized.

How to Confirm Proper Configuration

• Upload a single 500-page annual operation ledger PDF, wait for parsing to complete, check the system parsing log to confirm there are no timeout error records.
• Upload an Excel ledger containing equipment with overseas usage locations, search for a specified usage location field, and verify that the number of returned results matches the actual number of entries.
• Upload a scanned factory equipment specification page, check whether the extracted text includes core fields such as equipment model and rated operating hours.
• Adjust the chunkSize parameter, split long documents, and check that the chunking results do not split continuous operation records across devices.

The values listed above are common starting points and should be evaluated against the user'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 2026-09-28.