Business Process Guidance for Construction Machinery: Document Parsing and Chunking

What the Data for This Category Looks Like

Documents for construction machinery-related business processing come from four main sources: internal enterprise equipment ledger systems, procurement contract archives, on-site construction operation and maintenance records, and government quality inspection reports. Data updates trigger alongside business nodes. Equipment ledgers sync with incoming equipment. Maintenance logs are entered after each job completes. Supported document formats include PDF quality inspection reports, Excel equipment parameter sheets, CSV operation logs, and others. Document fields include equipment serial numbers, operation duration (unit: hours), working condition types, contract delivery dates, maintenance cycles, and more. Some long documents include multi-page step-by-step acceptance clauses and detailed parameter lists.

Constraints on Document Parsing and Chunking

The mixed multi-format nature of construction machinery business documents requires parsing support for PDF, Excel, and CSV formats. The system must adapt to multi-column parameter sheets in Excel and long multi-page documents. Fields with attached units such as operation duration must retain the binding between the parameter and unit during parsing. This prevents loss of semantics after chunking. Step-by-step acceptance clauses in long documents must avoid chunk splitting across steps. This preserves context integrity for business process guidance. Real-time updated short operation log entries require small-grain chunking. This matches the context needs of single queries. Equipment serial numbers as unique identifiers must retain associated equipment information during chunking. This prevents ambiguity during retrieval.

How to Set Configurations

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

| chunkSplitter | By paragraph and semantic boundary | Adapts to natural segmentation of step-by-step acceptance clauses and operation logs in long documents, preserves logical integrity of business processes |

| customPdfParse | Enabled | Adapts to PDF-format quality inspection reports and contract documents, retains parsing accuracy for tables and multi-page layouts |

| UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to upload requirements for multi-page quality inspection reports and batch operation logs of large equipment, calibrated via actual testing |

| PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time for long documents, avoids parsing timeout for large PDF or Excel files |

| chunkSettingMode | Manual configuration | Adapts to the multi-format characteristics of construction machinery documents, allows adjustment of chunking parameters for different document types |

Three Common Misconfigurations

• Phenomenon: When calling the FastGPT interface, it is not possible to parse both knowledge base call parameters returned with detail: true and streaming return results simultaneously. Reason: No custom parsing logic bound to the streaming return context node is configured, leading to failure to associate the two types of data.
• Phenomenon: When uploading Excel files or multi-column CSV files, parsed fields are missing or formatted incorrectly. Reason: No parsing configuration adapted to multi-column formats is enabled. The default CSV parsing only supports two columns of data, and corresponding parameters are not adjusted.
• Phenomenon: After deploying marker-pdf, a connection failure error occurs when calling within the container. Reason: Container ports are not correctly mapped or the marker-pdf service is not started, causing FastGPT to fail to call the parsing interface.

How to Verify Successful Configuration

• Upload an equipment ledger file with multiple columns of parameters, and confirm parsed fields retain all column content and associated units.
• Upload a multi-page step-by-step acceptance document, and confirm chunked content retains complete context for a single step, without splitting across logical nodes.
• Call the interface to test a request with detail: true, and confirm knowledge base call parameters and streaming return content can be properly associated and parsed.
• Upload a single large document, and confirm the parsing process does not generate timeout errors, matching the preset timeout configuration.

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 2026-09-28.