What the data for this category looks like
Logistics intelligent due diligence report data mainly comes from logistics enterprises’ waybill management systems, warehouse WMS systems, transportation track positioning systems, and reconciliation files from third-party logistics service providers. Data updates follow business batch progress. A single due diligence dataset usually covers full-link operation records from the past three months. The core document structure uses structured tables, with fields including waybill number, sending and receiving addresses, actual weight, volume, transportation method, delivery timeliness, abnormal item remarks, and more. Some attachments include GPS log files for transportation tracks. Field units use universal logistics measurement standards such as kilograms, cubic meters, and hours.
What constraints these characteristics impose on model integration and configuration
Logistics due diligence data has many structured fields and accompanying unstructured track attachments. This requires model integration to support both structured data extraction and unstructured document parsing needs. Data updates follow business batch progress, so batch processing concurrency must match the scale of daily due diligence datasets. Fields use specific logistics measurement units. The model’s field identification link needs preset dedicated field mapping rules to avoid unit confusion or field misalignment in extraction results. Unstructured GPS log files have large file sizes, so timeout parameters and segmentation rules adapted for large file parsing must be configured.
How to Determine Configuration Values
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Logistics track-related attachments have large file sizes, so sufficient time must be reserved for file parsing |
UPLOAD_FILE_MAX_SIZE | 2000 MB | The total scale of attachments for a single batch of due diligence is usually high, so this adapts to large file upload requirements |
chunkSize | 800–1200 characters | Balances short-text association of logistics structured fields and parsing integrity of long-text track logs |
recallTopK | Top 8–12 entries | Covers multi-node logistics data required for due diligence, and avoids redundant information interfering with model judgment |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters redundant extraction results with low matching degrees, and adapts to the semantic consistency characteristics of logistics domain terms |
EMBEDDING_MODEL_NAME | bge-large-zh-v1.5 | Provides higher semantic matching accuracy for Chinese logistics-specific terms, and adapts to the field extraction requirements of due diligence reports |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Issue: A 404 error occurs when using OneAPI to access the
bge-large-zh-v1.5model deployed via Ollama. Cause: The local model path of Ollama is not correctly mapped in the OneAPI model configuration, so the interface cannot recognize the target model. - Issue: Excessive variation appears in results when using the FastGPT built-in Workflow template for logistics due diligence classification. Cause: Parameters related to the similarity threshold are not adjusted for the logistics domain, so the model has insufficient differentiation between semantically similar logistics node data.
- Issue: The logistics due diligence question classification link takes too long. Cause: The recall count parameter is not adjusted, and excessive redundant data is recalled, leading to overly high model inference load.
How to Confirm Configuration is Complete
- Upload the largest single logistics attachment, and confirm whether the parsing task completes within the preset timeout period.
- Import a test dataset containing typical logistics fields, and confirm whether the field names and units extracted by the model conform to preset rules.
- Initiate a batch due diligence task, and confirm whether the concurrent processing volume of model inference matches the daily business scale.
- Call the classification interface, and confirm whether the response time of the returned results meets business expectations.
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.