What the data for this category looks like
Logistics marketing-related data originates from four primary sources: logistics waybill systems, delivery route planning documents, offline outlet service manuals, and online marketing campaign materials. Data update cycles vary widely: waybill data updates in real time as orders are created, outlet information synchronizes once weekly, and marketing campaign materials adjust when campaigns launch or conclude.
Document structures fall into two categories: structured fields and unstructured text. Structured fields include waybill numbers, recipient addresses, delivery time windows, freight amounts, and more, with units mostly being hours, days, and yuan. Unstructured text includes service scripts, delivery specification explanations, and similar content. Some datasets are bulk CSV waybill files, while others are long-form PDF manuals.
What constraints do these characteristics impose on the knowledge base retrieval and recall workflow
The multi-source, heterogeneous nature of logistics data requires the retrieval process to distinguish matching rules for structured fields and unstructured text, to avoid fuzzy searches interfering with precise matching. Real-time updated waybill data requires regular incremental indexing; otherwise, recalled delivery information may lag behind actual statuses.
Long-form service specifications and scripts contain numerous specialized terms such as trunk line transportation and last-mile delivery. Too short text segments split the context of these terms, reducing the accuracy of semantic recall. Bulk CSV waybill data includes many empty fields—for example, undelivered orders lack signing times. Invalid data must be filtered before retrieval to prevent recalling meaningless information. Marketing materials have strong timeliness; expired campaign content must be removed from the knowledge base in a timely manner to avoid misleading users.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Logistics documents include bulk CSV waybills and long-form service manuals, which take longer to parse. This setting avoids interrupting parsing tasks due to timeout |
chunk_size | 800–1200 characters | Logistics content is dense with specialized terms. Longer text segments preserve term context, preventing semantic breaks that reduce recall accuracy |
similarity_threshold | Calibrated via actual testing | Logistics marketing content has high requirements for keyword matching accuracy. Adjust the threshold based on business scenarios to avoid recalling irrelevant content or missing valid information |
recall_top_k | Top 8 results | Relevant information for logistics marketing content is spread across multiple documents. Sufficient recall results cover complete marketing service scenarios |
enable_structured_search | Enabled | Logistics data includes numerous structured fields such as waybill numbers and delivery time windows. Structured search improves the efficiency of precise matching |
index_update_interval | 15 minutes | Logistics waybill data has high real-time requirements. Regular incremental indexing ensures the timeliness of recalled data |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: When calling the knowledge base creation API, the returned results do not include a parsing status field, making it impossible to determine parsing progress. Cause: The
return_parse_statusswitch was not enabled in the API request parameters. Parsing status information is not returned by default. - Symptom: After importing bulk logistics waybill CSV files, the dataset status remains displayed as "Indexing" and does not update beyond the preset timeout period. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted to a reasonable duration. No automatic retry mechanism is triggered after bulk data parsing times out. - Symptom: After setting
similarity_thresholdto a specified value, a large number of results with similarity scores far exceeding the threshold are still recalled, and invalid content cannot be filtered using this parameter. Cause: The score normalization configuration for the vector model was not enabled. The default score range is 0–10000, which does not align with the system's default 0–1 filtering rule.
How to confirm that configurations are correctly set
- Call the knowledge base creation API, check whether the returned results include the
parse_statusfield, and confirm that the field values correspond to parsing in progress, ready, and parsing failed statuses. - Upload a single logistics service manual document, wait for parsing to complete, then check the document parsing status in the knowledge base to confirm there are no abnormal error messages.
- After configuring retrieval parameters, enter a query containing logistics specialized terms, check whether the field matching degree and score range of the recalled results meet business expectations.
- Trigger an incremental indexing task, check the index update logs, confirm that structured data fields are correctly identified and included in the retrieval scope.
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.