What the data for this category looks like
Data sources for shipping port intelligent due diligence reports include official vessel scheduling data filed with maritime authorities, berth throughput ledgers from port operators, and customs declaration records for cargo circulation. Three update schedules apply:
- Real-time operational data such as berthing and loading/unloading is updated daily
- Monthly throughput statistics are updated at the end of each month
- Annual planning data is updated quarterly
The document structure centers on structured tables, paired with unstructured operational notes and compliance explanations. Fields include berth number, vessel IMO number, cargo type, loading/unloading volume (unit: tons or standard container TEU), port detention duration (unit: hours), yard occupancy area (unit: square meters), and other standard fields. The parsed text length of a single complete due diligence report can reach tens of thousands of characters.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The mixed data structure of shipping port due diligence reports requires multi-turn dialogue to distinguish between structured field queries and unstructured operational note interpretation. Data with different update frequencies requires corresponding recall and refresh rules. For example, real-time berthing and loading/unloading data must be recalled from the vector database for the latest version during each dialogue. Monthly throughput data can use offline vector data that is synchronized regularly.
The diversity of field units requires prompt engineering to clearly specify unit verification rules for query results, to avoid confusion between TEU and tons, hours and days. The long text attribute of a single report requires multi-turn dialogue to retain sufficient context history, ensuring that cross-paragraph associated queries can be correctly identified. It also requires limiting redundant loading of invalid context to avoid model output bias.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single shipping port due diligence report documents have large per-segment lengths after splitting. Sufficient context must be retained to handle cross-segment associated queries and avoid loss of historical information |
PARSE_FILE_CHUNK_SIZE | 1500–2000 characters | Port data fields are closely linked. Each chunk must contain complete combinations of berth, cargo type, and loading/unloading volume to avoid query association failures caused by split breaks |
RECALL_TOP_N | Top 8–12 entries | Due diligence report data has multiple dimensions. Sufficient associated data must be recalled to support field linkage queries for multi-round follow-up questions |
SIMILARITY_THRESHOLD | 0.72–0.78 | Port data field naming is standardized but has synonyms. Balance recall precision and coverage to avoid missing relevant data or introducing redundant results |
UPLOAD_FILE_MAX_SIZE | 2000 MB | A single port due diligence report may include multiple batches of vessel data files. Support for large file upload and parsing is required |
CONVERSATION_MEMORY_TTL | 3600 seconds | Multi-round due diligence dialogues are typically completed within one hour. Retaining context sufficiently covers the complete query process and avoids losing historical parameters mid-session |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- A 413 Request Entity Too Large error is returned after calling the API to upload port data files. The cause is failure to configure
UPLOAD_FILE_MAX_SIZEto adapt to large file sizes, or the uploaded file format is not recognized by the supported parser. - The AI dialogue node outputs a large amount of unnecessary conversational explanatory content in the return result. The cause is failure to turn off the "auto-append to conversation history" switch for the node, and the prompt engineering does not clearly limit the return to only structured results.
- Subsequent queries in multi-turn dialogue cannot associate the previously specified port berth ID. The cause is failure to enable the
CONVERSATION_MEMORY_TTLconfiguration, or insufficientmaxContextsetting causes historical context to be truncated.
How to confirm correct configuration
- Upload a single batch of port vessel data files, check that the parsed data blocks contain complete IMO numbers and berthing time fields, with no truncation or split errors. This can be verified via the node's "parsed result preview" interface.
- Initiate two rounds of dialogue: first query the throughput of a specified berth, then follow up with a question about the proportion of corresponding cargo types. Check that the conversation history panel retains the previously specified berth ID parameter, with no loss.
- Call the API to upload a file and initiate a query, check that the content field of the return result only contains structured data, with no additional conversational explanatory content.
- Adjust
SIMILARITY_THRESHOLDto the set interval, initiate a multi-dimensional query, check that the recalled results include the target fields and no redundant irrelevant port operation data.
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.