What this category of data looks like
Bulk commodity trade data is sourced from transaction ledgers, port loading and unloading records, futures market application programming interfaces, and public industry association reports within the state-owned enterprise commercial logistics sector. Real-time market data updates at the second level. Transaction ledgers sync daily. Monthly industry reports update weekly. A single document typically includes fields such as transaction number, commodity category, transaction unit price, delivery location, transaction time, and paying/receiving party qualification. Common units include yuan/ton, ten thousand tons, cubic meters, and similar. Some documents include image or PDF attachments of electronic warehouse receipts.
Constraints for Multi-turn Dialogue and Prompt Engineering
Second-level real-time market updates require limiting how long old data is cached in dialogue context. Set a reasonable threshold for messages context length to avoid calling outdated information. Structured transaction data with multiple fields requires explicit key field specification in prompts, such as transaction number and unit price, to prevent the AI from missing core information like units or delivery locations. Electronic warehouse receipt image and PDF attachments require separate file parsing adaptation rules to ensure correct recognition of image-based knowledge base content during multi-turn dialogue. Transaction data sensitivity requires adding data desensitization rules to prompts to prevent leakage of sensitive information such as paying/receiving party qualifications.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 800–1200 characters | Bulk commodity trade data has many fields; overly long context causes AI to confuse new and old data |
messages parameter length limit | First 10 dialogue turns | Excessive historical dialogue occupies context space and overwrites core business information |
| File parsing trigger rule | Trigger by file extension | Bulk commodity trade documents mostly use PDF and image formats; triggering by extension accurately adapts parsing logic |
| Context refresh interval | 600 seconds | Matches real-time market update rhythm, balancing response speed and data timeliness |
| Prompt field constraints | Calibrated via actual testing | Must align with FastGPT v4.17.0 parsing logic; field differences vary across categories |
Three Common Misconfigurations
- A 503 error appears when uploading a document via the dialogue box, but backend logs show the file upload succeeded. The cause is that the
chatIdparameter for multi-turn dialogue is not correctly bound to the current session, so the file upload request cannot match a valid session context. - When using a strict question-and-answer template with the knowledge base, a "answer not found" prompt appears after retrieving an image address. The cause is that the prompt lacks configured image parsing adaptation rules, so it cannot recognize image-based knowledge base content and only processes text-based search results.
- Field extraction errors occur during multi-turn dialogue, such as missing delivery location or unit. The cause is that the prompt does not explicitly specify key fields to extract, so the AI overlooks core bulk commodity trade business information.
How to Verify Proper Configuration
- Initiate a multi-turn dialogue including real-time market queries, and check whether returned data update times match the preset context refresh interval.
- Upload an electronic warehouse receipt image or PDF document, and check whether the dialogue correctly extracts transaction fields and unit information from the document.
- View backend session logs to confirm the
messagesparameter only retains the specified number of historical dialogue turns. - Trigger a file upload operation, and check whether backend logs and the front-end interface display consistent upload status.
The values provided are common starting points and should be evaluated against relevant 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.