What the Data for This Category Looks Like
Data mainly comes from warehouse management systems, customs declaration systems, transportation scheduling archives, and internal enterprise approval workflow records. Updates are synchronized with the completion of each batch of business. The length of individual file documents varies widely, ranging from several hundred characters for a simple storage receipt to tens of thousands of characters for a complete customs declaration file. Document fields include document number, cargo category, actual shipped quantity, gross weight and net weight, transportation qualification number, and multi-node approval records. Units include pieces, tons, cubic meters, dates, and others.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The wide span of document lengths leads to significant differences in context length. Multi-turn dialogue must dynamically adapt to file content of different lengths to avoid truncating key approval node information. Real-time updated data requires prompts to associate the latest business node records, preventing the use of expired approval status information. The structure with multiple fields and units requires prompts to clearly specify field extraction and unit verification rules, avoiding confusion between business values of different dimensions. The nested structure of multi-node approval records requires multi-turn dialogue to gradually break down approval steps, preventing model understanding deviations caused by inputting too much content at once.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Maximum Context | 8000–12000 characters | Adapts to the span of warehousing files from hundreds to tens of thousands of characters, avoiding truncation of key approval records |
| Question Optimization | Enabled, preset for warehousing approval scenarios | Optimizes field extraction accuracy for document structures with multiple fields and units |
chatId | Bound to the unique identifier of a single approval process | Associates multi-turn dialogue logs under the same scenario, avoiding context confusion |
variables | Preset two variables: cargo category and transportation qualification | Quickly extract core business parameters from files, improving dialogue efficiency |
messages | Split dialogue rounds by approval node | Corresponds to the file structure of multi-node approval, gradually passing approval step information |
| Single Retrieval Limit | Top 5 entries | Matches the number of core fields in a single file, reducing irrelevant information interference |
| Timeout Period | 600 seconds | Adapts to the multi-turn dialogue processing duration for long files |
Three Common Misconfigurations
- Symptom: No subsequent output or empty results after the workflow reaches the AI dialogue step. Cause: Failure to split
messagesaccording to the multi-node structure of warehousing files, leading to excessive context triggering truncation and early termination of the workflow. - Symptom: Confusing units in returned field values, such as mixing tons and pieces. Cause: Question optimization not enabled, or unit verification rules not clearly specified in the prompt, failing to adapt to the multi-unit document characteristics of warehousing logistics.
- Symptom: No streaming output response after calling the API, or content displayed only after returning the full result. Cause: Incorrect configuration of context association for dialogue rounds, or failure to pass the round structure of the
messagesparameter as required, leading to failure of the streaming logic.
How to Confirm Successful Configuration
- Upload a typical warehousing customs declaration file, trigger multi-turn dialogue, and check whether the
chatIdin the dialogue log is bound to the unique number of the file. - Submit a query involving multiple field extractions, and verify whether the returned result accurately identifies fields with units such as cargo category and weight.
- Test three consecutive queries for different approval nodes, confirm that the context is not reset and the dialogue process is coherent.
- Call the API to initiate a dialogue, check whether chunked response content can be returned according to the preset round structure.
The values provided here are common starting points and should be evaluated 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.