What This Category of Data Looks Like
Reference only; verify against the target FastGPT release and deployment documentation.
Constraints Imposed by These Characteristics on Multi-Turn Dialogue and Prompt Engineering
The storage structure divided by unit requires that the current session's affiliated unit identifier must be bound during multi-turn dialogue, to avoid confusion between cross-unit systems. The document structure with clear metadata requires that prompts guide the model to prioritize matching the unit, effective date and other fields bound to the dialogue, to reduce the return of irrelevant results. The hierarchy and update logic of systems requires that multi-turn dialogue supports users supplementing or correcting the effective scope and version information of systems, while retaining unit and document number context from historical sessions to avoid repeated inquiries about basic information.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Session context window | 8000–12000 token | Internal system texts are generally long, and key metadata such as unit and document number in multi-turn dialogue must be retained |
| Question optimization switch | Enabled | User questions often omit the unit prefix; after optimization, bound unit information can be automatically supplemented to narrow the retrieval scope |
messages context truncation threshold | Retain the most recent 15 turns of dialogue | Avoid excessive context occupying model computing resources, while covering key interactive context |
chatId binding strategy | Bind the unique identifier of the unit to which the current session belongs | Ensure that retrieval within the same session only matches the system library of the corresponding unit |
| Single recall result limit | Top 8 entries | The unit-based system library has a clear hierarchy; too many results will reduce the model's screening efficiency |
variables global parameter injection | Automatically inject the current session's unit and effective time range | Further narrow the retrieval scope and improve result accuracy |
Three Common Misconfigurations
- Symptom: No return result occurs after a workflow executes to the AI dialogue link, and the same process can be reproduced for a specific question. Cause: The unit parameter in
chatIdorvariablesis not bound, causing the retrieval scope to match irrelevant systems across units, and the model cannot generate a valid answer. - Symptom: Dialogue return results include system documents from other units, and the number of results does not match the configured limit. Cause: The question optimization switch is not enabled, or the unit context from prior dialogue in
messagesis not retained, causing the retrieval scope to not be limited to the target unit. - Symptom: Dialogue logs do not retain unit-related context as expected, and subsequent questions require repeated inquiries about unit information. Cause: The session context window is set too small, truncating prior dialogue content containing the unit identifier, causing the model to lose key binding information.
How to Confirm Correct Configuration
- A question containing a unit name may be initiated. Returned results may be checked to confirm they only include system documents for that unit. Retrieval scope parameters may be adjusted until results meet expectations.
- Consecutive multi-turn questions may be initiated. The
messagesfield may be checked to confirm it retains unit and document number information from prior dialogue. Context truncation thresholds may be adjusted to cover necessary interactive content. - When calling the API,
chatIdmay be checked to confirm it is bound to the unique identifier of the corresponding unit, orvariablesmay be checked to confirm correct unit parameters are injected. The session binding logic may be verified to take effect. - A streaming output request may be triggered. Returned chunked content may be checked to confirm it is returned step-by-step according to system chapters. The recall limit may be adjusted to control the number of results returned per round.
The values provided here are common starting points and should be evaluated against samples from the relevant deployment environment.
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.