Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Hotline knowledge base maintenance data comes from official public policy documents, archived historical tickets, and frontline agent standardized script libraries. Update rhythm synchronizes with policy adjustments, with routine bulk updates performed weekly or monthly. Each individual knowledge base document includes fields such as question category, standard reply text, applicable scenario tags, and effective end date. Reply content is counted in characters, associated tickets are counted in units of tickets, and there is no fixed overall length limit for documents, covering all types of inquiry content ranging from tens to thousands of characters.
What constraints do these characteristics impose on the multi-turn dialogue and prompt engineering link
Because the knowledge base data includes effective date fields, the multi-turn dialogue link must explicitly require returning only reply content within the active validity period in the prompt, to avoid calling expired scripts. The high update frequency requires configuring context logic to automatically filter expired documents in the dialogue flow, preventing calls to invalid policy content. The wide span of document lengths and presence of multi-dimensional classification tags mean the prompt must explicitly specify extracting only the question category and applicable scenario tags that match the current inquiry, to avoid redundant information interfering with dialogue logic. Associated tickets accumulate as business grows, so the number of associated tickets recalled per dialogue turn must be limited to prevent context overload.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Keep the most recent 8–12 turns of dialogue context, automatically filter documents with effective dates earlier than the current time | Hotline knowledge base content has effective cycles; filtering expired content avoids incorrect replies. Retaining 8–12 turns covers most multi-turn inquiry scenarios |
variables | Configure two custom variables: "Current inquiry time" and "Business scenario" | Used to automatically incorporate time validity check conditions in prompts, and match the business category the agent belongs to |
chatId | Bind the current agent's session ID | Hotline services have multiple parallel agents; binding session IDs ensures dialogue contexts from different agents are isolated from each other |
| Max context length | 8000–12000 characters | Adapts to the characteristic that single reply texts in the hotline knowledge base can be up to thousands of characters long, avoiding loss of key information due to context truncation |
| Context recall count | Top 3–5 matching results | Limits recall quantity to prevent redundant information from multi-dimensional classification tags from interfering with dialogue logic |
Three Common Misconfigurations
- Phenomenon: Multi-turn dialogue only completes a single question, and cannot perform follow-up questions or progressive inquiries. Cause: No context retention logic is configured in the
messagesparameter, only single dialogue results are called without carrying over historical inquiry content. - Phenomenon: Responses from all AI dialogue nodes in the workflow are displayed on the final dialogue interface. Cause: No node output hiding rules are configured, or no marker to retain only the target node's output is passed via
variables. - Phenomenon: Expired policy reply content is returned when calling the knowledge base. Cause: No effective date check logic is added to the prompt, and no current inquiry time is passed as a filter condition via
variables.
How to Confirm the Configuration Is Correct
- Import a test knowledge base document with an effective end date, set the current inquiry time to later than the end date, and verify that the document is not recalled in the dialogue result.
- Construct a test dialogue with multiple progressive turns, and verify that the system can carry over historical inquiry content and complete follow-up or supplementary questions.
- Configure multiple AI dialogue nodes in the workflow, and after testing, verify that only the target node's response content is displayed on the interface.
- View the session context log, confirm that only the specified number of dialogue turns are retained, and expired documents have been filtered.
The values provided are common starting points and should be measured 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.