What the data for this category looks like
Survey and design internal office data primarily comes from project management systems, design documents exported from CAD, internally shared industry specifications, and project change orders. Data update frequency is adjusted according to project progress: real-time updates when project changes occur, and quarterly synchronization for specification documents. Documents include structured fields (project ID, person in charge, seismic intensity, working hours) and unstructured content (design descriptions, drawing annotations). Units include days, hours, MPa, intensity levels and other professional fields.
What constraints these characteristics impose on the multi-turn dialogue and prompt words link
The mixed structured and unstructured data in survey and design requires multi-turn dialogue to distinguish between structured ledger records and unstructured design content. Prompt words need to adapt to professional terminology and unit verification. Frequently updated change orders and project documents require that dialogue contexts synchronize the latest data in a timely manner, to avoid using expired content. Long-text design descriptions occupy a large amount of context window space, so it is necessary to limit the introduction of invalid redundant information while ensuring the recognition accuracy of professional terminology.
How to set the configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Maximum Context | 8000–12000 characters | Adapts to long-text design descriptions and project ledgers for survey and design, and can stably carry multi-turn dialogue history under FastGPT v4.17.0 |
variables | Preset project ID, professional domain variables | Matches fixed business fields for survey and design, reduces repeated prompt word writing |
| Single Search Limit | Top 6 results | Avoids excessive retrieval results interfering with the clarity of multi-turn dialogue context |
messages | Retain the most recent 10 rounds of dialogue content | Balances context completeness and system response speed |
| Query Optimization | Calibrate based on actual testing | Adapts to scenarios with many professional terms in survey and design, adjusts query word accuracy |
| Reranking | Sort by professional relevance | Prioritizes returning specifications and drawing content matching the current design task |
Three common mistakes
- Symptom: Returns a 422 status code when calling AI dialogue, and cannot retrieve obtained document content. Cause: The context format of the
messagesparameter is not configured correctly, causing the system to fail to recognize the historical context of multi-turn dialogue, triggering parameter verification failure. - Symptom: Local conversation records disappear after the dialogue window is refreshed, but complete conversations can be viewed in the backend. Cause: Local cache configuration for
chatIdis not enabled, and only backend storage is relied on, leading to front-end session loss. - Symptom: After upgrading the FastGPT version, some historical conversation records cannot be loaded in the front-end window. Cause: The project-related field mapping in
variablesis not updated synchronously, leading to context matching failure and inability to associate with original sessions.
How to confirm the configuration is complete
- Initiate a multi-turn dialogue containing professional terms (such as seismic intensity, load level), check whether the
messagesparameter correctly transfers the role and content of each round, with no format errors. - Manually refresh the dialogue window, verify the consistency between front-end session records and backend storage, and confirm that the cache configuration for
chatIdtakes effect. - Trigger a query containing a preset project ID, check whether the business fields configured in
variablesare correctly called, and match the corresponding knowledge base content. - Turn off the user-visible output switch, initiate an internal debugging query, and verify that professional prompt words will not be visible to regular employees.
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.