What the data for this category looks like
Data sources for this category include the 12345 hotline platform, contractor business systems, official knowledge base, return visit evaluation data, and local hotline management regulations. The update frequency is daily. Daily generated documents include request registration forms, dispatch records, handling replies, return visit records, extension applications, return instructions, and knowledge base entries. Each document includes core fields such as request number, contracting entity, handling progress, and reply content. The overall process revolves around the full lifecycle flow of public requests.
What constraints do these characteristics impose on the multi-turn dialogue and prompt configuration link
This category’s data updates daily. Multi-turn dialogue links must sync the latest policy and work order data to avoid referencing outdated content. Multiple document types require separate retrieval scopes based on usage. Policy-based Q&A must prioritize calling official knowledge base entries. Queries related to work order handling must link to the corresponding request’s registration and dispatch records. The unique identifier of a request must be passed to the dialogue context via variables to ensure accurate matching of the corresponding work order’s handling process and policy requirements across multi-turn dialogue, avoiding cross-request confusion. Data sources are scattered. Prompts must explicitly define retrieval scopes, only calling policy documents related to 12345 hotline work order handling.
How to configure the setup
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Retain the first 8000 tokens of dialogue history, calibrated via actual testing | Adapt to the context requirements of 12345 work order multi-turn interactions, avoid exceeding the model’s context window |
variables | Pass two core variables: request number and contractor unit name | Ensure accurate matching of the corresponding work order’s handling records and policy requirements during dialogue |
| Question Optimization | Enable, set threshold to 0.7–0.9 | Standardize colloquial user requests to match retrieval keywords in policy documents |
| Single Retrieval Limit | Top 5 results | Adapt to the retrieval accuracy of policy documents, avoid interference from redundant information |
| Timeout Setting | 600 seconds | Align with the review process duration of work order handling, avoid dialogue interruption due to timeout |
Three common misconfigurations
- Phenomenon: Under the same prompt configuration, FastGPT’s policy basis reply accuracy is lower than expected. Cause: The retrieval scope is not limited to 12345 hotline-specific policy documents, causing the model to call irrelevant general policy content.
- Phenomenon: After calling the workflow, the dialogue log fields for the current and previous day are empty. Cause: The unique identifier of the corresponding session is not bound in the
chatIdconfiguration, causing the system to fail to correctly store or retrieve dialogue records. - Phenomenon: The workflow dialogue interface shows a call failure, but the model backend receives response logs. Cause: The core work order variables in
variablesare not correctly configured, causing the model to fail to match corresponding business data and return invalid responses.
How to confirm the configuration is correct
- A test dialogue with a real request number is executed. Check if the reply links to the corresponding work order’s handling process and policy basis content.
- Dialogue logs are reviewed. Confirm that the
messagescontent of the last 3 rounds is fully retained, with no truncation or loss. - A workflow call is triggered. Check if required fields such as request number and contractor unit are passed in
variables. - A long dialogue scenario is simulated. Confirm that the dialogue completes response within 600 seconds, with no abnormal interruption prompts.
The values given 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.