Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
The main data for this category comes from 12345 citizen hotline centers, offices of handling units, subdistricts, towns, and industry regulatory departments. Daily documents include appeal registration forms, dispatch records, handling responses, follow-up visit records, extension applications, return explanations, and knowledge base entries. Fields cover appeal content, caller information, responsible units, handling status, evaluation results, and more. Data sources include the 12345 citizen hotline platform, handling unit business systems, official knowledge bases, follow-up visit evaluation data, and local hotline management measures. The update frequency is once per day.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
The multi-source mixed data structure requires retrieval to match both colloquial appeal content and standardized business fields. The daily update feature requires the retrieval system to support high-frequency incremental synchronization. The field complexity of daily documents means retrieval needs to balance precise matching and contextual association, to avoid irrelevant results interfering with document drafting. The most time-consuming business links are appeal type judgment and response generation, so retrieval must return highly relevant results in a short time. Otherwise, it will directly affect work order handling efficiency, leading to issues such as overdue deadlines and returned responses.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Filter low-relevant historical work orders and policy entries, retain matching results that meet business requirements |
limit | Top 10 entries | Control the total character count of returned results, avoid exceeding the context window limit |
searchMode | Semantic search | Adapt to the colloquial expression of appeal content, improve matching accuracy with professional knowledge base entries |
usingReRank | Enabled | Re-rank recalled results, place highly relevant entries first, optimize reference efficiency for document drafting |
datasetSearchUsingExtensionQuery | Enabled | Automatically convert user appeals into professional search terms, optimize matching effects for cross-domain content |
collectionIds | Configured by handling unit category | Distinguish business knowledge bases of different industries, avoid irrelevant content from other units interfering with retrieval |
Three common errors
- Phenomenon: The knowledge base retrieval API call takes more than 30 seconds to return results. Cause: The
limitparameter is not set to restrict the number of recalled entries, orusingReRankis not enabled to optimize the sorting logic, resulting in a large number of low-relevant results participating in calculations. - Phenomenon: Target content cannot be retrieved after inserting documents via API. Cause: Data is not uploaded according to the standard field structure of daily documents, resulting in missing document metadata that cannot be recognized by the retrieval engine.
- Phenomenon: A resource restriction prompt appears when importing knowledge base documents in the community edition environment. Cause: The single dataset storage and synchronization frequency limits of the community edition are not followed, resulting in import failure.
How to confirm the configuration is correct
- Upload a typical appeal registration form document, search for the corresponding appeal keyword, and check whether the number and sorting of returned results meet business reference needs.
- Call the retrieval interface, check the response time of a single request, and confirm that it meets the time efficiency requirements of work order handling.
- Check the dataset synchronization logs, confirm that the daily updated work order data has been correctly imported into the knowledge base with no import failure records.
- Adjust the
similaritythreshold, compare retrieval results under different thresholds, and confirm that the matching accuracy adapts to the business scenario.
The values provided here 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.