Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Typical data sources for this category include meteorological early warning systems, hydrological monitoring systems, emergency command platforms, grassroots disaster reporting systems, and video surveillance. Daily documents cover duty logs, early warning notices, consultation minutes, rapid disaster reports, emergency response plans, and material allocation slips. Some documents include structured fields such as rainfall in millimeters, flow in cubic meters per second, quantity of materials, and number of transferred people. Data updates occur daily, with most being real-time or near-real-time business data.
What constraints do these characteristics impose on the knowledge base retrieval and recall workflow
The daily and near-real-time update rhythm requires the retrieval link to support incremental synchronization and fast recall. Full scans will slow response speeds, so they must be avoided. Multiple heterogeneous data sources and mixed structured and unstructured document types require retrieval to balance semantic matching and precise keyword matching. This adaptation supports query needs for different fields. The high timeliness requirement of emergency response means retrieval results must return core information quickly. Low-correlation data must be filtered to reduce subsequent processing burdens.
How to configure the system
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75-0.85 | Flood control data has high requirements for field accuracy. This range filters low-correlation redundant results and avoids interference with emergency judgment |
limit | Top 6-10 results | Emergency consultation and duty scenarios require quick browsing of core information. Excessive results increase information screening time costs |
usingReRank | Enabled | For multi-source heterogeneous data, reranking optimizes retrieval result relevance ranking. It raises the priority of core disaster and early warning information |
datasetSearchUsingExtensionQuery | Enabled | Emergency consultation questions often use colloquial expressions. Extended queries match terminology in professional documents and improve recall coverage |
collectionIds | Bind dedicated flood control emergency dataset ID | Isolate non-relevant business data. It prevents unrelated documents from mixing into retrieval results and ensures judgment accuracy |
Three common misconfigurations
- Issue: Chinese characters display garbled characters after importing a CSV template. English content displays normally. Cause: The CSV file was not saved with UTF-8 encoding, or the matching encoding format was not selected during import.
- Issue: The number of retrieval results is far lower than the set
limitvalue. Cause: The dataset has insufficient document chunks that meet thesimilaritythreshold, orcollectionIdsare bound to an incorrect dataset scope. - Issue: Disk usage statistics for locally deployed knowledge bases are inaccurate. Cause: The embedding vector storage directory size was not counted, or temporary cache files were mistakenly included in total usage.
How to confirm correct configuration
- Upload a test rapid disaster report CSV file. Check that Chinese content displays normally after import with no garbled characters.
- Initiate a test query with keywords such as rainfall conditions and water conditions. Verify that the number of returned results matches the
limitsetting, and that similarity values fall within thesimilarityrange. - Check the locally deployed system storage directory. Confirm that original files, chunked files, and vector data are stored in their respective paths, and that disk usage statistics include all three data types.
- Enable
usingReRank, then compare retrieval result sorting before and after enabling. Confirm that higher-relevance documents appear at the top of retrieval results.
The values provided here are common starting points and should be evaluated 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.