What data for this category looks like
This category's data primarily comes from meteorological warning systems, hydrological monitoring systems, emergency command platforms, grassroots disaster reporting systems, and video surveillance. Daily generated documents include duty logs, warning notices, consultation minutes, rapid disaster reports, emergency response plans, and material allocation slips. Data is updated daily; some real-time monitoring data has shorter update intervals. Document fields include rainfall and water level values, displaced resettlement counts, material categories and stock levels, disposal process nodes, and some documents include timestamps and responsible entity identifiers.
What constraints these characteristics impose on vector models and indexes
Scattered data sources that include both structured numerical values and unstructured text require indexes to support vectorization adaptation for multiple content types. Daily updated batch data must align with incremental indexing scheduling rhythms to avoid excessive resource usage from full indexing. Long texts such as consultation minutes and rapid disaster reports require reasonable chunking rules to preserve complete contextual semantics. Short real-time monitoring data entries need to adapt to index update logic for high-frequency writes, while ensuring recall accuracy is not impacted by frequent updates. Timeliness requirements in emergency scenarios set a maximum latency limit for index retrieval, requiring a balance between the number of recalled entries and response speed.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to semantic coherence of long texts such as consultation minutes and rapid disaster reports, avoiding splitting of key information |
| Overlap Length | 100–200 characters | Compensate for contextual gaps after long text chunking, improving accuracy of cross-block information recall |
defaultToken | 4000 token | Match the basic context quota for a single retrieval, adapting to multi-document summarization needs in emergency scenarios |
maxToken | 12000 token | Allow recall of up to 12000 tokens of associated content, meeting information integration needs for complex consultation scenarios |
| Similarity Threshold | 0.75–0.85 | Balance recall accuracy and number of recalled entries in emergency scenarios, avoiding interference from excessive irrelevant content |
| Single Search Limit | Top 10 entries | Control the number of documents returned per retrieval to prevent exceeding the model's context capacity limit |
Three common mistakes
- Phenomenon: Retrieval returns relevant documents but the model prompts that no matching content is found. Cause: The
maxTokenconfiguration value is smaller than the total token count of the recalled content, and the recalled document fragments are truncated, so the model cannot obtain complete information. - Phenomenon: Knowledge base retrieval takes too long. Cause: The
defaultTokensetting is too high, loading too much associated content for a single retrieval, increasing the model's processing load. - Phenomenon: Image documents cannot be indexed correctly. Cause: FastGPT v4.17.0 or a later version is not used, or the corresponding configuration for image vectorization is not enabled.
How to confirm configurations are set correctly
- Upload a typical consultation minute document, and confirm that the chunked fragments retain key disposal process and responsible entity information.
- Initiate a simulated retrieval, and confirm that the number of returned associated documents matches the single search limit configuration, and the total token count does not exceed the
maxTokensetting. - Review the vector model's normalization configuration, and confirm that it is compatible with the embedding model in use, for example, enable the normalization option for Doubao embeddings.
- Test indexing of image documents, and confirm that vectors can be generated normally and participate in retrieval under FastGPT v4.17.0.
The values provided here are common starting points, and should be evaluated against the samples relevant to the specific deployment.
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.