What data in this category looks like
Data sources for this category include local public data open platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. Daily produced documents include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. The core of the data is structured fields, including data item name, open scope, desensitization rules, interface address, update cycle and other fields. The update frequency is monthly, and a small number of long-text interface description documents are also included.
What constraints these characteristics impose on the vector models and indexes link
The monthly update frequency requires that the index supports regular incremental synchronization to avoid resource consumption caused by full reconstruction. The data includes structured fields and long-text interface descriptions. When chunking, the association between fields must be retained to avoid damaging the corresponding relationship between data items and authorization boundaries after splitting. Data items have clear open conditions and desensitization rules. Vector generation must retain relevant key information to accurately distinguish the differences between different data items. Long-text interface documents must adapt to the token limits of embedding models to prevent information fragmentation caused by improper chunking.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the structured fields and interface descriptions included in documents of this category, matches the maxToken limit, and avoids splitting that disrupts field associations |
| Overlap Length | 100–200 characters | Retains the associated information of data items and authorization boundaries across chunks, avoiding context fragmentation |
| Similarity Threshold | 0.75–0.85 | Distinguishes differences in open conditions between different data items, filters irrelevant catalog entries |
| Single Search Limit | Top 10 entries | Adapts to the large number of data items in this category, controls the recall range to avoid redundancy |
| Reranking | Enabled | Optimizes retrieval relevance for interface documents and data item descriptions |
defaultToken | 4096 | Adapts to the maximum input length of most embedding models, and matches the text length after chunking for this category |
Three common mistakes
- Phenomenon: After repeatedly configuring embedding models with the same name, the original configuration is overwritten, and retrieval results do not meet expectations. Cause: A unique identifier is not configured for each embedding model, and only the model name is used as the recognition basis.
- Phenomenon: Vector generation fails, and the interface prompts a token overlimit error. Cause: Long-text interface documents of this category are not chunked according to the
defaultTokenlimit, exceeding the maximum input length supported by the embedding model. - Phenomenon: After chunking Excel source data, the association between data items and desensitization rules is lost, and retrieval results cannot match authorization conditions. Cause: The header and corresponding data rows are split during chunking, damaging the association of structured fields.
How to confirm the configuration is complete
- View the index management page of FastGPT v4.17.0, check that the chunk length and overlap length configurations match the set values.
- Perform a retrieval test to verify that the recall results include the associated information of data items and authorization boundaries, and meet the set similarity threshold filtering effect.
- Check the embedding model configuration page to confirm that each model has a unique identifier, and there is no repeated overwriting situation.
- Trigger an incremental update task to verify that the monthly updated index generation is completed normally without abnormal errors.
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.