What Data Looks Like for This Category
Data for this category comes primarily from local public data open platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. Maintained daily documents include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. Data updates occur monthly. Each individual data entry includes structured fields such as data item name, open scope, desensitization method, interface address, and update cycle. Some entries are linked to unstructured interface call instruction documents.
What Constraints These Characteristics Impose on the Vector Model and Indexing Link
The monthly update requirement means incremental indexing logic must be prioritized to avoid extra time from full reindexing. Structured fields make up a large share of the data. Differentiated recall weights must be set for fields tied to party building learning Q&A, such as open scope and desensitization rules. Interface documents are long-form text, so a reasonable chunk length is needed to avoid semantic fragmentation. The large number of data items and clear field boundaries require vector model generated dimensions to match field semantics. Sensitive unfiltered data must be blocked during vector generation to prevent information leaks.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic integrity of single data item descriptions and interface documents in the public data catalog, avoids semantic breaks after long text chunking |
| Overlap Length | 100–200 characters | Ensures semantic coherence between adjacent chunks, covers connected content between data item names and their associated descriptions |
| Similarity Threshold | Calibrated via actual testing | Adapts to recall needs for mixed structured fields and unstructured documents, balances precision and recall rate |
| Maximum Results per Search | Top 5 entries | Matches the single-round query scenario of party building learning Q&A, avoids returning too many irrelevant data items |
maxToken | 8192 | Adapts to the maximum input limit of text-embedding-ada-002, supports vector generation for long interface documents |
defaultToken | 1024 | Adapts to the standard length of single data item descriptions, balances vector generation efficiency and semantic integrity |
Three Common Misconfigurations
- The interface shows an "Indexing" status that does not change for a long time. Possible causes include failing to adapt to the monthly update rhythm of the public data catalog, the incremental index trigger cycle not matching the data update frequency, or setting an overly large chunk length that causes excessively long single-batch processing time.
- An error occurs when calling the vector model. Possible causes include failing to correctly add the locally deployed m3e model to available channels, or the configured
defaultTokenexceeding the maximum input length supported by the model. - No matching data is returned in knowledge base search results. Possible causes include failing to set corresponding recall rules for structured fields in the public data catalog, only searching unstructured text, and missing semantic matching for key fields such as data item names and open scope.
How to Verify Proper Configuration
- Upload a single public data catalog document, check the vector generation log, and confirm there are no sensitive data leakage prompts.
- Trigger an incremental indexing task, verify that the update time matches the monthly data update cycle.
- Enter a query related to party building, confirm that search results include content such as the open scope and desensitization rules of the corresponding data items.
- Check model channel configuration, confirm that the added text-embedding-ada-002 or locally deployed m3e model is in an available state.
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.