What This Category of Data Looks Like
This category of data primarily comes from local public data open platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. The update frequency is once per month. Commonly processed documents include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, update frequency descriptions, and more. Data fields include data item names, open scope, desensitization requirements, interface addresses, update cycles, authorization conditions, and others. Most are a combination of structured fields and unstructured descriptive text.
What Constraints These Characteristics Impose on Vector Models and Indexing
Batch data updated monthly requires the indexing process to support switching between incremental synchronization and full re-run, to avoid re-indexing historical data. Heterogeneous data from multiple sources requires unified chunking rules to adapt to different document formats. Long texts in interface documents need to be split to fit the vector model's token limits. For mixed data of structured fields and unstructured descriptions, configuration for field-level indexing and full-text indexing must be distinguished, to ensure retrieval accuracy for key fields such as authorization conditions and desensitization rules. At the same time, data leakage risks require that only desensitized data is strictly retained during the indexing process, and original sensitive information must not be stored.
How to Configure the Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-block text length of documents such as public data catalogs and interface documents, to avoid exceeding the maxToken limit |
| Overlap Length | 100–200 characters | Retains contextual association across chunks, ensuring semantic coherence of data item descriptions |
| Similarity Threshold | 0.72–0.85 | Distinguishes similarity between public data items, avoiding retrieval of unrelated desensitization rule content |
| Single Search Limit | Top 10 entries | Matches the needs of internal office scenarios for precise queries of data items by employees |
defaultToken | 8192 | Adapts to the token limit of the text-embedding-3-large model, meeting processing requirements for long interface documents |
| Maximum Context | 4000–6000 tokens | Balances information completeness of retrieval recall and model inference efficiency |
Three Common Misconfigurations
- The symptom is that indexing tasks remain stuck in the "indexing" state for a long time and cannot enter the ready state. The cause is that incremental synchronization rules adapted to batch public data are not configured, and reasonable
maxTokenchunking limits are not set during full indexing, resulting in model call timeouts. - The symptom is that the data volume in the dataset automatically doubles or even triples. The cause is that the automatic indexing switch for duplicate data is not turned off, and old index entries are not cleared before each monthly update, resulting in repeated superposition of historical data and newly generated indexes.
- The symptom is that tasks get stuck after changing
defaultTokento text-embedding-3-large, and recovery is not achieved after commenting out the model configuration and restarting the service. The cause is that residual index cache files are not cleaned, and service restarts do not synchronously update the dependency parameters of the vector model, causing the process to still attempt to call the old model's configuration items.
How to Confirm the Configuration Is Correct
- Upload a single data open list document, check that the chunked text length falls within the preset chunk length range, with no truncation or overflow.
- Trigger a full indexing task, view the indexing progress logs, confirm that the incremental synchronization logic only processes newly added data from the past month, with no repeated indexing of historical data.
- Retrieve desensitization rule documents, verify that after similarity threshold filtering, only results with matching degrees within the preset range are returned, with no unrelated content mixed into the retrieval results.
- Restart the service and submit an indexing task again, confirm that the task can start normally and complete, with no stuck or abnormal exit phenomena.
The values provided 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.