What data for this category looks like
Data for this category primarily comes from elderly care service information platforms, civil affairs statistics systems, fire safety inspection records, and facility operation ledgers. Daily documents include facility registration materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission files. Data updates occur monthly. Most documents combine mixed text and structured fields, including facility identifiers, bed counts, inspection dates, issue items, care levels, and rectification records. No fixed unified format exists for these documents.
Constraints for vector models and indexes
Data updates occur monthly, so the vector model and index workflow does not require high-frequency incremental updates. Use monthly full updates or weekly incremental updates to reduce computing resource consumption. Most documents combine mixed text and structured fields, and cover multiple content types including registration materials, rectification ledgers, and subsidy applications. Retain field association information during chunking to avoid breaking semantic integrity when slicing. The oversight scenario requires recall results to accurately match oversight needs. The index must support recall associated with document types, and adapt to unified vector dimensions across multi-source data.
Configuration Recommendations
For FastGPT v4.17.0, the following configuration items have recommended settings:
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Matches the average text length of documents such as elderly care registration and rectification ledgers, avoids breaking field associations during slicing |
| Overlap length | 100–200 characters | Retains semantic association between adjacent chunks, prevents semantic breaks between rectification records and issue items |
| Similarity threshold | 0.75–0.85 | Filters low-match results, meets the accurate recall requirements of oversight scenarios |
| Single search limit | Top 10 results | Controls the number of returned results, avoids overload while covering valid data after monthly updates |
defaultToken | 4096 | Adapts to the input token limit of mainstream vector models, matches the average length of a single document |
maxToken | 8192 | Reserves sufficient context space to accommodate the spliced length of multiple associated documents |
Three Common Misconfigurations
- Phenomenon: Vectorization requests only pass a single text chunk, without using batch processing. Cause: Batch vectorization trigger logic is not configured, and calls are only initiated for individual text entries.
- Phenomenon: Search results have insufficient matching accuracy with oversight requirements, and cannot accurately match core content such as rectification and subsidy applications. Cause: The similarity threshold is not adjusted to meet the semantic needs of elderly care oversight scenarios, or field associations are broken during chunking.
- Phenomenon: The number of index recall results does not meet expectations, and cannot cover all relevant documents. Cause: The single search limit is set too low, or semantic association of the document is broken during chunking.
How to Verify Correct Configuration
- Review vectorization call logs to confirm chunks are generated using the configured chunk length and overlap length, and that batch requests initiate normally.
- Submit test queries, check that returned result similarity falls within the set threshold range, and that result count does not exceed the single search limit.
- Check index update task logs to confirm monthly full updates run as scheduled, with no failures or delays.
- Verify the configured values of
defaultTokenandmaxTokento confirm they match the token limit range of the currently used vector model.
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.