What This Category's Data Consists Of
Daily documents for this category include filing materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission records. Data sources include elderly care service information platforms, civil affairs statistics systems, fire inspection records, and institution operation ledgers. Data is synchronized in monthly batches. Documents include both structured fields (such as institution filing number, number of beds) and unstructured explanatory text. Fields cover oversight-related dimensions: institution qualifications, service status, safety status, and more. No high-frequency real-time updates occur.
Constraints Imposed on Vector Models and Indexing
The monthly batch update rhythm requires indexes to support periodic full refreshes. Avoid frequent incremental indexing to reduce computing resource consumption. The multi-field mixed document structure requires retaining field association information during chunking. This prevents loss of binding relationships between institution filing numbers and corresponding rectification records after splitting. The coexistence of unstructured text and structured fields requires indexes to support both semantic recall and precise field matching. Data sources scattered across multiple systems require unified formatting before index construction. This prevents recall deviations caused by inconsistent field values.
Configuration Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fits the average text length of elderly care service documents, avoids losing business associations after splitting |
| Overlap Length | 50–80 characters | Retains contextual connections between adjacent chunks, ensures semantic coherence of policy clauses |
defaultToken | 4096 | Meets the long context requirements of policy text, aligns with the default parameter range of FastGPT v4.17.0 |
maxToken | 8192 | Supports full loading of complete content for single filing or subsidy application documents |
| Similarity Threshold | 0.72–0.80 (calibrated via actual testing) | Balances precise recall and coverage for policy Q&A, adapts to the strict requirements of oversight scenarios |
| Single Search Limit | Top 6 entries | Controls the number of returned results, avoids excessive redundant information interfering with oversight staff judgments |
Three Common Mistakes
- Phenomenon: Index tasks stay in the "indexing" state for extended periods with no progress updates. Cause: The monthly batch update rhythm of elderly care service documents is not addressed, and frequent incremental indexing is enabled. This leads to exhausted computing resources and a stuck task.
- Phenomenon: Custom index configuration produces unexpected recall results, with no matching policy basis content returned. Cause: Incorrect chunk length settings cause loss of complete semantic meaning of policy clauses after splitting. Or data formats from multiple systems are not unified prior to index construction.
- Phenomenon: Local m3e model deployment cannot complete vectorization, with a model loading failure prompt displayed. Cause: The local deployment path of the model is not correctly configured in FastGPT v4.17.0. Or the token length range supported by the model is not matched.
How to Confirm Proper Configuration
- Upload a single elderly care institution filing material, and verify that chunked text retains the association between the institution filing number and corresponding application content.
- Initiate a policy basis Q&A test, and confirm that the similarity scores of recalled results fall within the preset threshold range.
- Review index task logs, and confirm that batch update task completion times align with the monthly synchronization rhythm.
- Test custom index configuration, and verify that structured fields are correctly associated with corresponding text chunks.
The values provided here are common starting points, and should be evaluated 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.