Reference only; verify against the target FastGPT release and deployment documentation.
What This Category of Data Looks Like
The data for this category mainly comes from consultation records at government and enterprise service windows, officially released service guidelines, policy interpretation documents, and standardized common question banks. Data updates are triggered on demand as policies adjust or window processes change, with no fixed cycle. A single data entry mostly includes structured fields such as title, applicable scenario, handling steps, consultation replies, and contact channels. Some documents have supplementary information such as document numbers and handling time limits. The length of data varies widely, ranging from a few hundred words for common questions to several thousand words for complete service process descriptions.
Constraints Imposed on Vector Models and Indexes
This category of data has wide variation in length and obvious structural differences. Some long documents need to be split to fit the input limits of vector models. Structured fields such as handling time limits and document numbers require separate weight marking. The on-demand update feature requires indexes to support incremental synchronization, avoiding resource consumption from full reconstruction. Consultation records are mostly colloquial, differing from the written language in policy documents. Indexes need to adapt to consistent vectorization of mixed text. In addition, some data contains sensitive government-related information. The indexing process must comply with data desensitization requirements, avoiding plaintext storage of sensitive content.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the average length of government and enterprise service documents, avoids single chunks exceeding model input limits |
| Overlap Length | 100–200 characters | Retains contextual connection between chunks, prevents loss of coherent service process information after splitting |
maxToken | 8192 token | Matches the input upper limit of mainstream vector models, adapts to long document vectorization needs |
| Similarity Threshold | 0.75–0.85 range | Filters low-match irrelevant service information, reduces invalid recall |
| Single Search Limit | Top 10 entries | Controls the number of recall results, adapts to the precise reply needs of government and enterprise consultations |
defaultToken | Calibrated based on actual testing | Adapts to the average text length of the dataset, optimizes index construction efficiency |
Three Common Misconfigurations
- Dataset import shows "Indexing in progress" for an extended period, lasting several hours without completion. Cause: Chunk parameters are not adjusted based on the length of government and enterprise service documents. Single chunks are too long to exceed vector model input limits, triggering repeated retries or timeout blockages.
- Vector calculation scores are abnormally high, and multiple results have identical scores. Cause: Reasonable weights are not set for structured government fields such as document numbers and handling time limits, leading to excessive matching of duplicate content or fixed fields.
- Vectorization time far exceeds expectations after uploading long documents such as PDFs. Cause: Chunk parallel processing configuration is not enabled. Single-threaded processing of long documents leads to low index construction efficiency.
How to Verify Proper Configuration
- Check the dataset's index construction logs to confirm split text length does not exceed vector model input limits.
- Randomly select 10 consultation questions, perform a recall test, and verify returned results match within the preset threshold range.
- Import a single long document, verify index construction time meets preset performance requirements, with no timeout errors.
- Check desensitization configuration for sensitive government fields, confirm plaintext sensitive information is not stored in the vector index.
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.