What Data for This Category Looks Like
Daily documents for this category include basic government service item catalogs, implementation lists, service guides, standard material catalogs, flowcharts, and frequently asked questions. Data is primarily sourced from the national basic government service item catalog, administrative licensing item list, and local government service item databases. Updates occur monthly. Each individual document includes fields such as item name, acceptance conditions, application material list, handling process, and handling hierarchy. Some documents include quantitative descriptions of material quantities. The data structure has clear hierarchies but a large number of fields.
Constraints on Vector Models and Indexing
The monthly update schedule requires indexes to support incremental synchronization. This avoids excessive resource usage from full index reconstruction. Multiple document types and field structures require indexes to adapt to vectorization results for texts of varying lengths and topics. This prevents semantic fragmentation. Each individual document contains multiple associated business fields. Context associations between fields must be retained during chunking to preserve business logic after vectorization. Cross-hierarchy data comparison demands high recall accuracy for indexes. This ensures matching accuracy between superior catalogs and local implementation lists.
Configuration Settings
| Configuration Setting | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Fields such as acceptance conditions and application materials in government service documents have moderate lengths. This range covers the complete semantics of a single business content segment, avoiding overly fragmented or overly long chunks |
| Overlap Length | 100–150 characters | Retains context associations between adjacent chunks, ensuring business logic between item fields is preserved after vectorization |
| Similarity Threshold | 0.75–0.85 | Government data requires high matching accuracy. This range filters low-relevance results and prevents inconsistent online service standards |
| Single Recall Limit | Top 10 entries | Similar matching results for government items do not need to be excessive. 10 entries cover main comparison needs and meet organization efficiency for ledger summary |
defaultToken | 4096 | The default parameter of FastGPT v4.17.0 adapts to the vectorization length of most government service documents, ensuring basic vectorization accuracy |
maxToken | 8192 | Supports complete vectorization of longer service guide and implementation list documents, avoiding truncation of critical business information |
Three Common Mistakes
- Phenomenon: Indexing tasks remain stuck in the "indexing" state and cannot complete, or timeout errors are returned during calls. Cause: Chunking and token parameters are not adjusted for government service long texts, leading to token limit exceeded errors or excessively long processing times during vectorization.
- Phenomenon: The number of entries in the dataset increases automatically over time, with multiple sets of duplicate index content appearing. Cause: Deduplication rules for incremental synchronization are not configured. Monthly updates re-import existing government service item data, leading to duplicate index generation.
- Phenomenon: Tasks get stuck after configuring
text-embedding-3-large. Restarting the service after commenting out the model does not resolve the issue. Cause: Residual index cache is not cleared, or configuration files do not take full effect, causing the service to still attempt to call the commented model interface.
How to Verify Proper Configuration
- A single typical service guide document is imported, and chunked text fragments are checked to confirm they retain complete business field associations, with no obvious truncation or semantic fragmentation.
- An incremental synchronization task is initiated, and the number of index entries is verified to match the volume of new data, with no duplicate entries generated.
- Cross-hierarchy data matching is tested, and recall result similarity is confirmed to fall within the preset threshold range, with no low-relevance false matches.
- FastGPT v4.17.0 runtime logs are checked to confirm no token limit exceeded error prompts appear during vectorization.
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.