Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Core data for this category comes from the national basic catalog of government service items, administrative licensing item list, power and responsibility list, and local government service item databases. Daily maintained documents include basic government service item catalogs, implementation lists, service guides, standard material catalogs, flowcharts, and frequently asked questions. The data update cycle is monthly. A single item usually contains structured fields such as item name, implementing entity, acceptance conditions, application materials, and handling process. Some supporting documents are unstructured process descriptions or frequently asked question texts.
Constraints on vector models and indexing workflows
Monthly updated bulk data requires the indexing process to support incremental synchronization, to avoid resource consumption caused by full reconstruction. Mixed structured and unstructured data types require vector models to adapt to semantic alignment of both item field text and free question-and-answer content. Strong business correlations exist between each item field. For example, acceptance conditions are bound to application materials. Indexing must retain field correlation information to avoid semantic fragmentation. A data volume of hundreds of thousands of items requires the index structure to balance recall accuracy and query speed, while adapting to the storage and retrieval characteristics of the PGSQL vector database.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of government service item fields and supporting documents, avoids semantic fragmentation in a single chunk |
| Overlap Length | 100–200 characters | Retains semantic association between adjacent chunks, prevents loss of cross-chunk business information |
| Similarity Threshold | 0.75–0.85 | Filters low-correlation recall results, adapts to the strong semantic matching requirements of government service items |
| Single Recall Limit | Top 20 items | Balances query speed and recall coverage, meets the batch verification scenarios of internal office work |
defaultToken | 8192 | Adapts to the maximum input limit of current mainstream vector models |
maxToken | 16384 | Adapts to the vector generation requirements of long-text service items |
Three common configuration mistakes
- Interface displays excessive search latency, and the performance bottleneck is incorrectly attributed to the vector model. Cause: The time-consuming links between vector retrieval and large language model integration results are not distinguished. After long-text vector recall for government service items, the large language model must splice multiple chunks of semantic content, causing delays.
- An error "No available embedding model" pops up when calling the knowledge base. Cause: The text-embedding-ada-002 model is not correctly added to the API channel, or the call parameters for the corresponding model are not configured in the configuration file.
- After bulk indexing data, the retrieval results of some items deviate significantly from the original text semantics. Cause: The chunk length is set too large, causing semantic fragmentation within a single chunk, or the overlap length is insufficient, leading to loss of cross-chunk business correlation information.
How to verify a complete configuration
- Import a single complete government service guide document, check the vector generation log, and confirm that the values of
defaultTokenandmaxTokenare compatible with the currently used vector model. - Initiate a query for a specific acceptance condition, verify the matching degree of the recall results, and adjust the similarity threshold to meet business verification requirements.
- Trigger an incremental data synchronization, check the indexing task log, and confirm that the monthly updated incremental synchronization process runs normally.
- Import bulk test data, verify that the single recall limit setting meets the result quantity requirements for internal verification.
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.