Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Data for this category primarily comes from engineering construction project approval management systems, construction drawing review systems, enterprise qualification databases, and public credit platforms. Commonly processed documents include construction permit application materials, construction drawing review qualification certificates, quality and safety supervision procedure documents, and completion record forms, among others. The data update frequency is daily. Each individual document includes fields such as approval nodes, participating construction unit qualifications, personnel information, and material compliance. Fields adjust dynamically with approval stages, and there are no unified fixed units.
What constraints do these characteristics impose on vector models and indexing
Scattered data sources lead to large format differences for single-batch indexed documents. Adaptation rules are required for multi-source heterogeneous data chunking. The daily update frequency requires indexes to support incremental synchronization mechanisms, to avoid repeated full index execution. Documents contain professional engineering terms such as qualifications and approval nodes. Vector models that cover construction domain vocabulary must be selected. Associations between participating unit information and approval nodes require indexes to retain field-level associations, to prevent loss of cross-document association information.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of engineering documents, avoid splitting professional terms |
| Overlap Length | 100–150 characters | Retain cross-chunk professional term associations, prevent context breaks |
maxToken | 8192 | Adapt to the input length limit of mainstream large models, cover complete approval process documents |
defaultToken | 2048 | Match the vector output length of a single indexed chunk, balance storage efficiency and recall accuracy |
| Single Search Limit | Top 10 entries | Cover multi-node association information in approval ledgers, avoid missed recalls |
| Reranking | Enabled | Perform secondary sorting on recalled approval-related documents, improve professional content matching |
Three common misconfigurations
- After configuring a vector model, the interface prompts a connection failure, with the console returning a 400 status code. The cause is failing to correctly configure the third-party vector model’s API key and access address, or failing to bind the corresponding vector model in the channel configuration.
- After index creation, the number of recall results is insufficient, with 0 matching results returned when searching ledger data. The cause is that the chunk length is set too large, preventing professional approval documents from being fully split and indexed.
- Incremental index synchronization times out, with the task status remaining in processing for a long time. The cause is that the
maxTokenparameter is not set reasonably, and the daily updated approval data volume is not processed in batches.
How to confirm the configuration is properly set
- A single construction permit application document is uploaded, index creation is initiated, and the chunk preview is checked to confirm it retains complete qualification fields and approval node information.
- Queries such as "participating unit qualification verification" are entered, and the recalled results are checked for documents from the corresponding approval system.
- Vector model call logs are reviewed to confirm each request returns normal vector output with no error messages.
- An incremental index synchronization is manually triggered, and the data update status is checked to confirm it matches expectations after the task completes.
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.