What data for this category looks like
Data for this category primarily comes from official airport service guides, historical customer service Q&A libraries, real-time terminal announcements, flight adjustment notices, and business rule documents for security checks, check-in, baggage check and other services. There are two update cadences:
- Real-time data such as flight updates and delay notices is synced hourly
- Business rule data such as security requirements and fee standards is updated quarterly or when policies are adjusted
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints these characteristics impose on vector models and indexing
Real-time data’s high-frequency updates require indexes to support incremental synchronization, avoiding performance loss from full index reconstruction. The high proportion of short Q&A pairs requires chunk length to align with vector modeling accuracy for short texts, preventing semantic loss from over-segmentation or under-segmentation. Structured content with clear units needs to retain unit association information during indexing, to avoid confusing different parameters of similar business during vector matching. Additionally, business rule data has a long update cycle. Regular full verification of index consistency can ensure vector accuracy of policy-related content.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to short Q&A and medium-length procedure documents for airport service inquiries, balancing semantic completeness and vector index density |
| Overlap Length | 100–150 characters | Prevents coherent business procedure descriptions from being cut off at chunk boundaries, ensuring cross-chunk semantic association |
| Similarity Threshold | 0.75–0.85 | Meets the accuracy requirements for airport business Q&A, filtering low-relevance non-business search results |
| Single Search Limit | Top 8 entries | Adapts to the scope of common questions for airport inquiries, avoiding returning excessive redundant results |
maxToken | 4096 token | Adapts to the input length limit of mainstream embedding models, ensuring complete vectorization of long procedure documents |
defaultToken | 1024 token | Matches the vectorization requirements for short Q&A pairs, optimizing modeling efficiency for small texts |
Three common configuration mistakes
- Search results remain unchanged for long periods, and the interface shows cache hit status. The cause is that an independent cache expiration policy is not configured for real-time flight data, resulting in dynamic content being cached for extended periods and unable to sync the latest information.
- An error "Training order not completed" is returned when calling the knowledge base interface, with a status code of 400. The cause is confusing the usage scenarios of "add data to collection" and "create training order". Adding data to a collection only supports single short texts, and batch long documents require training orders to complete index construction.
- Search results do not include image-related content, and returned fields are empty. The cause is that the index configuration for image vectorization is not enabled, or the OCR text of images is not bound and stored with business Q&A.
How to confirm the configuration is correct
- Upload a test flight delay notice, wait for index completion, then initiate a search with the same keywords, confirm that results change synchronously with the test data update time.
- Initiate two inquiry queries with highly similar semantics, check whether the matching degree of search results falls within the preset similarity threshold range.
- Try to import a test document containing image OCR text, confirm that search results include text fragments related to the image during retrieval.
- View the vector index update log, confirm that the incremental synchronization frequency of real-time data meets the preset update cycle.
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.