What this category’s data looks like
Civil aviation airport policy and regulatory basis data comes from three main sources: industry regulations issued by civil aviation authorities, internal airport operation management systems, and air traffic control coordination documents. Document structure includes fields such as release document number, issuing unit, effective date, clause number, applicable scenario, and specific clause content. Clause numbers use the "Article X" format, and effective dates follow the YYYY-MM-DD standard. Update cycles align with civil aviation authority regulation revision cycles. Internal airport systems are updated quarterly or annually. Single document length typically ranges from several thousand characters.
What constraints these characteristics impose on vector models and indexing
The official nature of civil aviation policy data requires vector indexes to retain metadata including release document number and effective date. This enables precise filtering and matching of policy content from specific time periods. The clause-based document structure requires chunking operations to preserve the integrity of individual clauses. Splitting content across clauses causes semantic breaks, so this must be avoided. Fixed update cycles require vector indexes to support batch re-embedding triggered by update periods. The system must also support temporary incremental update scenarios. The existence of multilingual policy documents requires vector models to support cross-language vector generation. This prevents confusion between content in different languages.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average length of single clauses in civil aviation policy documents, preventing cross-clause chunking |
| Overlap Length | 50–100 characters | Preserves contextual association between adjacent clauses, avoiding semantic breaks |
| Similarity Threshold | 0.75–0.85 | Policy content requires high precision, so a high matching threshold is needed to filter irrelevant recall results |
| Single Search Limit | Top 8 results | The number of civil aviation policy clauses is limited; excessive results will interfere with precise matching |
maxToken | 8000 token | Covers the associated context of relevant policy clauses, meeting the complete semantic requirements of Q&A |
defaultToken | 2000 token | Adapts to the vector generation length of single policy clauses, preventing truncation of critical content |
Three common errors
- Calling the vector model API returns 404 Not Found. The cause is incorrect path format when configuring the embedding model’s API endpoint, which does not match official required interface specifications.
- After batch updating knowledge base vectors, some document indexes disappear automatically after several hours. The cause is not enabling the knowledge base persistent index storage configuration in FastGPT v4.17.0. Temporarily generated indexes are not saved persistently in this case.
- Multilingual policy documents have low recall rates. The cause is failing to retain original language tags during chunking. This prevents the vector model from correctly distinguishing content in different languages, leading to cross-language mismatches.
How to confirm proper configuration
- Upload a single civil aviation policy document, then check vector generation logs. Confirm there are no errors, and that metadata such as release document number and effective date is correctly associated with vector entries.
- Initiate a query targeting a specific policy clause. Verify the number of recall results matches the single search limit setting, and that results include matching clause content.
- Replace the embedding model, then trigger a batch re-embedding task. Wait for the task to complete, then verify recall results for multilingual policy documents meet expected matching accuracy.
- Call the Q&A API, then check that context length does not trigger truncation errors. Confirm the configured values of
maxTokenanddefaultTokenare suitable for the current Q&A scenario.
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.