What this category’s data looks like
Aviation airport investment research data is mainly sourced from Civil Aviation Administration public operation reports, airport monthly operation statements, flight schedule rosters, airspace control policy documents, infrastructure project plans, and airline joint operation announcements. Data update cycles cover daily flight dynamics, weekly bridge usage frequency statistics, monthly passenger throughput data, quarterly airspace traffic reports, and annual infrastructure plans. Document structures include structured tabular data, semi-structured operation analysis paragraphs, and unstructured policy interpretation texts. Core fields include takeoff and landing sorties, passenger throughput, bridge usage frequency, and airspace traffic. Corresponding units are sorties, person-times, times/hour, and cubic meters/second.
What constraints these characteristics impose on the knowledge base retrieval and recall link
The structured proportion of aviation airport investment research data is high, and fields are highly specialized. The retrieval link must match both keywords and vector features, to avoid professional term ambiguity caused by relying only on vector recall. Data update frequencies vary widely. Flexible switching between incremental and full updates is required to ensure timeliness of data across all dimensions. Long documents contain cross-page table and paragraph associations. Field context must be retained during chunking, otherwise the complete semantics of the data will be compromised. Field naming varies slightly across different documents. Unified field mapping rules must be implemented before retrieval, to avoid field confusion in recall results.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Aviation airport documents often contain long tables and professional paragraphs. Excessive length will split field associations, while insufficient length will lose contextual coherence |
Similarity Threshold | 0.72–0.80 | Investment research scenarios require precise matching of professional terms such as airspace and flights. A threshold that is too low will introduce irrelevant general operation data |
Recall Count | Top 6–8 results | Single aviation airport investment research documents carry large amounts of information. Excessive recall will exceed the context window limit |
maxContext | 1500–2000 tokens | Matches the knowledge base chunk length and total length of retrieval results, to avoid truncation of key operation data fields |
Incremental Update Trigger Cycle | Every 24 hours | Adapts to the regular update rhythm of airport flight dynamics and operation data, to ensure data timeliness |
Reranked Return Count | Top 3–4 results | Investment research decisions require support from core data. Excessive return results will disperse information weight |
The parameter values provided on this page are all conventional recommendations, used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on internal samples before finalizing the settings.
Three common mistakes
- Phenomenon: After uploading a long PDF document, retrieval results have low matching accuracy and cannot accurately extract flight schedule or throughput data. Cause: Structured parsing for tables and professional terms in the document is not enabled, and field associations are not retained during chunking.
- Phenomenon: In FastGPT version 4.6.7, when
Chunk Lengthis set to 5000 tokens andmaxContextis set to 1500 tokens, retrieval results that exceed the context limit are still returned. Cause: TheReranked Return Countis not adjusted synchronously, causing the total recalled content to exceed the window limit. - Phenomenon: Retrieval results do not reflect the latest flight schedules or airspace control policies after the knowledge base is updated. Cause: The incremental update trigger mechanism is not configured, and the full update process is still used, resulting in delayed data updates.
How to confirm correct configuration
- Upload a typical airport monthly operation statement PDF, and check whether the parsed text blocks retain the association between fields such as takeoff and landing sorties and passenger throughput.
- Initiate a retrieval targeting professional terms, and verify that the similarity of returned results meets the preset threshold range.
- Manually trigger an incremental update, and check whether only newly added operation data is synchronized in the knowledge base update log.
- Test setting different
Recall Countvalues, and confirm that the total final returned context length does not exceed themaxContextlimit.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.