What This Category’s Data Looks Like
Data sources for satellite communications intelligent due diligence reports include satellite orbit monitoring databases, ground station operation logs, frequency band occupancy detection reports, link performance test documents, and more. The data updates dynamically based on events such as orbit adjustments and frequency band allocation changes, with no fixed cycle. Document structure centers on structured parameter tables, paired with unstructured link analysis and risk assessment paragraphs. Fields include orbital inclination, downlink bandwidth, signal delay, and ground station coverage radius, with corresponding units of degrees, Mbps, ms, and kilometers respectively.
Constraints on Vector Models and Indexing
The coexistence of structured parameters and unstructured text requires vector models to encode both numeric fields and natural language paragraphs, to avoid losing structured information from single encoding approaches. Dynamically updated data sources require indexes to support incremental refresh, to avoid resource consumption from full index rebuilding. Fields with clear units require segmentation and encoding steps to retain unit associations, preventing confusion between similar parameters with different units. Long link analysis paragraphs require segmentation strategies that balance semantic completeness and retrieval granularity, to avoid splitting complete link logic into overly small retrieval units.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Balances semantic completeness and retrieval granularity for the relatively long link analysis paragraphs in satellite communications reports |
chunk_overlap | 100–150 characters | Prevents cross-segment link logic from being split, ensuring retrieval semantic coherence |
vector_search_top_k | Top 10 entries | Covers multi-dimensional due diligence data including orbits, frequency bands, and ground stations, meeting comprehensive retrieval requirements |
similarity_threshold | 0.75–0.85 | Filters low-match noise data, retaining parameters and text strongly relevant to due diligence objectives |
index_refresh_interval | Daily incremental refresh (supported in FastGPT 4.9.0 and above) | Adapts to the dynamic update rhythm of satellite data, reducing resource usage from index rebuilding |
rerank_model | General semantic reranking model | Adapts to sorting retrieval results that mix structured parameters and unstructured text |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Errors to Avoid
- Vector recall results fail to match orbital parameters with units. This happens because the segmentation step does not retain the association between fields and units, leading vector encoding to lose key matching dimensions.
- Duplicate entries appear after incremental index is triggered. This happens because the incremental trigger rule for
index_refresh_intervalis not configured, and a full index rebuild covers existing historical data. - The number of query return results is far lower than the set
vector_search_top_kvalue. This happens becausesimilarity_thresholdis not adjusted based on report field characteristics, and the high threshold filters out a large number of valid but moderately matched communication link data.
How to Verify Proper Configuration
- Upload a standard satellite communications due diligence report, view the parsed segmentation list, confirm that segmentation lengths fall within the configured
chunk_sizerange. - Initiate a query containing orbital parameter keywords, check the match degree distribution of recall results, confirm that they meet the set
similarity_thresholdrequirements. - Manually trigger an incremental index, check index update records in system logs, confirm that only newly added reports are included in the index.
- Compare recall result rankings for different keywords, confirm that the reranking model sorts semantic associations as expected.
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.