What Data for This Category Looks Like
The data sources for aerospace equipment intelligent due diligence reports include public development documents from military scientific research institutes, equipment finalization test reports, parameter announcements released by industry associations, and public disclosure information from supply chain supporting enterprises. Update rhythm varies by equipment phase. Documents for newly developed equipment are updated quarterly, while documents for finalized and fielded equipment are updated less frequently. Document structure includes fields such as equipment model, development unit, performance parameters, supporting systems, fielding progress, and some reports include text descriptions of technical charts. Field units include professional measurement standards such as kilometers, kilograms, flight hours, etc. The length of a single report is usually dozens of pages.
What Constraints These Characteristics Impose on Vector Models and Indexing
The mixed content structure of aerospace equipment due diligence reports requires vector models to adapt to both text description and structured parameter encoding needs, to avoid losing semantic information of professional parameters with a single encoding method. Phase-based differences in update rhythm require indexes to support flexible switching between incremental refresh and full reconstruction, to adapt to the rapid iteration needs of newly developed equipment. Long single reports and dense professional terminology require chunking rules to balance paragraph integrity and semantic coherence, to avoid context breaks caused by excessive splitting, or insufficient segmentation that affects retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Aerospace equipment due diligence reports contain extensive continuous technical descriptions. Excessively long segments will lose context association, while excessively short segments will destroy the integrity of parameter groups |
chunk_overlap | 100–150 characters | Avoid splitting the front and back associations of core performance parameters, and adapt to the semantic coherence of long technical paragraphs |
embedding_model | Alibaba multimodal-embedding-v1 | Supports unified encoding of text descriptions and structured parameter tables, adapting to the mixed content structure of reports in this category |
retrieval_top_k | Top 6–8 results | Core parameters of aerospace equipment due diligence reports have strong relevance. Too many retrieved results will introduce irrelevant content, while too few will miss key information |
similarity_threshold | 0.72–0.78 | Reports in this category have a high density of professional terminology, so low-correlation retrieval results need to be filtered |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | A single aerospace equipment due diligence report has a long length, and parsing takes a long time, so the timeout period needs to be extended |
index_refresh_interval | 1 hour | Due diligence documents for newly developed equipment are updated frequently, so the latest fielding or test data needs to be synchronized in a timely manner |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Empty fields are returned when calling the API to obtain chunked index content. The cause is that the "Chunk Index Persistence" configuration item was not enabled in FastGPT 4.8.10, resulting in chunked data not being persistently stored.
- Retrieval results have large deviations after configuring the Alibaba
multimodal-embedding-v1vector model. The cause is that the chunking rules were not adjusted for this model, resulting in an imbalance in the encoding weight of technical parameters and descriptive text. - Indexing of aerospace equipment due diligence reports takes too long to complete. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSparameter was not modified, and the default timeout period is insufficient to parse long report content.
How to Confirm Successful Configuration
- Check the system settings page to confirm that the
embedding_modelparameter is configured as Alibabamultimodal-embedding-v1, and the associated API key has been verified. - Upload a test aerospace equipment due diligence report, and check that the number of parsed chunks matches the configuration rules of
chunk_sizeandchunk_overlap, with no excessive splitting or missing core paragraphs. - Perform a retrieval test, enter typical aerospace equipment professional terminology, and verify that the number of retrieved results matches the
retrieval_top_kconfiguration, and the relevance of the results meets preset requirements. - Check the index management page to confirm that the incremental index refresh interval matches the
index_refresh_intervalsetting, and newly uploaded test documents can complete index updates within the corresponding time frame.
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.