What data looks like for this category
Aerospace equipment financial report data mainly comes from public annual reports of listed military groups, public defense procurement announcements, and industry dynamics disclosed by industry associations. The update cycle takes annual reports as the core cycle, with semi-annual reports released simultaneously. Temporary supplementary documents are generated for major contract signings and equipment delivery milestones. The document structure includes fields such as core financial indicators, equipment model delivery volumes, total contract amount, and R&D investment ratio. Units include RMB ten thousand, equipment set counts, launch mission counts, and others.
What constraints these characteristics impose on vector models and indexing
The coexistence of regular updates and temporary supplements for aerospace equipment financial reports requires indexes to support incremental synchronization, avoiding repeated calculations caused by full reindexing. Documents contain both structured financial indicators and unstructured business details, so vector models need to adapt to professional terminology in the military field to ensure semantic consistency of professional fields. Numeric fields such as contract amount and delivery sets must be vectorized in association with text fields to prevent split business semantics. Single temporary announcements have high content density, so chunking must retain business context integrity to prevent key information from being split.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Aerospace equipment financial reports contain professional terminology and long sentences. Excessively large chunks will destroy semantic integrity, while excessively small chunks will split key business-related information |
chunk_overlap | 150–200 characters | Professional terminology in aerospace financial reports has strong coherence. Insufficient overlap length will cause terminology to be split into different chunks, affecting vectorized semantic consistency |
embedding_batch_size | 4–8 | Adapts to the batch processing limit of commercial vector services, improves vectorization efficiency, and resolves the inefficiency of single-slice vectorization |
embedding_model_name | Alibaba multimodal-embedding-v1 | Supports multi-modal field vectorization, adapts to text and numeric associated content in financial reports, and meets semantic requirements for military industry professional terminology |
retrieve_top_k | Top 8–12 entries | Associated information in aerospace financial reports is widely distributed. Too few entries will miss key data such as major contracts and R&D progress |
similarity_threshold | 0.72–0.78 | Semantic similarity differentiation of professional terminology in the aerospace field is relatively high. A threshold that is too low will introduce irrelevant announcements, while a threshold that is too high will miss weakly associated but critical financial data |
vector_index_type | HNSW | The volume of vector data for aerospace financial reports continues to grow with incremental updates. The HNSW index balances retrieval speed and recall rate, and is suitable for incremental synchronization scenarios |
The parameter values provided on this page are all conventional recommendations used to determine a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Symptom: Vectorization tasks take too long. Checking the API logs of FastGPT 4.8.10 shows that only 1 text slice is passed for vectorization each time. Cause: The
embedding_batch_sizeparameter is not configured, single-slice vectorization mode is enabled by default, and the batch processing requirements of the vector service are not met. - Symptom: Calling the
get_chunk_indexinterface returns a null value, and the generated chunk index content cannot be obtained. Cause: Chunk index persistence configuration is not enabled, chunk indexes are only stored in memory cache, and are lost after service restart. - Symptom: After configuring multiple index fields for the same document block, the retrieval results contain a large amount of irrelevant non-aerospace equipment content. Cause: The index field associated with retrieval is not explicitly specified, all indexes are retrieved by default, and a domain-adapted vector model is not used, leading to semantic matching deviation.
How to Verify Correct Configuration
- Check the vector service monitoring panel, confirm the batch count of vectorization requests, and verify whether the
embedding_batch_sizeconfiguration takes effect. - Call the chunk index query interface, enter the ID of an uploaded document, and confirm that the returned chunk content matches the chunking rules of the original document.
- Upload a test document containing aerospace professional terminology, perform retrieval, and check the semantic relevance of the returned results to verify the adaptability of the vector model.
- Trigger an incremental update task, confirm that only newly generated documents are synchronized to the vector index, and no full reindexing is triggered.
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.