Data Profile for This Category
Aerospace equipment investment research data is sourced from public financial reports of military aircraft manufacturers, aero-engine system disclosure documents, official announcements from the National Defense Science, Technology and Industry Administration, civil aviation airworthiness notices, industry research reports, and type development progress announcements. Updates follow quarterly and annual financial report cycles, plus irregular pushes for temporary order or first flight announcements. Document formats include structured tables of production capacity, cost, and flight test data, long-text descriptions of development progress, and detailed financial indicators. Common fields include unit aircraft cost, annual production capacity, flight test duration, and total order quantity. Common units are ten thousand yuan per aircraft, aircraft per year, hours, and individual aircraft.
Constraints for Vector Models and Indexing Pipelines
Aerospace equipment investment research data has dense specialized terminology, mixed structured numerical data and unstructured long text, which creates multiple constraints for vector models and indexing. First, the field contains many specialized military terms, such as turbofan engine models and airworthiness certification standards. Vector models must adapt to domain-specific semantics to avoid term ambiguity in recall results. Second, sudden temporary announcements generate incremental data. Indexes must support low-latency incremental writing to avoid excessive time spent on full index reconstruction. Third, structured production capacity and cost tables are mixed with long-text development documents. The pipeline must support adaptive segmentation logic to avoid losing contextual associations of specialized terms after splitting, while also supporting filtered retrieval for numerical fields.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | bge-m3 or a dense vector model fine-tuned for the aerospace equipment domain | Adapts to military specialized terminology and improves the accuracy of semantic recall |
chunk_size | 800–1200 characters | Meets the segmentation needs of long-text development documents and avoids losing contextual associations of aircraft types after splitting |
recall_top_k | Top 10–15 results | Balances retrieval recall rate and computational overhead, and matches the retrieval needs of multi-dimensional investment research information |
similarity_threshold | 0.72–0.80 | Filters low-relevance general text and retains valid recall results that match specialized terminology |
index_type | HNSW index | Supports low-latency incremental writing and fast recall, and adapts to sudden update requirements for temporary announcements |
vector_db_provider | zilliz or locally deployed milvus | Supports migration storage from pgvector and adapts to batch incremental data management |
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. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: Vector document ingestion fails in the interface, with error code
400 Bad Request. Cause: External vector database connection parameters were not properly configured, so FastGPT version 4.9.6 cannot establish communication with the external indexing service. - Symptom: Low matching rate for specialized terms in retrieval results, with a large number of irrelevant general text returned. Cause: A vector model fine-tuned for the aerospace equipment domain was not used. General models cannot accurately identify military specialized terms, leading to deviations in semantic recall.
- Symptom: Reranking model results have no valid sorting, or return empty values. Cause: The deployed private reranking model was not adapted to military domain corpus, and was not trained to adapt to aerospace equipment specialized terms, leading to failure of the sorting logic.
How to Verify Successful Configuration
- Run a vector ingestion test for a single aerospace equipment specialized document.
Check the ingestion status code returned in the interface. Confirm no errors are present, then verify that vector data for the corresponding document exists in the vector database.
- Enter a retrieval term related to the aerospace equipment domain, such as "development progress of a certain turbofan engine".
Check the number of recall results from the retrieval, and adjust configuration items to meet expected quantities.
- Compare retrieval results from a general vector model and a domain fine-tuned model.
Confirm whether the matching rate of specialized terms meets requirements, to verify the effectiveness of the vector model configuration.
- Run an incremental data upload test, upload a temporary type order announcement.
Check whether the index is automatically updated, and confirm that the incremental writing configuration takes effect.
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.