What the data for this category looks like
Aerospace equipment investment research data sources include public military industry research reports, annual and half-year annual reports from original equipment manufacturers, public announcements of aerospace equipment model development milestones, and public aviation material quotation data. Update cycles cover three categories: annual financial reports, semi-industry trends, and sudden model milestone events. Document structures include structured parameter tables, semi-structured technical description documents, and unstructured research report text. Fields cover parameters such as total aircraft empty weight, maximum range, specific thrust, and development progress. Units include kilograms, kilometers, kilonewtons, hundreds of millions of yuan, and others.
Constraints on Context and Token Processing
Aerospace equipment investment research data includes multi-source structured parameters, long-cycle technical documents, and sudden model milestone announcements. Units from different sources, such as kilometers and nautical miles, easily cause context ambiguity. Long documents consume large amounts of tokens. Sudden milestones require rapid recall, but context stitching may exceed window limits. Multi-dimensional associations of structured fields also increase token consumption during context integration. Targeted adjustment of configuration parameters is needed.
Recommended Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single aerospace equipment research report or model document can reach thousands of characters. This range covers core parameters and sudden milestone information, and avoids context truncation |
chunkSize | 1000–1500 characters | Aerospace equipment documents contain many long technical sentences. Too-fine segmentation breaks parameter association logic, while too-coarse segmentation consumes excessive tokens |
recallTopK | Top 6–8 results | Multi-source structured parameters, model milestones, and research report viewpoints should be recalled simultaneously to avoid missing key associated information |
similarityThreshold | 0.72–0.78 | Subtle parameter differences between aerospace equipment series should be distinguished to avoid incorrectly recalling content from non-target models |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large aviation material quotation tables or batch research report collections take longer to parse, preventing mid-task timeout interruptions |
rerankTopK | Top 3–4 results | Core associated content is retained after reranking recall results, reducing unnecessary token consumption |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Each scenario requires individual analysis. It is recommended to test on samples specific to the target deployment before finalizing settings.
Three Common Misconfigurations
- Symptom: After startup, an error indicates
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis invalid, and model calls fail. Cause: Environment variables are not configured correctly, the endpoint address lacks a protocol prefix, or the token has insufficient permissions. - Symptom: An
E11000 duplicate key error collection: wisegpt.error occurs when adding a user as therootuser. Cause: A user with the same name already exists in the database, or a unique index conflict has occurred, and user uniqueness was not validated beforehand. - Symptom: RAG knowledge base classification matching returns an abnormal number of results, or token consumption exceeds the limit. Cause: The
chunkSizeparameter was not adjusted. The default segment length is too large, causing individual segments to consume too many tokens, or context stitching exceeds window limits.
How to Verify Correct Configuration Setup
- Upload a single aerospace equipment research report PDF, view the parsed segment results, and confirm segment lengths match expectations, with no overly long or short fragments.
- Initiate an investment research query, view the context reference sources returned by the model, and confirm recall counts and similarity meet configuration requirements.
- Upload multiple large documents in batch, check that all parsing tasks complete without timeout errors.
- Call the API interface for testing, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations can successfully initiate requests.
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.