What data looks like for this category
Aerospace equipment investment research data comes from public annual reports of aircraft manufacturers, national defense and military industry standard documents, public test flight reports, airspace operation statistics, and similar sources. Update cycles vary:
- Annual/quarterly financial reports update on fixed schedules
- Test flight data releases align with project milestones
- Industry standard documents are revised irregularly as technology evolves
Document structure mixes structured parameter tables and unstructured technical text. Structured fields include thrust, range, service ceiling, with units such as kilonewtons, kilometers, meters, and others. Unstructured text includes complete aircraft design specifications, supply chain analysis reports, and similar content. Single-document length varies widely.
What constraints do these characteristics impose on database and operations?
Mixed structured parameters and unstructured text requires the database to support both relational queries and vector retrieval. It must balance exact matching for structured fields and semantic recall for unstructured text.
Irregular update cycles create demand for incremental synchronization and scheduled full backups. The system must support both batch updates and real-time data write loads.
Strict field unit requirements mandate storing unit metadata in the database. This avoids confusing numerical meanings of different parameters during retrieval.
Wide variation in document length requires parsing and chunking strategies to accommodate both short parameter entries and long technical reports. This prevents reduced retrieval accuracy from improper chunking.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Aerospace equipment technical documents often exceed 100 pages; long text parsing requires a longer timeout to prevent mid-parsing interruptions |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Single complete aircraft design specification documents may exceed 1 GB; large file upload support is needed to cover all investment research materials |
Chunk Length | 800–1200 characters | Aerospace equipment documents mix parameter tables and long text; this range balances contextual relevance and retrieval recall accuracy |
Recall Count | Top 10 results | Investment research scenarios require balancing multi-dimensional parameters and background documents; too many recall results increase computational load and retrieval latency |
DB_BACKUP_CRON | 0 2 * * 0 | Scheduled full data backups are required before quarterly/annual batch updates; Sunday early morning is a low-load period to reduce business impact |
VECTOR_DIMENSION | 1536 | Adapts to standard vector dimensions of most public embedding models, ensuring semantic matching performance for investment research scenarios |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Restoring a backup and accessing the question answering splitting interface triggers an
Invalid array lengtherror. The cause is missing knowledge base metadata fields in the backup, with associated vector database configuration not properly synchronized. This results in empty array parameters during splitting. - After deploying a PostgreSQL database, knowledge base indexes cannot be created. The cause is the host machine having only 8 cores and 16 GB of memory, with the
POSTGRES_SHARED_BUFFERSparameter not adjusted to a reasonable range. Insufficient memory during vector index construction causes task interruption. - Modifying the database password results in service connection failure. The cause is failing to synchronously update the
DB_PASSWORDenvironment variable for the FastGPT container. Authentication credentials between the service and database do not match.
How to Verify Proper Configuration
- Upload a typical aerospace equipment complete aircraft parameter document. Wait for parsing to complete, then check the parsing log. Confirm chunk length matches the configured setting, with no timeout errors.
- Run an incremental data synchronization task. View the database monitoring dashboard to confirm no delay in incremental data writes and normal index construction progress.
- Modify the database password, restart the FastGPT service. Attempt to log into the backend to access the knowledge base. Confirm successful authentication with no connection errors.
- Search for a specific aerospace equipment parameter. Verify that the similarity of recall results matches the configured threshold range, with no abnormally low matching accuracy results.
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.