Data Characteristics of Automated Equipment Investment Research Data
Automated equipment investment research data primarily comes from official manufacturer technical manuals, industry mechanical operation and maintenance standards, real-time sensor collection logs, and third-party performance test reports. Update frequencies cover real-time (sensor operation data), quarterly (industry standard updates), and irregular (manufacturer firmware updates). Documents are divided into two categories:
- Structured parameter tables, which include fields such as device model, rated power, rotational speed, and fault codes
- Unstructured operation and maintenance procedures, which include long-form text for fault troubleshooting steps and spare parts replacement processes
Field units follow general mechanical industry standards: kilowatts (kW) for power, revolutions per minute (rpm) for rotational speed, and hours (h) for operating duration.
Constraints Imposed on Database and Operations Workflows
Mixed data sources of structured parameters and unstructured documents require support for both vector retrieval and structured conditional queries. Performance optimization must be balanced for both query types. High-frequency real-time collection data creates high concurrent write pressure, so sufficient database connection resources must be configured. The large number of device models and significant field differences require the database to support dynamic metadata management or flexible table structure adaptation. Additionally, some scenarios require avoiding open-source and foreign components, so localization verification of databases and drivers must be completed in advance.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongodb.maxPoolSize | 50–80 | Automated equipment data has high write concurrency; this range avoids connection pool exhaustion leading to request failures |
pg_vector.recall_count | 10–15 | Device parameter matching requires balancing accuracy and response speed; excessive recall increases retrieval latency |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Large equipment operation and maintenance manuals have longer length, so parsing takes more time than general knowledge base documents |
contextWindow | 8000–12000 characters | Device documents have longer length, so sufficient context must be retained to handle consecutive investment research queries |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Manufacturers provide complete device manuals with large file sizes, so this range adapts to large file import requirements |
queryThreshold | 0.75–0.85 | Device parameter matching has high accuracy requirements; this range avoids low-similarity results interfering with investment research judgments |
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. Analyze specific issues individually, and test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Returning irrelevant content when triggering follow-up questions, with lost context. Cause: Not configuring the
contextWindowparameter to adapt to long documents, or setting the context window too small to retain associated information from previous device operation and maintenance questions. - Receiving
503 Service Unavailablestatus codes during high-concurrency retrieval. Cause: Not adjusting themongodb.maxPoolSizeparameter, leading to database connection pool exhaustion and delayed request processing. - Encountering an
unsupported drivererror during database import. Cause: Not using localized database drivers instead of official open-source drivers, and not completing localization adaptation configuration for the database.
How to Verify Proper Configuration
- Import a single complete device operation and maintenance manual, and verify that no vector fields or structured fields generated in the database are missing after parsing is complete.
- Initiate multi-round consecutive retrieval, and verify that each retrieval result is associated with the device model and parameter context from previous questions.
- Simulate peak concurrent requests, and verify that database connection pool monitoring metrics do not trigger thresholds, and no request timeout errors occur.
- Replace open-source database drivers with localized adaptation versions, and verify that device parameter data can be written and queried normally.
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.