What This Type of Data Looks Like
Power grid equipment investment research data primarily comes from equipment factory technical documents, on-site operation and maintenance inspection reports, power grid dispatch operation logs, industry technical standards, and supplier technical white papers. Updates have no fixed cycle. They trigger when new equipment connects to the grid, annual operation and maintenance inspections are completed, or relevant industry standards are revised. Most individual documents combine structured and semi-structured content. They include fields such as equipment model, rated voltage, rated capacity, insulation grade, and maintenance cycle. Common power industry units are used, such as kilovolt (kV), megavolt-ampere (MVA), and percentage (%).
Constraints for Deployment and Upgrade
Power grid equipment investment research data is dispersed across multiple sources, has no fixed update cycle, and has clear structured fields. These traits create three core constraints for deployment and upgrade. First, the system must support access to multi-format documents. Configure corresponding parsing rules to accurately extract core fields such as equipment model and rated parameters. Second, the lack of a fixed update cycle requires the upgrade process to support incremental synchronization. This avoids computing resource usage caused by full reconstruction. Third, common industry unit standards require configuring unit verification logic during deployment. This prevents unit mismatches after parsing. In addition, large equipment technical documents have lengthy content. Adjust the parsing timeout parameter during deployment to avoid parsing failures caused by mid-process interruptions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Power grid equipment technical documents have lengthy content. Standard timeout settings cannot complete full parsing |
UPLOAD_FILE_MAX_SIZE | 800 MB | Documents such as equipment factory manuals and operation logs have large file sizes. This setting adapts to large file upload requirements |
Number of recalled entries | Top 8–12 entries | Investment research requires covering multi-dimensional equipment parameters. The number of recalled entries must balance information completeness and context load |
Similarity threshold | 0.75–0.85 | Power grid equipment parameter fields have high accuracy requirements. This filters low-relevance recalled content |
Chunk length | 1200 characters | Balances the integrity of structured document paragraphs and context window limits. This avoids parameter splitting breaks |
LLM_API_RETRY_TIMES | 3 times | Addresses API fluctuations during multi-source access of power grid data. This reduces the probability of parsing interruptions |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Analyze specific issues on a case-by-case basis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: A 400 error is returned when calling the large model. FastGPT version is V4.9.7. Cause: Model access permissions are not configured correctly, or special fields from power grid equipment investment research data in request parameters trigger format verification interception.
- Symptom: After uploading a large equipment document via API, the agent backend has no response and the process freezes. Cause: The
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDSparameters are not adjusted. The large file parsing timeout does not trigger a reasonable interrupt mechanism. - Symptom: After deploying with docker-compose, workflow and knowledge base configurations disappear after a period of time, but the API can be called normally. Cause: Persistent storage volumes are not mounted. After the container restarts, configuration files and database data are cleared. Only the basic API runtime environment is retained.
How to Verify Successful Configuration
- Upload a typical power grid equipment technical document. Check if parsed fields include preset equipment model, rated parameters, and other content. Verify consistency between parsed results and original documents.
- Trigger an incremental synchronization task. Check that only newly added or modified equipment data is updated. No full reconstruction of the entire knowledge base is performed.
- Call the large model API to test investment research queries. Confirm that returned results include correct equipment parameters and units, with no abnormal errors.
- Restart the FastGPT container. Check that workflow and knowledge base configurations are retained, with no data loss.
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.