What the data for this category looks like
The data for power grid equipment intelligent due diligence reports mainly comes from factory inspection archives, on-site inspection ledgers, operation and maintenance logs, and power grid dispatching and monitoring data. Update cadences vary by data type: factory archives only update when the device is put into operation, inspection ledgers update per inspection cycle, and operation logs and dispatching data sync in real time. A single report typically includes unique device ID, model specification, installation location, previous test parameters, and fault handling records. Fields include insulation resistance (unit MΩ), load rate (unit %), and cumulative operating duration (unit hours). The page count parameters for single documents vary widely. It is recommended to count or measure using local samples before finalizing values.
Constraints imposed on deployment and upgrade
Multi-source real-time synchronization, large-volume documents, and multiple field features of power grid equipment due diligence data impose multiple constraints on deployment and upgrade. Multi-source real-time data requires stable persistent connections to avoid interrupting sync links during upgrades. Large single documents extend indexing time, so parsing timeout parameters and sharding strategies need adjustment. Fields with specific units require retaining unit validation rules during parsing to avoid matching deviations. High-frequency updates of inspection and operation data require reserving sufficient database read/write resources during deployment. Sync link availability must be verified after upgrades.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large single documents for power grid equipment due diligence reports have long parsing times, need to avoid timeout interrupting parsing processes |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Single due diligence report may include multiple pages of test data and inspection photos, total upload volume needs to match document scale |
maxContext | 800–1200 characters | Power grid equipment has many fields with units, segment length needs to cover complete parameter groups to avoid context breaks affecting matching accuracy |
Similarity threshold | 0.75–0.85 | High precision is required for device parameter matching, need to filter low-correlation historical inspection and operation data |
RECALL_TOP_K | Top 10 entries | Due diligence reports need to cover multi-dimensional device historical data, number of recalled entries needs to balance query speed and information completeness |
DB_CONNECTION_POOL_SIZE | 20–30 | High-frequency real-time sync of operation data requires sufficient database connection pool to avoid sync interruptions caused by exhausted connections |
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 using local samples before finalizing values.
Three common mistakes
- The symptom is long-term stagnation of knowledge base indexing progress with no clear progress update logs. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter. Large-volume power grid equipment due diligence report parsing timeouts are not handled correctly, leading to suspension of the indexing process. - The symptom is an empty
outputfield in the returned result after calling the orchestration interface via HTTP request. The cause is that the structured output switch is not enabled by default in orchestration configuration for version4.6.9, so model return content is not written to theoutputfield. - The symptom is a 502 error code returned when calling the model service after mapping an external network address for intranet deployment. The cause is incorrect container port mapping rules or failure to open outbound access permissions for the model service, leading to FastGPT being unable to connect to the model interface.
How to confirm correct configuration
- Upload and parse a single large-volume power grid equipment due diligence report, check that there are no timeout errors in the parsing log, and confirm that the
PARSE_FILE_TIMEOUT_SECONDSparameter configuration matches the document scale. - Initiate an HTTP orchestration request, check that the
outputfield in the returned result contains model-generated due diligence analysis content, and confirm that the orchestration configuration is correct. - View database connection pool monitoring metrics, confirm that the number of connections does not reach the configured upper limit, and verify that the
DB_CONNECTION_POOL_SIZEparameter adapts to real-time sync requirements. - Test model service connectivity in the intranet environment, confirm that the mapped external network address can be called normally, and verify that port mapping and network permission configurations are correct.
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.