What this category's data looks like
The data for computer equipment intelligent due diligence reports mainly comes from hardware detection tool export files, enterprise asset management system interfaces, and official manufacturer warranty documents. Data updates are triggered by equipment configuration changes or warranty status updates, with no fixed cycle. Document structures primarily use structured tables, containing fields such as unique device identifier, hardware model, serial number, CPU model, memory capacity, hard disk capacity, production date, warranty expiration date, and total usage duration. Units uniformly use international standard units including GB, TB, year, and hour. Some batch reports package multiple single-device documents into compressed archives.
What constraints these characteristics impose on model access and configuration
The diversity of data sources requires support for multi-format parsing and field mapping, to prevent the model from failing to recognize key information due to input format differences. The lack of a fixed update cycle requires configuring incremental synchronization trigger rules to ensure the timeliness of due diligence data. Fields include many professional hardware terms and fixed units, so the model must support unit consistency checks to avoid output errors caused by unit confusion. Each document has fewer than 10 fields, so an overly long context window is unnecessary, and recall and parsing parameters can be optimized for this use case.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Computer equipment due diligence documents are mostly structured tables, with parsing time stably ranging from 2 to 3 minutes. 300 seconds covers most scenarios |
embedding_model | m3e-base | There are many hardware professional terms, and the semantic matching effect of m3e-base on device model and parameter text meets requirements |
max_context | 800–1200 characters | The core content length of a single device due diligence document is moderate. An overly long context will dilute the weight of key parameters |
rag_recall_top_k | Top 6 entries | The core due diligence dimensions for devices do not exceed 6. Recalling too many entries will introduce irrelevant information |
custom_model_api_key | Exclusive token bound to the One API channel | Uniformly manage API call permissions for multiple device data sources to avoid token leakage risks |
UPLOAD_FILE_MAX_SIZE | 500 MB | The compressed package size of a single batch device due diligence report usually does not exceed this threshold, which can prevent upload failures |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Symptom: After configuring the One API channel and token, the configuration is lost after service restart. Cause: The channel configuration was not saved to the persistent storage directory, and was only stored in the local temporary cache.
- Symptom: Hardware units in model-returned due diligence reports are confused, such as displaying memory capacity in GB as TB. Cause: Field unit verification configuration was not enabled, and no unit normalization processing was performed on input data.
- Symptom: Due diligence content output by the model cannot be passed to subsequent processing modules in the workflow. Cause: The "Output to context" switch was not enabled in the AI chat node, preventing structured data from being passed between modules.
How to verify successful configuration
- Upload a single computer equipment due diligence document, and check that parsed fields fully match the original data's units and format.
- Call the One API channel test interface to confirm that returned embedding vector formats meet FastGPT requirements, with no error messages.
- Trigger a workflow test to confirm that model output content can be received by subsequent processing modules and displayed in the chat window.
- Restart the service, and check that configured channels and parameters still appear in the system settings interface.
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.