What the data for this category looks like
Data for computer equipment intelligent due diligence primarily comes from publicly available manufacturer hardware parameter documents, enterprise asset ledgers, operation and maintenance monitoring logs, and third-party hardware inspection reports. Data is divided into two categories: static and dynamic.
Static data includes device model, serial number, CPU clock speed, memory capacity, hard disk specifications, manufacturing date, and similar fields. Field naming varies across manufacturers.
Dynamic data includes current operating temperature, disk usage, remaining maintenance duration, and similar metrics. Update frequencies range from minute-level to daily-level.
A single due diligence document typically includes more than ten standardized fields. Units mostly use common hardware measurement standards such as GB, TB, GHz, and ℃.
What constraints these characteristics impose on the tool calling and plugins workflow
Differences in static data field naming require plugins to support cross-manufacturer field mapping. This prevents extraction failures caused by inconsistent naming.
The real-time nature of dynamic data requires tool calling to separate trigger logic for static parameter retrieval and dynamic monitoring data synchronization. This avoids repeated calls to high-latency interfaces.
Device serial numbers serve as unique identifier fields. Validation logic must be added during tool calling to ensure accurate data association.
Multi-dimensional hardware parameters require tool calling models to precisely match extraction rules. Additionally, vector retrieval needs for multimodal data such as device nameplates and appearance images require plugins to support access to non-standard multimodal embedding models.
How to configure settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
plugin_field_mapping | Manufacturer standard fields → local standardized fields | Computer equipment field naming varies widely across manufacturers, and unification is needed to adapt to the output format of due diligence reports |
api_request_timeout | 120-180 seconds | Hardware parameter retrieval requires traversing multi-dimensional interfaces, leading to long single-request latency |
multimodal_embedding_provider | Tongyi multimodal embedding model | Adapts to non-standard multimodal embedding services and adds retrieval dimensions for device images |
cross_origin_allow_list | Third-party browser call domain whitelist | Restricts cross-domain access scope to ensure API call security |
tool_call_model | Large model that supports tool calling | Must precisely match tool selection logic to complete extraction of multi-dimensional hardware parameters |
parse_file_timeout_seconds | 600 seconds | Full device asset ledger parsing requires traversing a large number of entries, so extended parsing time limits are needed |
The parameter values provided on this page are conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Symptom: Calling a multimodal embedding model returns
400 Bad Requestwith a prompt indicating unsupported model type. Cause: The corresponding manufacturer was not specified in themultimodal_embedding_providerconfiguration, and only OpenAI-related parameters were configured. - Symptom: No preset device parameter extraction tool is triggered during tool calling, and empty results are returned. Cause: The
tool_call_modelwas not configured as a model type that supports tool calling, or the tool calling switch was not enabled. - Symptom: A third-party browser calling the API returns a
403 Forbiddenstatus code. Cause: The corresponding domain name was not added to thecross_origin_allow_listconfiguration item.
How to Confirm Successful Configuration
- Initiate a tool calling test, input a device model and serial number, and check if standardized hardware parameter fields are returned.
- Review vector database logs to confirm that multimodal embeddings have been successfully generated and associated with the corresponding device data.
- Initiate an API call from the configured third-party domain and check if a normal JSON-formatted response is returned.
- Simulate a long-latency hardware parameter retrieval request to verify that the
api_request_timeoutconfiguration meets the request duration requirements.
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.