What the Data for This Category Looks Like
Data sources for semiconductor intelligent due diligence include publicly disclosed annual/quarterly financial reports of enterprises, monthly production capacity and supply chain data released by industry associations, patent application records publicly available from patent offices, and order announcements from downstream terminal manufacturers. Update cadences vary: financial reports are updated quarterly, industry production capacity and supply chain data monthly, and patent data in real time.
Document structures typically include fields such as wafer process nodes, production capacity scale, revenue breakdowns, supply chain partner proportions, patent classifications and quantities. Some documents include content such as wafer yield and unit production cost. Corresponding units for fields include wafers per month (in ten thousands), 100 million yuan, inches, and other standard units.
What Constraints Do These Characteristics Impose During Model Access and Configuration
The multi-source nature of semiconductor due diligence data requires configuration to support parsing and adaptation of multiple document formats, to avoid loss of professional fields due to format differences. Data with different update frequencies must correspond to different recall and update cycles, to ensure that the due diligence report uses the latest production capacity and supply chain data.
The presence of professional terminology and specific units requires configuring a relatively high semantic recall threshold, to avoid recalling general data from unrelated industries. The total length after splicing long documents is large, so the context window configuration must be adjusted to accommodate complete associated data, ensuring that the model can accurately integrate information from multiple documents.
How to Set the Configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 tokens | Semiconductor due diligence reports typically include multiple spliced long documents, requiring sufficient context to correlate supply chain and production capacity data |
chunkSize | 1000–1500 characters | Semiconductor data includes professional terminology and long fields; splitting must avoid damaging the integrity of terminology |
recallTopK | Top 8–12 results | Semiconductor due diligence requires precise matching of supply chain manufacturers and production capacity data; excessive recall will introduce irrelevant information |
similarityThreshold | 0.75–0.85 | Semiconductor professional terminology requires a relatively high semantic similarity threshold to avoid recalling data from unrelated industries |
apiRequestTimeout | 60 seconds | Parsing some semiconductor industry data documents takes a relatively long time; this avoids timeout interruptions |
fileParseMaxSize | 500 MB | Semiconductor due diligence reports often include packaged files with multiple financial reports and production capacity data, requiring support for large single-file uploads |
The parameter values provided on this page are common starting points for determining configurations. Actual values are affected by material format, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The model returns the error
[] is too short - 'messages'during testing. Cause: The initial message template for conversation context is not configured correctly, or thesystemPromptis empty, resulting in an insufficient length of the message array. - Phenomenon: A 422 status code is returned when initiating a workflow request, but the model interface test succeeds individually. Cause: The model parameters configured in the workflow do not match the actual calling API parameters, or the input field format does not meet the model requirements.
- Phenomenon: After configuring a third-party model API, normal calling fails, and the interface displays model loading failure. Cause: The permission scope of the API key is not filled in correctly, or the corresponding request address and request header for the model are not configured.
How to Confirm the Configuration is Successful
- Upload a single semiconductor due diligence document, verify that the parsed text fully retains professional fields such as wafer process and production capacity, along with their corresponding units.
- Initiate a model test call, input queries related to semiconductor supply chains and production capacity, and check that the semantic matching degree of the returned results meets the preset threshold.
- View the workflow log records, confirm that there are no error codes such as 422 or timeouts, and that the returned results include the required breakdown data.
- Test uploading multiple spliced documents, verify that the context configuration can accommodate the total text length after splicing.
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.