What data looks like for this category
Data sources for this category include procurement orders, drawings and specifications, supplier quality certificates, inspection equipment, historical quality data, and supplier portals. Updates occur with each incoming batch, monthly or quarterly supplier evaluations, and irregular audits. Daily documents cover inspection specifications, sampling plans, incoming material reports, supplier audit reports, exception contact sheets, 8D rectification reports, and concession acceptance approval forms. These documents include fields such as incoming material batch number, sampling quantity, non-conforming items, and supplier code. Units include pieces, batches, days, and similar units.
What constraints these characteristics impose on model access and configuration
Data sources are scattered and mostly external supplier-related documents. Multi-source data access paths must be configured to cover quality documents and inspection records from different suppliers. Update frequency changes frequently with incoming batches. Incremental update trigger logic must be adapted to avoid resource consumption from full reconstruction. Documents contain specific business fields such as batch numbers and sampling quantities. Field extraction and structured processing configurations must be matched to ensure Q&A can accurately associate inspection standards for corresponding batches. Quality risks are linked to production links. The rigor of model output must be strictly controlled to avoid misjudgment or missed judgment caused by improper configuration.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Documents related to incoming material inspection are mostly batch-level detailed content, and need to cover complete process specification and technical standard context |
temperature | 0.1–0.3 | This scenario requires rigorous standard reference and judgment to avoid deviations in output |
maxTokens | 2000–3000 token | Complete output of inspection judgment basis, rectification suggestions and related document references is required |
quoteMaxToken | Top 6 retrieved documents | Multiple sampling plans, historical inspection data and supplier audit records need to be covered |
AIPROXY_API_ENDPOINT | Fill in according to the deployment environment | Adapt to local or cloud model call paths, and comply with the proxy configuration specifications of FastGPT v4.17.0 |
AIPROXY_API_TOKEN | Fill in according to the actually applied key | Ensure valid identity verification for model calls |
Three common configuration errors
- Symptom: Conversation response timeout, logs show
ETIMEDOUTstatus code. Cause: The scenario's high-frequency batch-level data retrieval is not adapted, andmaxContextor timeout-related configurations are not adjusted. - Symptom: Conversation results are inconsistent with model returns in the workspace. Cause:
temperatureandmaxTokensparameters are not uniformly configured, leading to differences in generation logic across application instances. - Symptom: OneApi call error, prompting unauthorized or path error. Cause:
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTare not filled correctly, and configuration paths for local deployment and third-party proxies are confused.
How to confirm configurations are properly set
- Upload an incoming material inspection specification document, trigger a retrieval test, check the quantity of retrieved documents and content matching degree, and adjust related configurations to meet business requirements.
- Launch a Q&A related to sampling plans, check whether the output contains accurate field references, and adjust generation-related configuration parameters to optimize result rigor.
- Simulate a batch incoming material update scenario, verify whether incremental update of the vector database is normally triggered, and adapt to changes in data update frequency.
- Call the configured proxy interface, verify that the returned results are consistent with the FastGPT workspace, and check the correctness of API configuration items.
The values provided are common starting points and should be measured against the reader's own samples.