What data for this category looks like
Data for this category typically originates from object storage, data warehouses, annotation platforms, and customer acceptance feedback. Update frequency follows per-batch or per-version cycles. Daily produced documents include dataset descriptions, version records, delivery lists, and acceptance reports. Common data fields include version numbers, sample lists, delivery package checksum values, acceptance pass status, and additional relevant fields. Each data entry is linked to a unique version identifier for full delivery process tracing.
What constraints these characteristics impose on model access and configuration
Since data is updated per batch or version and linked to a unique version identifier, the model access link must bind the corresponding version’s dataset metadata to prevent cross-version mixing. Format differences across multiple data sources require configuration items to adapt to validation rules of different data sources, ensuring automated delivery integrity checks. Frequent updates to version records require configurations to support dynamic association with the latest version’s delivery list, while also retaining configuration snapshots for historical versions to ensure training results are reproducible. The unstructured nature of customer acceptance feedback requires configurations to support multi-format acceptance result parsing, ensuring accuracy in compliance review workflows.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total text length of a single batch dataset delivery package, prevents context overflow |
maxTokens | 2000–3000 token | Controls output length per round of compliance review, matches clause parsing requirements for acceptance reports |
quoteMaxToken | 600–800 token | Limits the length of referenced dataset fragments, ensures precise location of compliance clauses |
temperature | 0.1–0.3 | Reduces output randomness, ensures consistency of compliance review results |
AIPROXY_API_ENDPOINT | Fill in according to deployment environment | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions. This is not a single-model address for VLLM/Ollama/Xinference |
toolChoice | auto | Automatically selects tools required for compliance review, adapts to parsing of multi-format delivery documents |
Three common configuration errors
- Phenomenon: Service startup triggers a model not bound error, or model parameter confusion occurs during cross-version calls. Cause: Server environment variables
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or configuration values do not meet requirements, leading to service startup validation failure or inability to call the AI Proxy service normally. - Phenomenon: Compliance review results only return simplified conclusions, without linking to specific clause content in delivery documents. Cause:
quoteMaxTokenis set too small, leaving insufficient length for extracting original clause fragments. - Phenomenon: Review tasks for batch delivery packages time out and interrupt. Cause:
maxContextvalue exceeds resource limits of the current deployment environment, leading to context loading failure.
How to confirm configurations are properly set
- Check environment variable configurations in the App/Admin server.
AIPROXY_API_ENDPOINTis the root address of the AI Proxy service.AIPROXY_API_TOKENis a non-empty administrator token matching the AI Proxy server configuration. - Initiate a test call for a single delivery list, confirm that the returned result includes reference fragments of original clauses.
- Switch dataset metadata for different versions, verify that configuration parameters used during model calls match the configuration for the corresponding version.
- Adjust the
temperaturevalue to 0.3, initiate two identical review requests, confirm that output result differences fall within an acceptable range.
The values provided are common starting points, and should be evaluated against the reader’s own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.