What the data for this category looks like
Data for this category comes primarily from ticketing systems, customer interview records, CRM customer management systems, and product roadmap documents. Daily output documents include requirement pool lists, requirement evaluation forms, customer feedback records, and version planning descriptions. Data updates follow a weekly or per-product version cycle. Each data entry includes basic fields: requirement number, associated customer identifier, priority tag, submission time, and evaluation status. No unified mandatory units apply. Settings adjust based on the business attributes of individual requirements.
What constraints these characteristics impose on context and token handling
Data sources for this category are scattered, and formats vary. Requirement records exported from different systems have field misalignment and inconsistent formatting. Additional format alignment is required when splicing contexts. This uses extra token quota. Data updates occur at a high frequency. The context range for a single summary must match the weekly or version cycle. An overly long context will exceed token limits, truncating key requirement information. The requirement pool also contains many duplicate similar feedback. Redundant content further squeezes effective token space. Deduplication processing must be completed in advance to optimize context utilization efficiency.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 1000–1500 token | Matches the total effective information of weekly requirement pool summaries, and avoids exceeding the model context limit of FastGPT v4.17.0 |
quoteMaxToken | 200–300 token per entry | Adapts to the typical length of single requirement records, and prevents single references from occupying excessive token quota |
| Context deduplication switch | Enabled | Filters duplicate submitted similar requirements, reducing token occupation by redundant content |
| Context scope | Last 7 days or current version cycle | Matches the data update frequency of this category, ensuring timeliness of summarized information |
| Single context extraction limit | Top 20 valid records | Controls the number of requirements loaded in a single session, avoiding token overflow that affects summary results |
Three Common Configuration Errors
- Symptom: Returns an error stating
AIPROXY_API_ENDPOINTis not configured orAIPROXY_API_TOKENis invalid. API calls fail. Cause:AIPROXY_API_ENDPOINT(the root address of the AI Proxy service; do not use single-model addresses such as VLLM/Ollama/Xinference) orAIPROXY_API_TOKEN(AI Proxy administrator token; do not use application API keys, model vendor keys, or other tokens) is not configured. Or the format ofAIPROXY_API_ENDPOINTis incorrect. OrAIPROXY_API_TOKENdoes not match the AI Proxy server configuration or is empty. - Symptom: Requirement records with field misalignment and format confusion appear after context splicing. Cause: Format alignment is not performed for requirement documents from multiple sources, leading to invalid content occupying token quota.
- Symptom: A large number of duplicate similar requirement entries appear in summary reports. Cause: Context deduplication configuration is not enabled. Redundant requirements use too many tokens, leading to truncation of key information.
How to Confirm Proper Configuration
- Review environment variable configurations on the App/Admin server. Ensure that the values of
AIPROXY_API_ENDPOINT(the root address of the AI Proxy service; do not use single-model addresses such as VLLM/Ollama/Xinference) andAIPROXY_API_TOKEN(AI Proxy administrator token; do not use application API keys, model vendor keys, or other tokens) match the actual deployed proxy service. - Generate a single weekly requirement summary. Review the context preview panel to verify the proportion of valid requirement records meets expectations.
- Trigger a single requirement pool update sync. Check for log prompts related to duplicate key errors.
- Adjust the
maxContextparameter. Verify that the context does not truncate and lose key requirement information.
The values provided are common starting points and should be measured against the user'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.