What this category of data entails
Documents covered in this category include procurement plans, requirement specifications, bidding documents, bid evaluation records, contracts, acceptance documents, supplier complaint materials, and more. Data sources include enterprise procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases. Data updates occur daily. Fields include procurement method, budget amount, supplier qualifications, scoring rules, performance status, acceptance results, and others. Some documents require cross-module data association across multiple systems. The length of individual documents varies widely.
Constraints on model access and configuration
This category of data originates from multiple sources and updates daily. This requires the model access link to support real-time synchronization and integration of data from multiple systems. Document types are diverse and their lengths vary widely. It is necessary to set limits on context and generated token upper limits to avoid model output truncation or timeouts. There are many cross-system associated fields. It is necessary to configure logic that supports associated parsing of multiple documents to ensure the integrity of ledger summaries. Compliance review requirements are strict. It is necessary to ensure that model-generated content matches enterprise procurement systems and audit standards. Adjust parameters to ensure output confidence.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the characteristics of numerous tender and procurement documents with wide length variance, avoiding context overflow |
maxTokens | 2000–3000 token | Ensures the integrity of compliance report generation content, meeting the output length requirements of business documents |
quoteMaxToken | 1500–2000 characters | Limits the length of quoted original text, ensuring that quoted content in ledger summaries is accurate and not redundant |
temperature | 0.3–0.5 | Reduces generation randomness, ensuring the rigor of compliance report content and matching audit review requirements |
AIPROXY_API_ENDPOINT | Fill in according to the enterprise's internal deployment environment | Adapts to enterprise intranet access restrictions, ensuring data transmission security and stability |
AIPROXY_API_TOKEN | Exclusive token generated according to permissions | Ensures identity verification for interface calls and restricts unauthorized access |
Three Common Configuration Errors
- Phenomenon: Model calls time out, and logs show a
timeoutstatus code. Cause: Failure to adjustmaxContextandmaxTokensparameters based on tender and procurement document length leads to excessive model processing load. - Phenomenon: The reranking model passes post-deployment testing, but retrieval result reranking is marked as false. Cause: Failure to configure correct reranking trigger logic, or knowledge base retrieval content does not meet the system's default reranking call conditions.
- Phenomenon: The reranking model is not called when knowledge base retrieval content is too long. Cause: Failure to adjust context-related parameters causes the system to automatically truncate long content and skip the reranking link.
How to Verify Successful Configuration
- Run a parsing test on a single bidding document, and verify whether the ledger fields output by the model match the source document fields.
- Adjust the
temperatureparameter to 0.3, generate a compliance report, and check the rigor and compliance of the output content. - Simulate a daily data update scenario to verify whether the model access link can normally pull the latest procurement data.
- Call the test interface to check whether the verification status of
AIPROXY_API_TOKENis normal, with no permission error reports.
The values provided are common starting points and should be measured 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 2026-09-28.