Data Profile
This category’s data comes from state-owned enterprise and public sector procurement platforms, public resource trading platforms, supplier databases, contract management systems, and audit issue databases. Updates occur daily. Commonly processed documents include procurement plans, requirement specifications, bidding documents, bid evaluation records, contracts, acceptance documents, and supplier complaint materials. Data fields cover procurement methods, budget basis, scoring rules, supplier qualification requirements, and performance acceptance information. Individual document sizes vary. File-type data links multiple documents per procurement batch.
Constraints for Model Access and Configuration
This category’s data is sourced from multiple locations and updated daily. The model access link must adapt to format specifications of different data sources, such as enterprise procurement platforms and public resource trading platforms. It must also ensure stable incremental synchronization. Documents vary in type, with multiple files linked per procurement batch. The model must support cross-document context association. Recognition accuracy for key compliance fields is critical. These fields include procurement methods, scoring rules, and supplier qualifications. Individual document and overall file sizes vary widely. The model’s context processing capability must cover inputs of all relevant lengths. Configurations must meet strict review requirements from audit and discipline inspection departments. This avoids missed or incorrect judgments of key information.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multiple documents linked to a single procurement batch, covering the total character count of core materials such as bidding documents and bid evaluation records |
maxTokens | 2000–3000 token | Ensures detailed output for compliance reviews, including basis explanations and risk reminders, to avoid output truncation |
temperature | 0.1–0.3 | Reduces output randomness, ensures consistency of compliance judgments, and adapts to the strict requirements of audit and discipline inspection departments for review results |
toolChoice | none | This scenario only requires text compliance review, no external tool calls are needed, avoiding additional logical risks from tool calls |
AIPROXY_API_ENDPOINT | Fill in the actual deployed proxy address | Adapts to unified proxy configuration for multi-source data calls, ensuring consistent call links between external models and locally deployed models |
quoteMaxToken | 1000–1500 token | Limits the length of quoted original text, ensures review content focuses on key compliance clauses, and avoids redundant quotations interfering with judgments |
Common Misconfigurations
- Empty content is returned when calling a locally deployed Ollama model, or no response occurs during testing. The cause is failure to correctly configure
AIPROXY_API_ENDPOINTto point to the Ollama local service address, or failure to update the configuration after the Ollama service port changes following a restart. - The model fails to load image files after the image input option is enabled. The cause is failure to confirm that the currently used model is a multimodal model, or failure to enable the
visionparameter in the model configuration to support image input, incorrectly configuring a plain text model for image tasks. - Call errors occur when using AIPROXY to access the model, with a prompt indicating abnormal parameter format. The cause is mixing unspecified parameter names in the model configuration, or failure to configure
AIPROXY_API_TOKENas the correct proxy verification key as required.
Verify Configuration Correctness
- Access the model testing page in FastGPT v4.17.0, enter a compliance review question for a single bidding document, and verify whether the output includes the verification results of procurement methods and budget basis, to confirm that context processing is working normally.
- Switch to linked data from different procurement batches for testing, verify whether the review results of multi-document association conform to business logic, and confirm that the
maxContextconfiguration adapts to the current data size. - Restart the FastGPT container and associated model services, run the model test again, and confirm that the configuration still works normally after restarting.
- View the model call logs, confirm that each call uses consistent parameters and configuration items, with no missing or incorrect configurations.
The values listed 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 2026-09-28.