What this category of data looks like
Data for this category primarily comes from tender announcements, tender documents, enterprise qualification databases, and supplier quotations. The update frequency aligns with each individual project. Daily processed documents include tender documents, qualification certificates, business deviation tables, and technical deviation tables. Document structures strictly match the format requirements specified by the tenderer. They include fields such as qualification verification items, clause response items, and quotation details. Content must accurately correspond to tender clauses to avoid discrepancies.
Constraints imposed on model access and configuration by these data characteristics
The characteristics of this category of data impose multiple constraints on model access and configuration. Multi-source data that updates per project requires configurations to support quick access to tender announcements, tender documents, enterprise qualification databases, and supplier quotations from different sources. Document structures that strictly match tender formats require configurations to retain format alignment processing capabilities. This ensures generated content complies with the tenderer’s typesetting and entry requirements. Requirements for content to accurately correspond to tender clauses necessitate limiting the range of deviations generated by the model. This prevents statements that do not match tender requirements. The high risk of bid rejection requires configurations to prioritize content verification while ensuring call stability.
How to configure these settings
This document is written based on FastGPT v4.17.0. The following are configuration recommendations for this scenario:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Tender documents and associated materials have large total content volume. This range covers the full tender and response content for a single project |
maxTokens | 2000–3000 token | The generated length of a single deviation table or qualification verification content must match the response requirements of tender clauses. This avoids truncation of critical information |
quoteMaxToken | 500 token | This limits the length of referenced tender clause content, ensuring response content focuses on clause matching |
temperature | 0.1–0.3 | This ensures content accuracy and compliance. It reduces randomness in generated content and prevents statements that do not meet tender requirements |
AIPROXY_API_ENDPOINT | Root address of the AI Proxy service, for example http://aiproxy:3000. Do not append /v1 or /chat/completions. Do not configure as a single model address | This is the access root address for the AI Proxy service. It ensures network connectivity for model calls and adapts to enterprise internal network policies |
AIPROXY_API_TOKEN | Administrator token consistent with the AI Proxy server configuration. It cannot be empty. Do not use application API keys, model vendor keys, or tokens issued per application | This is the identity verification credential for the AI Proxy service. It ensures the security of model call identity verification and prevents unauthorized access |
Three common configuration errors
- The AI model option dropdown in the workflow is empty, and no available models can be selected. Troubleshooting steps: Check if
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare correctly configured as App/Admin server environment variables. Incorrect or missing configuration prevents normal retrieval of the model list. Missing required variables will trigger startup verification failures. - The platform displays the prompt "Model stream response is empty, please check if model stream output is normal". The cause is that
maxTokensis set too small. This results in truncated model generated content being empty, or model calls timing out without returning valid content. - The knowledge base page displays all content, but the model prompts that the knowledge base is empty during conversation. The cause is that
maxContextis set too small. This fails to load the full migrated knowledge base content, or the knowledge base embedding configuration was not updated synchronously.
How to confirm configurations are properly set
- Verify the server environment variable configurations for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN. EnsureAIPROXY_API_ENDPOINTis the root address of the AI Proxy service. EnsureAIPROXY_API_TOKENmatches the administrator token configured on the AI Proxy server and is not empty. - Initiate a matching test for a single tender clause. Confirm the model returns response content in the correct format, with no empty returns or errors.
- Check the AI model selection interface in the workflow. Confirm the list of connected models loads normally.
- Import test qualification certificate and deviation table documents. Trigger the compliance verification workflow. Check if generated content complies with tender format requirements.
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 2026-10-19.