What the data for this category looks like
The core users of this category include tender specialists, pre-sales consultants, and business managers. Daily output documents cover tender documents, qualification certificates, business deviation tables, and technical deviation tables. Data mainly comes from public tender announcements, official tender documents, enterprise qualification databases, and supplier quotation databases. Updates occur once per task. The document structure for each task follows the fixed format requirements of the tenderer. It includes fixed fields such as clause responses, qualification certificates, and quotation details. No uniform general field units exist. Strict matching of the tenderer’s specified format requirements is required.
What constraints these characteristics impose on the "context and token" workflow
Data for this category comes from scattered sources, and formats change dynamically based on the tenderer. Precise matching of context content required by tender rules is needed to prevent irrelevant data from occupying token resources. The total length of documents for a single task varies with tender scale. Sufficient token space must be reserved to fully generate response content. Since updates happen once per task, context does not need long-term caching. Latest data can be reloaded for each task. Content such as deviation tables and qualification certificates in documents must retain full formatting. Token truncation that causes missing content must be avoided. Otherwise, serious consequences may occur, including bid invalidation, score deduction, or margin loss.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 1500–2000 characters | Ensures single-segment tender clauses and response content are split completely, avoiding format confusion and content truncation |
maxContext | 8000–12000 tokens | Covers core context content for a single tender task, including tender documents, qualification databases, and quotation data |
| Context recall count | Top 6–8 entries | Matches core requirements in tender announcements, avoiding excessive irrelevant content occupying token resources |
| Context loading threshold | Calibrated via actual testing | Adapts to document lengths for different tender scales, ensuring loaded context meets task requirements |
Three common mistakes
- Symptom: The
failed to get gpt-3.5-turbo token encodererror appears when generating tender documents. Cause: Context token encoding parameters are not configured correctly, making it impossible to parse special format content in tender documents. - Symptom: Markdown-formatted deviation table output is truncated, with a
[hide X char]prompt displayed at the end. Cause:quoteMaxTokenis set too small, failing to cover complete clause response content, leading to forced content truncation. - Symptom: Token consumption is too high and generation time is too long for a single tender task. Cause: Too many context recall entries are set, introducing a large amount of irrelevant qualification database content and occupying unnecessary token resources.
How to confirm configurations are set correctly
- Upload a single complete tender document and enterprise qualification database data. Check the context preview module in FastGPT v4.17.0 to confirm all core tender clauses and qualification requirements are covered.
- Generate test content that includes a technical deviation table. Verify that the output is complete, with no truncation prompt information.
- Check the task's token consumption statistics to confirm that the token usage for a single task does not exceed the preset
maxContextlimit. - Trigger a complete test task to check for any token encoder-related error messages.
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.