What the data for this category looks like
The data sources for this category are tender notices, bidding documents, enterprise qualification databases, and supplier quotations. The update frequency is per project. Daily processed documents include bid documents, qualification certificates, commercial deviation tables, and technical deviation tables. Core fields include tender clause response requirements, qualification document numbers, quotation details, and format verification rules. The data structure centers on the specific requirements of a single tender, is dynamically generated for each bidding project, has no fixed universal template, and must strictly match the format and clause details of the corresponding tender.
What constraints these characteristics impose on the "context and token" workflow
Each project’s data is dynamically generated based on the tender project. The input text volume for a single project fluctuates greatly, so token usage in the context window must be strictly controlled. The strong binding relationship between tender clauses and qualification documents requires that recalled context must accurately match the corresponding clauses, to avoid irrelevant content occupying token quotas. The clause-by-clause response requirements of commercial deviation tables and technical deviation tables will generate a large number of context calls for segment-by-segment comparison. If the recall scope is not restricted, token quotas will be quickly exhausted. Additionally, the high-frequency calls of short texts for format verification rules will also increase token consumption for context splicing.
How to configure the settings
This configuration guide is based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the total text volume of a single bidding project, to avoid exceeding the model's native context window |
quoteMaxToken | 800–1200 characters | Controls token usage for a single recalled content piece, adapting to the segment length of deviation tables and qualification documents |
| Single recall count | Top 5 entries | Reduces redundant recalled content, avoiding irrelevant tender clauses occupying context space |
| Context reranking | Enabled | Prioritizes content strongly related to the current response clause, reducing invalid token usage |
| Response token limit | 4000–6000 token | Adapts to the clause-by-clause response length of bid documents, avoiding format errors caused by output truncation |
Three common configuration mistakes
- The interface prompts context overflow, and backend logs show that cumulative tokens exceed the model's supported range. The cause is failing to restrict the value of
maxContext, and directly splicing full tender document fragments into the context window. - Generated bid responses are truncated, with only partial clause content output. The cause is failing to set a reasonable response token limit, exceeding the model's native output limit.
- Too many irrelevant qualification materials are recalled, causing core tender clauses to not be prioritized. The cause is failing to enable context reranking, and setting the single recall count too high, introducing a large amount of unnecessary content.
How to verify correct configuration
- Upload a complete set of bidding documents and qualification files, check the token consumption value in the context preview panel, and confirm it does not exceed the preset
maxContextrange. - Initiate a clause response test, check the completeness of the returned content, confirm no output truncation occurs, and adjust the response token limit accordingly.
- Test recall results for different tender clauses, confirm that content directly related to the current clause is prioritized, and verify that the context reranking configuration is active.
- Check backend token consumption statistics, confirm that token consumption per round of conversation complies with the preset
quoteMaxTokenlimit, with no redundant content occupying resources.
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.