What the data for this use case looks like
Core data for this use case comes from tender announcements, tender documents, enterprise qualification libraries, and supplier quotations, with updates exclusive to each individual project. Daily processed documents include tender documents, qualification certificates, business deviation tables, and technical deviation tables. Most document structures follow fixed formats set by the tenderer, containing structured fields such as clause response items, qualification numbers, and quotation amounts, as well as unformatted supplementary explanation content.
What constraints do these characteristics impose on the context and token workflow
The exclusive per-project update requirement means context matching must adapt quickly to single projects, and cannot rely on long-term incremental knowledge base accumulation. Fixed-format tender clauses and response content require precise association. Improper data block splitting can lead to cross-clause matching deviations. Structured qualification and quotation data contain many duplicate fields. Oversized blocks cause ineffective token consumption. A single tender document has a large amount of core content, so total context length must be controlled to avoid exceeding the token capacity limit. Format differences across tender projects require context matching to adapt flexibly to different field combinations, further increasing token management complexity.
How to set the configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the typical length of clause response paragraphs in tender documents, avoids token overflow from oversized blocks, and avoids breaking clause integrity from overly fragmented blocks |
| Overlap Length | 150–200 characters | Ensures cross-block tender clauses and corresponding response content can be fully associated, avoids matching gaps |
maxContext | 8000–12000 token | Covers the core response content and corresponding tender requirements of a single tender document, adapts to the default context capacity limit of FastGPT v4.17.0 |
quoteMaxToken | 2000–3000 token | Limits the reference length of a single matched content, avoids redundant token consumption, and ensures core response content is prioritized for recall |
| Single Search Limit | Top 10 entries | Controls the number of recalled qualification and quotation data, avoids excessive irrelevant content occupying context tokens |
Three common mistakes
- An
E11000 duplicate key error collectionerror occurs. The cause is that an independent knowledge base category is not created for tender document data from different tender projects, resulting in duplicate document identifiers across projects. - When processing supplier quotation data in Excel format, oversized single blocks lead to excessive token consumption, and the interface displays a context limit exceeded prompt. The cause is failure to adjust the chunking rule for structured table data, treating the entire table content as a single data block.
- Recalled matched content cannot accurately correspond to tender clauses, resulting in abnormal increases or decreases in the number of results. The cause is incorrect overlap length settings, preventing cross-block clauses and response content from being correctly associated.
How to confirm the configuration is correct
- Upload a single typical tender document, view the chunk preview interface, and confirm that each data block’s length falls within the preset range.
- Run a round of tender clause response testing, review context consumption statistics, and confirm that the
maxContextvalue does not exceed the system capacity limit. - Import quotation data in Excel format, review chunking results, and confirm that structured tables are correctly split into compliant small data blocks.
- Trigger a knowledge base category matching task, review the number of matching results, and confirm that the single search limit value controls the number of recalled entries.
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.