What Data This Category Includes
This category of data primarily comes from enterprise existing IT system inventories, official directories of domestic hardware and software, project test environment records, and publicly available vendor materials. Daily produced documents include four types: adaptation lists, compatibility test reports, migration plans, and issue ledgers. Document fields mostly cover hardware and software models, version numbers, compatibility status, test conclusions, and risk levels. Units are mostly hardware model identifiers, version number strings, test dates, and similar items. Data updates follow individual project cycles, or sync the latest domestic hardware and software directory information quarterly.
What Constraints These Characteristics Impose on Context and Token Workflows
Adaptation-related documents often contain large volumes of hardware and software models, version details, and structured test data. This leads to high per-document token usage, which limits the share of valid content that fits in the context window. Data sources span multiple channels including system inventories and vendor materials. Cross-source recall of associated information is necessary, requiring precise matching between context recall scope and token quota. Differences in data update cycles create timeliness requirements: project-level data must prioritize the latest adaptation information for the current project, to avoid using outdated vendor materials. Issue ledger entries describe specific fault scenarios, requiring precise recall of relevant context. Insufficient token quota causes loss of critical fault details.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adaptation-related documents have high per-document token usage, need to cover complete hardware and software adaptation information and test conclusions, aligning with the context window configuration logic of FastGPT v4.17.0 |
quoteMaxToken | 1500–2000 token/entry | The core content length of a single adaptation or test report falls within this range, preventing a single quote from occupying too much context quota |
| Chunk Length | 800–1000 characters | Adaptation-related documents mostly contain structured models and text descriptions; this length balances chunk granularity and recall accuracy |
| Single Recall Limit | Top 6 entries | The number of associated entries for project-level adaptation data falls between 5 and 7; exceeding this will exceed the context window's capacity |
| Chunk Overlap Length | 100–150 characters | Prevents adaptation information across chunks from being truncated, ensuring context coherence |
Three Common Misconfigurations
- Symptom: The interface prompts that the context token limit is exceeded, and requirements and design documents cannot be generated. Cause: The
maxContextparameter was not adjusted based on the actual token usage of the adaptation document; the default quota is insufficient to carry complete adaptation lists and test report content. - Symptom: The configuration entry for
maxContextorquoteMaxTokencannot be found, or the chunk length is mistakenly set using token units. Cause: The corresponding setting was not found in the knowledge base configuration page of FastGPT v4.17.0, and the unit rules for chunk length were confused. - Symptom: A resource overrun error is triggered when uploading an adaptation document that contains embedded tables. Cause: The token proportion of embedded tables converted to text exceeds the single processing quota, and the chunk length parameter was not adjusted.
How to Verify Successful Configuration
- Upload a single typical adaptation list document, and check whether the recalled content displayed in the context preview window covers core hardware and software models and test conclusions.
- View the actual values of the configuration items, confirm that the settings for
maxContextandquoteMaxTokenmeet the document length requirements of the current project. - Initiate a requirements document generation test, and check whether the generated document contains complete migration plans and risk assessment content.
- Test the multi-document recall scenario, confirm that the number of recalled entries matches the set single recall limit.
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.