What this category of data looks like
The data for unit-based system retrieval comes from official system documents of subordinate units of state-owned enterprises, government agencies and public institutions, including general management measures, special work rules, post operation specifications, etc. The data update rhythm is triggered irregularly along with the release or revision of unit systems, with no fixed cycle. Each document has fixed fields including issuing unit, document number, release date, scope of application, main text, etc., among which "affiliated unit" is the core distinguishing field. Most document formats are structured text converted from PDF or Word, and the length of individual documents varies widely.
What constraints these characteristics impose on model access and configuration
Decentralized data sources divided by units require that model access supports permission-isolated retrieval grouped by units, to prevent cross-unit data leaks. Documents include fixed metadata fields, so model configuration must support metadata filtering to ensure retrieval results only match systems of the target unit. The wide range of document lengths requires configuring appropriate context capacity parameters to avoid model call failures caused by long text overflow. Irregularly updated data requires the vector database to support incremental synchronization, and the model access link must adapt to lightweight update trigger logic.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Individual unit-based system documents are relatively long, and need to accommodate context from multiple related systems to avoid exceeding the model's context window |
temperature | 0.1–0.3 | System Q&A requires precise and rigorous expression, reducing randomness to avoid errors inconsistent with system requirements |
quoteMaxToken | 1500 characters | System citations need to retain complete clause paragraphs, to avoid truncation of key management requirements or implementation rules |
| Re-ranking model selection | Determined through actual testing | Text styles of unit-based systems vary significantly, requiring re-ranking models matched to the scenario to improve retrieval accuracy |
AIPROXY_API_ENDPOINT | Enterprise intranet proxy address | State-owned enterprise and public institution data must be transferred within the intranet to ensure transmission security of sensitive system documents |
AIPROXY_API_TOKEN | Enterprise-specific authentication key | Isolate model call permissions across different units to prevent unauthorized access to other units' system data |
Three common configuration mistakes
- Phenomenon: A "no available channel for model gpt-4o-mini under current default group" prompt appears during QA search testing. Cause: The API key or proxy address for this model has not been configured under the corresponding unit group, or the key's authentication permissions are insufficient.
- Phenomenon: Re-ranking function returns results that do not match the core content of the system. Cause: The re-ranking configuration item is not enabled, or the selected re-ranking model is not adapted to the formal expression style of system text.
- Phenomenon: Retrieval results include system documents from non-target units. Cause: The "affiliated unit" metadata field is not mapped correctly, or the unit-grouped retrieval filtering logic is not enabled.
How to confirm configuration is complete
- In FastGPT v4.17.0, initiate a system query targeting a specific unit, verify that the returned results only include system documents from that unit, to confirm that the group filtering configuration is active.
- View the model call logs in the FastGPT backend, confirm that there are no format errors in the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations, and no authentication failure error messages. - Adjust the
temperatureparameter value, observe changes in the rigor of the model's responses, to confirm that the parameter configuration has the expected impact on model output. - Test queries about systems with long clauses, verify that the returned cited content is not abnormally truncated, to confirm that the
quoteMaxTokensetting meets requirements.
The values provided here are common starting points, and should be evaluated 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 2026-09-28.