What data for this category looks like
Data for this category comes from the National Petition Information System, local petition systems, the 12345 citizen hotline platform, grassroots governance platforms, and historical petition archives. Daily processed documents include petition registration forms, acceptance notices, transfer and assignment notices, handling opinions, review and re-examination applications, resolution plans, and more. Most documents are structured text, with fields such as requester subject, matter content, and handling process nodes. Most fields focus on petitions and handling procedures. Data is updated daily. The data covers full-process petition handling documents from grassroots to municipal levels, and must match policy basis related to legal handling procedures.
What constraints these characteristics impose on model access and configuration
Multiple data sources update daily. Ensure data source synchronization logic during model access matches the update rhythm to avoid missing same-day new petition documents. Fixed-type petition documents include structured fields and process nodes. Configuration must support extracting specific fields related to petition handling, to ensure retrieved content focuses on policy basis and handling procedures. Petition data involves legal handling procedures. Model calls must ensure retrieved content strictly corresponds to compliant basis, to avoid repeated petitions or petitions submitted at a higher level than required. Historical archive data has a large volume. Configuration must balance retrieval efficiency and context length limits, to avoid exceeding the model's processing upper limit.
How to set configurations
| Configuration Item | Recommended Value Range | Basis for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Covers the total context length of petition-related documents, ensuring complete retrieval of policy basis and handling procedures |
quoteMaxToken | 2000–3000 token | Retains complete fragments of original policy basis documents, avoiding truncation of key handling procedure explanations |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring accuracy and compliance of policy responses |
maxTokens | 4000–6000 token | Matches the output upper limit of mainstream open-source large models, covering complete handling opinion explanation content |
AIPROXY_API_ENDPOINT | Fill in according to local deployment path, for example http://localhost:8080/v1 | Adapts the proxy call format of FastGPT v4.17.0, ensuring normal forwarding of model requests |
AIPROXY_API_TOKEN | Fill in according to proxy service requirements | Completes identity verification for model calls, preventing request interception |
Three common configuration errors
- After enabling the Rerank model and enabling knowledge base index configuration, online retrieval test results do not display reranking effects. The cause is failure to bind the corresponding rerank model during the model access link, or incorrect configuration of rerank model calling parameters.
- When context-related configuration parameter values are set to 3000 or above, the large model does not receive context content. The cause is that the configured total length exceeds the input upper limit of the selected large model, leading to request truncation or discarding.
- When running a local open-source large model, frequent request timeout errors occur during conversations. The cause is failure to configure reasonable request timeout parameters, or insufficient local model loading resources leading to response delays.
How to confirm configuration is complete
- Enter the model access configuration page of FastGPT v4.17.0, check that the filled content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatches local deployment parameters. - Initiate an online retrieval test, review returned results for reranked document fragments to verify the Rerank model is active.
- Adjust the range of context-related parameters, test whether context can be normally passed to the large model, confirm no truncation or discarding occurs.
- Simulate a petition policy Q&A request, check if the large model output includes policy basis compliant with handling procedures, verify the impact of parameter configuration on answer accuracy.
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 2026-09-28.