What this category of data looks like
Urban investment platform policy and regulatory basis data mainly comes from publicly available internal operation management systems, urban investment supervision policies released by local state-owned assets supervision and administration commissions and local governments, and special bond declaration and use documents. Data update rhythm follows the release schedule of corresponding policy documents, with no fixed cycle. Single documents are mostly structured long texts, containing fields such as document number, issuing authority, effective date, scope of application, and specific clauses. Some attachments include quantitative indicator descriptions. Field formats are inconsistent across different sources, and some files have nested chapter structures.
What constraints do these characteristics impose during model integration and configuration
The nested structure and non-uniform fields of urban investment platform policy documents require reserving sufficient long-text processing space during model integration, to avoid losing key basis such as document numbers and effective dates when splitting text. The non-fixed cycle of data updates requires configuration items to support flexible knowledge base synchronization trigger rules. Policy texts have strong professionalism and clear local attributes, so model generation parameters need to be adjusted to ensure answer rigor, while adapting to access requirements for locally deployed professional large language models. This avoids answer errors caused by general models’ misunderstanding of urban investment terminology.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text nested structure of urban investment policy documents to avoid context overflow |
maxTokens | 2000–3000 token | Balances the level of detail and generation speed of policy answers |
quoteMaxToken | 1500 token | Retains complete references to key fields such as document numbers and effective dates in policy documents |
temperature | 0.1–0.3 | Urban investment policy Q&A requires rigor, reduce randomness to ensure accurate expression |
AIPROXY_API_ENDPOINT | HTTP address of locally deployed model, e.g. http://192.168.1.100:8000/v1 | Adapts to locally deployed professional large models such as QWQ-32B, meets privacy requirements for local policy data |
toolChoice | auto | Automatically trigger knowledge base recall tools to match the retrieval needs of policy Q&A |
Three common configuration errors
- Phenomenon: After uploading urban investment policy attachments in the chat window, the model cannot generate summaries or answers. Cause:
AIPROXY_API_ENDPOINTis not correctly configured to point to the local model, ormaxContextis set too small, causing the attachment text to fail to load completely. - Phenomenon: The model only returns 1 knowledge base matching text block, and cannot comprehensively output multiple sections of policy basis. Cause: The model's context recall range is not adjusted, causing retrieval results to be truncated to a single segment of content.
- Phenomenon: The knowledge base cannot be called normally after being integrated with enterprise WeChat. Cause: Access parameters are not adjusted according to the platform's latest enterprise WeChat integration configuration process, and outdated invalid configurations are used.
How to verify a successful configuration
- Upload a single urban investment policy document, test whether the model can correctly extract the document number and effective date, and confirm that the extraction result matches the original text.
- Enter questions related to local urban investment supervision, check whether the number of quoted text blocks returned by the model meets the expected configuration.
- Call the locally deployed QWQ-32B model, verify that there are no timeouts or connection errors during the answer generation process, and confirm that the
AIPROXY_API_ENDPOINTconfiguration is valid. - Manually trigger the knowledge base synchronization, verify that the model can retrieve the latest released policy documents after data update.
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.