What This Category of Data Looks Like
Data for urban water service policy and regulatory basis queries primarily comes from official red-headed documents issued by national urban construction, water resources departments, and local water affairs bureaus across provinces and cities. This includes local water supply and drainage regulations, water conservation management measures, pipe network operation standards, and similar content. Documents are updated on a non-fixed schedule, synced when new policies are released or existing clauses are revised. Most single documents are structured text with document numbers, issuing bodies, effective dates, and clause numbers. Some include attachments, quotas, charging standards, and other content. Fields include document number, applicable region, executing unit, and more.
What Constraints These Characteristics Impose on the Vector Model and Indexing Link
Structured clause numbers and applicable region fields require indexes to retain semantic context. Do not split content across multiple clauses, as this will damage the complete semantics of policy clauses. Documents with non-fixed update cycles require support for incremental index triggering. Sync new policy files on demand with this mechanism. Standard content with units needs unified unit representation during vector encoding. This prevents recall errors caused by unit ambiguity. Chunk long documents while balancing semantic integrity and vector window limits. Avoid single chunks that are too long to exceed model capacity limits.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Set the Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 1000–1500 characters | Adapts to the clause length of urban water service policy documents, avoids splitting a single complete policy clause |
| Overlap Length | 150–200 characters | Compensates for semantic gaps after chunking, ensures coherent policy context across chunks |
defaultToken | 8192 | Adapts to the default context window of mainstream open-source vector models, meets the token capacity requirements for most policy chunks |
maxToken | 16384 | Handles complete policy documents including attachments, avoids truncating key execution standard content |
| Similarity Threshold | 0.72–0.78, calibrated via actual testing | Distinguishes semantic relevance of policy clauses, filters irrelevant cross-regional water service policies |
| Single Search Limit | Top 8 results | Controls the volume of recall results, adapts to the precise traceability needs of policy Q&A |
Three Common Errors
- Phenomenon: Recall results include cross-provincial and municipal inapplicable water service policies. Cause: No regional dimension index filtering rules are configured, only global semantic recall is used for relevant content.
- Phenomenon: Vector index construction fails, with background error prompts indicating token limit exceeded. Cause: The
maxTokenparameter is not adjusted, and the default value is used directly to process complete policy documents including attachments. - Phenomenon: Recall results from different index models cannot be merged and sorted normally. Cause: Index models trained on different datasets are mixed, vector dimensions are not unified, violating vector space consistency requirements.
How to Confirm the Configuration Is Correct
- Upload a single urban water service policy document, check the chunk preview interface, confirm that a single chunk does not span multiple formal clauses.
- Enter a query statement with regional restrictions, verify that the release region of the recall results matches the query requirements.
- Check the background logs of index construction, confirm there are no truncation prompts for token limit exceeded.
- Test calling the index interface, confirm that the number of returned recall results matches the single search limit configuration.
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.