What Data Looks Like for This Category
Urban water service data sources fall into four main categories: municipal pipeline operation and maintenance logs, water quality test reports, drainage permit approval documents, and water supply scheduling ledgers. Daily operation and maintenance logs are long-text entries updated daily, with individual entries reaching thousands of characters. Water quality test reports are structured tables updated monthly. They include fields such as detection points, COD concentration, and pH value, with units mostly mg/L and dimensionless. Document formats include standard official documents, structured reports, and free-text records. Some documents are added to the knowledge base per permission rules.
Constraints Imposed by These Characteristics on Citation Source and Traceability
Chunking long-text operation and maintenance logs can break associated information. Improper chunking settings prevent traceability from locating the complete event chain. Structured test report fields require precise matching. Generalized retrieval leads to cited content that does not match document requirements. Frequently updated operation data must be synchronized to the knowledge base regularly. Otherwise, the traceability data source lags behind the current water service status. Water service documents have professional terminology and fixed format requirements. Cited original texts must retain professional expressions to avoid losing traceability basis after rewriting.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-paragraph information density of water service long-text logs in FastGPT v4.17.0, avoiding broken associated information caused by overly short chunks |
| Overlap Length | 150–200 characters | Retains contextual cohesion between chunks, ensuring complete events can be associated during long-text traceability |
| Single Search Limit | Top 5 entries | Matches the conventional citation requirements of water service documents, avoiding excessive redundant citations interfering with generation results |
quoteMaxToken | 800–1000 tokens | Controls the return length of a single citation, adapting to the context capacity limit of the model |
responseData | Enabled | Ensures returned citation data includes complete fields and location information of the original document |
detail | Enabled | Displays original fragments and metadata of cited content, meeting detailed traceability verification requirements |
Three Common Configuration Errors
- Phenomenon: Generated water service documents only show citations for text datasets. Structured water quality test reports are not associated. Cause: Retrieval rules for structured data are not configured. Only text content is used for recall matching.
- Phenomenon: After dynamically passing the knowledgeSearch parameter to call the workflow, the AI response does not return the corresponding citation source. Cause: The dynamic parameter is not correctly bound to the knowledge base retrieval trigger condition. The retrieval scope does not cover the specified water service dataset.
- Phenomenon: Cited content is truncated or generation is interrupted. A context limit prompt appears on the interface. Cause: The
quoteMaxTokenvalue is not set reasonably. It exceeds the model's allowed context capacity range.
How to Verify Correct Configuration
- Upload a long water service pipeline inspection document and a structured water quality test table. Initiate a document drafting request. Check whether the returned citation sources include both types of documents.
- Dynamically pass the specified water service dataset ID as the knowledgeSearch parameter. Verify that all returned citations come from this dataset.
- View the citation details in the returned results. Confirm that each citation includes the original document's field information and corresponding fragments.
- Adjust the
quoteMaxTokenparameter. Check whether the cited content length matches expectations. No unprompted truncation appears.
The values provided 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.