What data for this category looks like
Data for this category comes from four main sources. These are the national pollution discharge permit management information platform, corporate environmental protection ledgers, online monitoring systems, and environmental statistics systems. The update frequency for this data is quarterly. Commonly processed documents include pollution discharge permit application forms, implementation reports, self-monitoring plans, ledger records, change applications, and permit copies. Data fields include permit limits, discharge port codes, monitoring periods, emission concentrations, change items, and more. Some documents include structured headers and multi-line detailed content.
Constraints on vector models and indexes
Data formats from different sources are mixed. They include structured tables and unstructured text. Index configurations must support multi-format chunking. The quarterly update rhythm requires indexes to support batch incremental updates. This reduces resource consumption from full index rebuilding. Documents include multi-line details and core business fields. Chunk granularity must match business semantic units. This avoids splitting that breaks field associations. Core business terms such as permit limits and emission concentrations need precise semantic capture. This raises requirements for the model’s domain adaptability.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Pollution discharge permit documents include multi-line details and core fields. This length preserves semantic integrity while avoiding overly large single chunks |
| Overlap Length | 100–150 characters | Prevents chunk boundaries from cutting core business terms. It improves cross-chunk semantic matching accuracy |
maxToken | 8192 | Matches the context length limits of mainstream embedding models. It avoids truncation of core business content |
defaultToken | 1024 | Matches the default token threshold of embedding models. It avoids initialization errors |
| Similarity Threshold | 0.72–0.85 | Core business field matching requires high semantic similarity. It filters low-correlation matching results |
| Single Search Limit | Top 5 results | Pollution discharge permit review requires precise matching of multi-dimensional business rules. A small number of results improves review efficiency |
| Reranking Model | Enabled | Filters low-correlation matching results. It focuses on matching effects for core business fields |
Three common misconfigurations
- After building a knowledge base, search results have low matching accuracy. Single results include too many irrelevant detail lines. The cause is failure to adjust chunk length based on the detailed structure of pollution discharge permit documents. This leads to overly large chunk granularity that dilutes semantic associations.
- When adding a locally deployed embedding model, the configuration fails to take effect. It may also overwrite the existing model with the same name. The cause is failure to correctly specify the local model’s interface address and port in the FastGPT interface. Only one configuration is supported for models with the same name.
- Markdown formatting in search results is abnormal. First and second level headings go missing. The cause is failure to correctly handle structured headers and line breaks in source data. Chunking did not preserve the original document’s format markers.
How to verify configurations are correct
- Upload a single pollution discharge permit implementation report document. Trigger chunk preview. Check if chunks retain core business fields and complete semantic units.
- After configuring a local embedding model, enter business keywords on the test page. Verify that the model can normally return matching results without configuration errors.
- Adjust chunk parameters. Compare chunk results across different parameters. Confirm that chunk boundaries do not cut key business terms.
- Perform a batch index update. Verify that the quarterly update incremental synchronization function can trigger normally.
- The configurations on this page apply to FastGPT v4.17.0.
The values provided are common starting points. They 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.