What data looks like for this category
Data for this category comes from the national unified construction project environmental impact assessment declaration and approval system, ecological environment zoning control results, pollution discharge permit platform, and monitoring data. Update frequency is irregular.
Daily documents include environmental impact assessment reports or report forms, approval applications, public participation explanations, technical evaluation opinions, and environmental impact assessment approvals. Documents contain fields such as project category, environmental impact assessment level, pollution prevention measures, and public participation content. The length of individual documents varies widely.
Constraints on vector models and indexes
This category has multiple data sources and irregular update cycles. Vector models and indexes must support associative retrieval across heterogeneous data sources.
Document lengths vary widely, and some reports contain lengthy content. Reasonable chunking rules must be set to preserve context for technical terms.
The most time-consuming tasks include verifying project categories and pollution prevention measures. Retrieved results must accurately match project-specific data.
Errors have severe consequences. Retrieval results must maintain confidence levels to avoid irrelevant false recalls that disrupt project approval workflows.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the long paragraph structure of environmental impact assessment documents, avoids truncation of technical terms |
| Overlap Length | 100–200 characters | Preserves critical context across chunks, such as project categories and pollution prevention measures |
| Similarity Threshold | 0.75–0.85 | Matches the technical term density of environmental impact assessment documents, reduces false recall rates |
| Single Search Limit | Top 20 results | Meets the needs of associative retrieval across heterogeneous data sources, avoids redundant data volume |
defaultToken | 1000 token | Matches the core content length of a single environmental impact assessment report form |
maxToken | 8000 token | Accommodates context from retrieved results across multiple sources and documents |
Three common mistakes
- Phenomenon: Retrieval responses time out. The interface displays a response duration exceeding 60 seconds, or the console returns a timeout error. Cause: Chunk length is set too large, not adapted to the long paragraph structure of environmental impact assessment documents. This leads to excessive data volume in single-batch retrieval.
- Phenomenon: Retrieval results include large amounts of environmental impact assessment approval content unrelated to the current project. Field matching does not meet expected standards. Cause: The similarity threshold is set incorrectly, not adapted to the semantic matching accuracy of environmental impact assessment technical terms.
- Phenomenon: After Docker deployment, the index model fails to load. The console reports missing model call dependencies. Cause: Configuration requirements for FastGPT v4.17.0 were not followed. The embedding model call path was not configured correctly, or oneapi API configuration was not completed.
How to confirm configurations are correct
- Upload a single environmental impact assessment report form, run chunk processing, and verify that the chunked text length matches the set chunk length and overlap length parameters.
- Submit a retrieval request that includes project categories, and check that the similarity scores of returned results fall within the set threshold range.
- Trigger a cross-source data index update, and confirm that the index status for pollution discharge permits and ecological environment zoning control results is marked as completed.
- Run multiple retrieval requests, and check that response durations fall within the acceptable range for the target business scenario.
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.