What the data for this category looks like
Data for this category comes from the national unified construction project EIA 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, simplified environmental impact assessment report forms, approval applications, public participation descriptions, technical assessment opinions, and EIA approval documents. Covered fields include project number, project category, EIA level, pollution prevention measures, public participation opinions, and more. Document lengths vary widely, from approval documents of a few hundred words to report texts of tens of thousands of words.
Constraints Imposed on the Vector Models and Indexes Workflow
This category has scattered data sources with irregular updates. Indexes must support incremental updates and metadata association to enable quick filtering of relevant policy basis by project dimension. Document lengths vary widely, from short approval documents to long report texts, so chunking must avoid breaking the integrity of policy clauses. Data fields include key identifiers such as project category and EIA level, so metadata must be included in indexes to enable precise matching. Errors in this link can lead to missing approval basis and cause serious consequences, so the accuracy and comprehensiveness of recall results must be ensured. Use the maxToken and defaultToken parameters to adjust context length and chunk matching accuracy. The FastGPT v4.17.0 index module can adapt to these characteristics, but configuration parameters must be adjusted specifically.
Recommended Configuration Values
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to varying lengths of EIA documents and preserves complete semantics of policy clauses |
| Overlap Length | 100–200 characters | Prevents chunking from breaking contextual connections between adjacent policy clauses |
maxToken | 4096 | Adapts to input length limits of mainstream embedding models and matches token requirements of EIA chunked text |
| Similarity Threshold | 0.75–0.85 | Filters low-relevance search results and ensures accuracy of policy basis matching |
| Single Search Limit | Top 10 results | Balances retrieval efficiency and recall coverage to meet the needs of multi-basis verification for EIA approval |
defaultToken | 2048 | Controls context length per round of question answering to avoid exceeding processing limits of large language models |
Three Common Misconfigurations
- Phenomenon: Embedding model calls become stuck, index tasks show no progress in the interface, and restarting the service after commenting out the model configuration does not resolve the issue. Cause: No timeout parameter is configured for the embedding model, causing embedding requests for long text chunks to fail to complete normally.
- Phenomenon: Question and answer pair extraction tasks remain in the indexing state for an extended period and cannot transition to the ready state. Cause: No reasonable chunk length is set, and chunked long EIA documents exceed the input limit of the embedding model, causing index task blocking.
- Phenomenon: Index entries in the dataset are automatically generated repeatedly. Cause: Deduplication check for data updates is not enabled, generating redundant index groups when repeatedly synchronizing the same approval data from the national unified construction project EIA declaration and approval system.
How to Verify Correct Configuration
- Upload a typical EIA approval document and confirm that chunked text retains complete policy clauses with no obvious truncation.
- Initiate a policy basis search for a specific construction project category and verify that the similarity of returned results falls within the preset threshold range.
- Import an existing EIA dataset and confirm that index tasks complete normally and enter the ready state.
- Review system logs to confirm that timeout parameters for embedding model calls have been correctly set, with no frequent timeout errors.
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.