What the data for this category looks like
Data for this category comes from the national unified construction project environmental impact assessment (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 or report forms, approval applications, public participation descriptions, technical assessment opinions, and EIA approval documents. Document structure covers core fields such as project category, EIA level, pollution prevention measures, public participation content, and approval conclusion. Service targets include EIA departments of ecological environment authorities, administrative examination and approval bureaus, construction units, and EIA institutions.
What constraints these characteristics impose on the vector model and index link
Documents mostly have long text structures, and individual reports or report forms have large content volume, requiring chunking configurations adapted to professional chapter logic to avoid splitting professionally related content. Data sources are diverse and have format differences, so unified preprocessing is required before index construction. Update frequency is irregular, so incremental index support is needed to meet the dynamic needs of real-time approval. Professional terms account for a high proportion, so vector models must accurately match semantic features in the ecological environment field. The approval link has strict requirements for information accuracy, so index recall must filter irrelevant documents while ensuring that key information is not omitted.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the chapter structure of EIA reports, avoid splitting professionally logically related content into different chunks |
| Overlap Length | 100–200 characters | Retain cross-chunk context association, prevent key information such as project category and EIA level from being split |
| Similarity Threshold | 0.72–0.80, calibrated via actual testing | Match professional semantic similarity of government documents, filter irrelevant historical approval documents |
| Single Recall Count | Top 5 results | Align with the limited information needs of EIA approval verification, reduce invalid data loading |
maxToken | 8192 | Adapt to the input length limit of mainstream open-source vector models, compatible with the embedding requirements of the m3e series models |
defaultToken | 1024 | Set the basic embedding token quota for a single segment of text, balance processing accuracy and speed |
Three Common Misconfigurations
- Knowledge base upload status remains stuck in indexing: The cause is that the chunk length is set too large, and the token count of a single chunk of text exceeds the model's embedding limit, causing the indexing process to block.
- Embedding rate limit error during vectorization: The cause is failure to limit the number of indexing threads, or failure to adjust the
defaultTokenquota, resulting in API call frequency exceeding service limits. - Knowledge base text chunks are missing: The cause is that a reasonable overlap length is not set, causing paragraphs containing key professional terms to be completely split and omitted.
How to Verify Proper Configuration
- Upload a single environmental impact assessment report, check the chunk preview interface, confirm that each chunk length falls within the preset range and core fields are not split.
- Initiate a test query for project category or pollution prevention measures, check the similarity distribution of recall results, confirm that the threshold setting meets expectations.
- Check the indexing logs of FastGPT v4.17.0, confirm that there are no embedding rate limit or chunk abnormality errors.
- Upload a new approval application document, test the incremental indexing function, confirm that the new document can be properly indexed and recalled during queries.
The values listed are common starting points and should be evaluated against samples relevant to the specific deployment.
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.