What this category of data looks like
Data sources for this category include 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 processed documents include environmental impact assessment reports or report forms, approval applications, public participation explanations, technical assessment opinions, and EIA approval documents. These documents contain content such as project category, pollution factor analysis, pollution prevention measures, and public participation status. They include both structured form documents and long unstructured report documents with a common length of 5000–20000 characters.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Scattered data sources with irregular updates require retrieval to cover heterogeneous documents exported from multiple systems, and support cross-dataset recall. Daily documents include structured forms and long unstructured reports, so retrieval needs to balance precise matching of short fields and semantic recall of long texts. EIA approval documents are directly linked to project construction and compliance, so retrieval results must have high confidence. The recall scope must be limited to approval materials related to the current project and ecological zoning control requirements, to avoid irrelevant results interfering with the formation of technical assessments and approval opinions. At the same time, field differences across different documents require retrieval configurations to adapt to diverse text structures, ensuring accurate recall of core fields such as project category and pollution prevention measures.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.72–0.85 | EIA documents require precise matching of core project attributes. A threshold that is too low will introduce irrelevant results, while a threshold that is too high may miss valid reference materials. Calibrate based on actual measurements combined with business scenarios |
limit | Top 6–10 results | Too many single-pass recall results will increase context load, while too few will fail to cover comprehensive approval reference materials. Adapt to the multi-dimensional technical review requirements of EIA |
searchMode | vector+keyword | Balance semantic recall and structured field matching, adapting to the mixed structure of EIA documents that include both long text reports and form fields |
usingReRank | Enabled | Perform secondary sorting on recall results to filter low-correlation content and improve the presentation accuracy of core approval-related information |
datasetSearchUsingExtensionQuery | Enabled | Automatically expand query terms to cover common synonymous expressions in EIA approval, improving recall coverage |
collectionIds | Specify by business dataset | Limit the retrieval scope to EIA approval-specific datasets, avoiding irrelevant documents from other business modules and improving retrieval accuracy |
Three common misconfigurations
- Phenomenon: Some PDF documents have no valid text fragments after being imported into the knowledge base, or cannot be retrieved. Cause: No OCR recognition process is configured for scanned PDF documents, or the documents have encryption, non-standard format, or other issues, resulting in failed text extraction.
- Phenomenon: Knowledge base retrieval results are returned all at once without segmented streaming presentation. Cause: No trigger rule for output streaming response is configured, resulting in packaged return of results and affecting interactive experience.
- Phenomenon: Errors related to the embedding model are prompted during retrieval, or imported documents cannot be recalled. Cause: The embedding model interface configuration has permission or address errors, or the specified embedding model is not correctly bound in the system, resulting in failed vector generation.
How to confirm the configuration is complete
- Import a typical environmental impact assessment report form document, and check whether the knowledge base list shows that the document has successfully generated vectors.
- Initiate a query targeting the project category, and verify whether the number of recall results matches the preset
limitparameter value. - Adjust the
similarityparameter value to verify whether the relevance of retrieval results changes in line with business judgment standards as the parameter changes. - After enabling the
usingReRankfunction, compare the recall result sorting before and after enabling to confirm that the re-ranking process is running normally.
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.