Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category primarily 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 processed documents include environmental impact assessment reports or report forms, approval applications, public participation explanations, technical assessment opinions, and EIA approvals. The content size of single documents varies widely. Core fields include project name, approval status, construction unit, project category, EIA level, and there is no unified fixed unit, with changes based on project type and document attributes.
Constraints imposed by these characteristics on the vector models and indexing link
Data sources for this category are scattered, and updates have no fixed cycle. This requires indexing to support incremental synchronization from multiple data sources, to avoid resource consumption caused by repeated full indexing. The wide variation in single document size requires adapting text chunking logic for different lengths, to avoid cutting critical EIA information in long documents or creating redundant chunks in short documents. Core retrieval needs focus on professional terms such as project category and EIA level, so vector models must have semantic understanding capabilities in the EIA domain to ensure business relevance of retrieval results. Additionally, errors in approval data can lead to issues such as delayed project startup, so high accuracy and stability for index recall must be ensured.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk length | 800–1200 characters | Adapt to the typical paragraph length of EIA reports and report forms, avoid cutting critical EIA information |
| Overlap length | 100–150 characters | Ensure semantic coherence between chunks, avoid splitting professional EIA terms across chunks |
defaultToken | 2000 token | Match the core semantic scope of short documents such as single approval applications and public participation explanations |
maxToken | 8000 token | Adapt to the upper limit of vector encoding for long documents like environmental impact assessment reports |
| Similarity threshold | 0.75–0.85 | Filter low-match irrelevant EIA documents, ensure retrieval accuracy |
| Single search limit | Top 10 entries | Control retrieval result scale, adapt to batch processing needs for ledger summary |
Three common mistakes
- Phenomenon: A parameter error prompt is returned after submitting the vector model configuration, and the interface displays "Configuration item format is invalid". Cause: The value range of
defaultTokenandmaxTokenwas not configured according to the field requirements of EIA documents, and the values exceed the parameter range allowed by the system. - Phenomenon: Indexing takes more than 30 minutes, and the task status stays at "Indexing" for a long time. Cause: Adaptation parameters for chunk length and overlap length were not adjusted, and default chunking rules were used for ultra-long EIA reports, leading to an excessive number of chunks and increased repeated computation.
- Phenomenon: Vector retrieval cannot be triggered after docker-compose deployment, and the ledger summary function prompts "Index service not connected". Cause: Port mapping for the vector index service was not opened in the docker-compose configuration file, and the index service was not added to the application network.
How to confirm the configuration is correct
- Upload a single environmental impact assessment report form, run the vector indexing task, and check if the task log shows that the number of chunks matches the document size.
- Enter search terms containing project category and EIA level, check if the matching order of retrieval results conforms to business logic, and adjust the similarity threshold to the required range.
- In the docker-compose deployment environment, check if the service list includes the vector index service, and confirm that the port mapping configuration has taken effect.
- Run a batch ledger summary task, check if the generated report contains correct associated information of EIA projects, and verify the accuracy of index recall.
The values provided here are common starting points, and should be evaluated against the samples used in 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.