What this category of data looks like
This category of data mainly comes from the national pollutant discharge permit management information platform, enterprise environmental protection account books, online monitoring systems, and environmental statistics systems. Daily processed documents include pollutant discharge permit application forms, implementation reports, self-monitoring plans, account records, change applications, and permit copies. Documents contain fields such as permit limits, discharge port parameters, and regulatory requirements, and some fields use units specified in ecological environment industry standards. The data update frequency is quarterly, and each update includes the current quarter’s implementation reports, account records, and change application content.
What constraints these characteristics impose on the "vector models and indexes" link
Quarterly batch data updates require indexes to support incremental synchronization and batch reconstruction, to avoid excessive time consumption from single updates. Mixed storage of multiple types of documents requires a chunking strategy that balances structured parameters and unstructured text, to prevent key regulatory clauses from being split. Differences in data formats from multiple sources require the preprocessing link to align fields, ensuring consistency in vector embeddings. At the same time, the data involves compliance requirements for ecological environment supervision, and the accuracy of vector recall directly affects subsequent approval and supervision processes, so the false recall rate must be strictly controlled.
How to set configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of pollutant discharge permit documents, avoid splitting complete permit clauses or regulatory requirements |
| Overlap Length | 100–200 characters | Retain cross-chunk association logic, prevent permit limits and corresponding emission requirements from being truncated |
| Similarity Threshold | 0.75–0.85 | Match the rigor of regulatory scenarios, filter search results unrelated to pollutant discharge permit requirements |
| Single Search Limit | Top 8 entries | Cover multi-dimensional regulatory content of a single permit, avoid missing key information |
maxToken | 8000 | Carry the complete content length of permit copies and associated implementation reports |
defaultToken | Calibrated based on actual measurements | Adapt to interface call margins in different environments, avoid triggering token limit error messages |
Three common mistakes
- Phenomenon: After data is added to a collection, no results are returned when searching newly uploaded pollutant discharge permit documents. Cause: The functional boundaries between adding data to a collection and creating a training order are confused. Adding data to a collection only completes data storage in the database. Creating a training order triggers the index generation process of the vector model.
- Phenomenon: After uploading a scanned copy of a self-monitoring plan, no associated images are returned when searching for environmental regulatory requirements related to party building. Cause: The configuration for image vectorization and retrieval is not enabled, and only text content is vectorized.
- Phenomenon: An error is returned when calling the vector model interface, prompting that the specified model is unavailable. Cause: The access parameters for the corresponding model are not added in the system configuration, or the FastGPT v4.17.0 version is not updated to support the latest model.
How to confirm the configuration is complete
- A pollutant discharge permit copy document is uploaded, a training order is created and submitted, the task is waited to complete, then a query containing permit clauses is executed, and relevant content from the document is confirmed to be retrievable.
- The vector model configuration page is entered, the currently used embedding model is confirmed to be added, and parameters such as the interface address and key are confirmed to be configured correctly.
- The similarity threshold is adjusted to 0.7, a query related to discharge limits is entered, and the matching degree of returned results is verified to meet expectations.
- The execution log of the training order is viewed, the task status is confirmed to be successful, and no error messages such as token limit exceeded or interface timeout are present.
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.