What the data for this category looks like
Data for coal chemical industry declaration materials and case review comes primarily from project establishment declaration forms, environmental impact assessment reports, safety pre-assessment reports, completion acceptance case files, and daily supervision ledgers. Updates to data are triggered in stages alongside project approval workflows, with large single-update volumes for project materials.
Documents primarily mix structured fields and unstructured text. Structured fields include project number, enterprise name, designed production capacity, environmental emission indicators, and similar items. Unstructured text consists of report bodies and approval opinions. Units follow professional chemical measurement standards, including ten thousand tons per year, mg/m³, cubic meters, and others.
What constraints these characteristics impose on the vector models and indexes link
Unstructured text in coal chemical declaration materials contains a large number of specialized chemical terms. Vector models must adapt to semantic encoding of these terms to avoid semantic loss.
Single project materials have long text lengths. Chunk length must fit the context association of long texts. Otherwise, continuous descriptions of approval processes will be disrupted.
Data updates are triggered in staged batches, not real-time high-frequency updates. Index strategies can therefore use incremental updates to reduce computing resource consumption.
Mixed storage of structured fields and unstructured text requires indexes to support joint retrieval of numerical fields and vector fields.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Single-page text of coal chemical declaration materials is approximately 300–500 characters. Chunking fits long-text context association to avoid breaking key information |
| Overlap Length | 100–150 characters | Covers cross-chunk continuous approval process descriptions to ensure semantic coherence |
maxToken | 8192 token | Adapts to long-text segmentation requirements of national standard coal chemical environmental impact assessment reports, matches the default upper limit of FastGPT v4.17.0 |
| Similarity Threshold | 0.75–0.85 | Distinguishes similar production capacity indicators and environmental parameter descriptions in declaration materials to reduce false matching probability |
| Single Search Limit | Top 10 entries | Covers the number of historical approval records that need to be associated in single case review, meets full-process review requirements |
defaultToken | Calibrated based on actual measurements | Adapts to the proportion of specialized term tokens unique to coal chemical projects to optimize vector encoding effects |
Three common errors
- Phenomenon: Parameter error returned when adding indexes in batches, status code 400. Cause: The
maxTokenparameter was not configured according to the single-file text length of coal chemical declaration materials, causing some long documents to exceed the threshold. - Phenomenon: Number of matching results recalled during review is far lower than expected. Cause: An independent index was created for each declaration material by mistake, and all associated materials of the same project were not merged into a unified index, resulting in failure to effectively recall cross-material associated information.
- Phenomenon: No synchronization log records for vector library add/delete operations. Cause: The vector library was managed directly through MongoDB, and operations were not performed through FastGPT official interfaces, resulting in inconsistency between index metadata and actual vector data.
How to confirm the configuration is properly set
- Upload a single coal chemical environmental impact assessment report, and check if chunked text fragments retain complete core information such as production capacity and environmental indicators.
- Initiate a batch indexing task, and verify whether the task log shows that all uploaded declaration materials have completed vector conversion and index storage.
- Simulate a case review query, and check whether the recalled results cover all relevant historical approval materials under the same project.
- View the FastGPT vector library monitoring panel, and confirm that the index update frequency matches the staged approval rhythm of coal chemical projects.
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.