What This Category of Data Looks Like
Data for engineering consulting supervision declaration materials and file review comes from various review submission materials and acceptance files generated throughout the engineering supervision process. These include material inspection forms, sub-project acceptance records, supervision logs, rectification notices, project completion files, and more. Review submission materials are updated in real time during the project execution phase. Stock files are archived after completion acceptance. Daily supplementary recording and revision operations are also common. Document structure includes structured metadata and unstructured main text. Metadata fields include project number, supervision unit, construction unit, review date, rectification status, and others. Main text contains specific review opinions, material lists, rectification requirements, and similar content. Units involved include square meters, construction duration days, project cost amounts, and more.
Constraints Imposed on Vector Models and Indexes by These Characteristics
Engineering consulting supervision documents have large differences in length, numerous metadata fields, and complex update scenarios. Short inspection forms of a few lines and dozens of pages of supervision monthly reports exist side by side. This means index chunking cannot use fixed lengths, and must adapt to the information density of different documents. Metadata includes project-specific identifiers and review statuses. Retrieval must support precise filtering based on metadata, so indexes must associate metadata fields. Real-time updated review submission materials and batch archived historical files require separate processing. Index configuration must balance the efficiency of incremental updates and full updates, and avoid invalid full scans.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length differences of engineering consulting supervision documents, covers valid information in most review submission materials and supervision logs |
| Overlap Length | 100–150 characters | Prevents chunk boundaries from truncating key review opinions or material lists, ensures contextual coherence |
| Similarity Threshold | 0.72–0.80 | Matches the semantic matching accuracy of professional terms in the engineering field, balances recall rate and accuracy |
| Single Search Limit | Top 8 entries | Meets business requirements for associating multiple relevant materials during file review |
maxToken | 4000 token | Adapts to the default context limit of FastGPT v4.17.0, covers the total volume of most merged retrieval documents |
| Embedding Model | Ali-emb3 | Accurately identifies professional terms in the engineering field, aligns with the text characteristics of declaration materials and files |
Three Common Configuration Mistakes
- Multiple instances of the same supervision inspection form appear in retrieval results. The cause is failure to configure a unique identifier for each document chunk, leading to repeated recall of the same material across multiple index branches.
- Index tasks take longer than 600 seconds to complete. The cause is failure to distinguish between real-time updated review submission materials and archived historical files, using full index processing for daily updated data.
- Retrieval results fail to match professional review opinions. The cause is failure to use the Ali-emb3 embedding model. General models have insufficient vector expression accuracy for engineering terms.
How to Verify Proper Configuration
- View the FastGPT index preview function, check that the chunked document fragment length and overlap range match the preset configuration.
- Launch a retrieval test for a single document, verify that the same document is recalled only once, confirming that the document chunk unique identifier configuration is active.
- Adjust the test range of the similarity threshold, compare retrieval results across different thresholds, confirm that matching results meet business review screening requirements.
- Check the embedding model configuration item, confirm that Ali-emb3 is selected, aligning with the professional text characteristics of engineering consulting supervision work.
The values provided here are common starting points, and should be evaluated against the user'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.