What data for this category looks like
Data for this category primarily originates from original equipment manufacturer (OEM) equipment maintenance manuals, bidding compliance documents, production ledgers, on-site working condition records, and after-sales repair reports. There is no fixed update cycle. New documents are imported in bulk when new projects launch. Sporadic files are updated during daily operations alongside individual repairs or working condition adjustments. Document structures include fixed fields such as equipment model, serial number, operating hours, and working condition parameters. Units cover engineering-specific measurements including hours, units, and megapascals. Document length varies widely, ranging from single-page repair records to dozens of pages of full machine operation manuals.
What constraints these characteristics impose on vector models and indexing workflows
Specialized engineering fields such as equipment model and working condition parameters have strong semantic relevance. Vector models must adapt to the encoding logic of professional engineering terms to avoid encoding deviations from general models for engineering parameters. Document length varies widely, from single-page records to dozens of pages of manuals. Index sharding strategies must adapt to text blocks of different lengths to prevent semantic breaks caused by over-splitting long texts. Document formats vary significantly across sources. Index construction requires a unified preprocessing workflow to avoid compatibility issues during vector embedding for documents of different formats. Sporadically updated documents must support incremental indexing to avoid computing resource consumption from full reconstruction. Engineering-specific units are tightly bound to numerical values. Indexes must retain the associated semantics of numerical values and units to prevent semantic loss after embedding.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the varying length of construction machinery documents and avoids splitting key engineering parameters |
| Overlap Length | 100–150 characters | Retains semantic connections between adjacent chunks and prevents semantic breaks after long text chunking |
maxToken | 4096 token | Adapts to the input limit of most general vector models and avoids embedding failures |
| Similarity Threshold | 0.72–0.80 | Filters low-relevance recall results and meets the semantic rigor requirements of engineering documents |
| Single Search Limit | Top 8 results | Balances recall accuracy and computing resource consumption |
defaultToken | 2000 token | Controls the text length of single-round embedding and adapts to sporadic repair record documents |
Three common errors
- Phenomenon: After importing maintenance manuals, retrieval results for equipment models include irrelevant working condition description text. Cause: Field associations were not preserved during chunking, leading to text from different fields being merged into the same chunk and causing semantic confusion.
- Phenomenon: Timeout errors occur during batch embedding, and logs show task interruptions. Cause: The
maxTokenparameter was not set appropriately, and long documents were not properly truncated, leading to timeout during vector embedding. - Phenomenon: The relevance of retrieval results varies widely, with some results having no direct connection to the query content. Cause: The similarity threshold was not set to meet the rigor requirements of engineering documents, and the filtering rules were not adapted to professional scenarios.
How to confirm correct configuration
- Import a typical full machine maintenance manual and manually check if chunked text retains complete semantics of key fields such as equipment model and operating hours.
- Input a query statement that includes engineering parameters and verify if the number and relevance of recall results meet expectations.
- Check the storage usage of the vector database to confirm it matches the estimated storage space.
- Test incremental import of a new repair record to confirm that the index can be updated normally without affecting existing data.
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.