What Data for This Category Looks Like
The data for this category primarily comes from engineering quality and safety supervision systems, construction site real-name platforms, supervision logs, and test reports. Daily generated documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. The data update frequency is once per week. Each individual document contains fields such as supervision subject identification, hidden danger level description, rectification requirements, and acceptance status. There is no unified fixed unit, and fields are dynamically adjusted based on document type.
What Constraints These Characteristics Impose on the Vector Models and Indexes Link
Multi-source and heterogeneous data sources require the indexing link to support batch import of documents in different formats, to avoid format conversion losses. The weekly update frequency allows use of incremental indexing strategies to reduce resource consumption from full reindexing. Diverse document types include long-text inspection records and short-text rectification notices, which require adaptation to different chunking rules to avoid semantic fragmentation. The scale of hundreds of thousands of documents requires indexing to use a reasonable sharding strategy to ensure retrieval response speed. At the same time, retrieval results are directly linked to hidden danger rectification and acceptance links, so retrieval recall accuracy must be ensured to avoid missing key information that could lead to quality or safety accidents.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the semantic integrity of long texts such as supervision inspection records and supervision logs |
| Overlap Length | 100–200 characters | Compensates for semantic gaps caused by chunking, and ensures association of cross-chunk hidden danger descriptions |
| Similarity Threshold | 0.75–0.85 | Matches the semantic matching accuracy of key documents, avoiding false recalls or missed recalls |
| Single Search Limit | Top 10 entries | Adapts to the rapid retrieval needs of internal office work, reducing redundant results |
maxToken | 4096 token | Adapts to the upper limit of vector encoding for long documents, ensuring complete semantic embedding |
| Query Optimization | Enabled | Optimizes employee query expressions and improves retrieval matching effects |
Three Common Misconfigurations
- Search response timeout, with the interface showing loading for more than 30 seconds. The cause is failure to adjust the indexing sharding strategy for hundreds of thousands of supervision documents, resulting in an overly large traversal range during vector retrieval.
- A vector model call failure error pop-up appears when calling the knowledge base, and the text-embedding-ada-002 model has been added to the configuration file. The cause is failure to correctly associate the configured vector model with the FastGPT vector database channel, or the third-party call channel has not opened the call permission for this model.
- Incremental indexing does not take effect, and newly added hidden danger rectification notices cannot be retrieved. The cause is failure to configure the incremental indexing trigger rule according to the weekly update frequency, with mistakenly set high-frequency updates leading to excessive resource occupation.
How to Confirm the Configuration Is Correct
- Import a single supervision plan document, perform vector encoding, and check whether the task log shows normal completion of encoding with no
maxTokenexceeded prompts. - Initiate a retrieval request for a specific hidden danger type, and verify that the number of returned results conforms to the single search limit configuration rule.
- Add a new test quality problem handling form, trigger incremental indexing, wait for the configured update cycle, then perform retrieval to confirm that the test document is normally recalled.
- View the index statistics of the vector database, and confirm that the number of shards adapts to the total scale of the current documents.
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.