What this category of data looks like
This category of data primarily comes from engineering quality and safety supervision systems, on-site construction real-name registration platforms, supervision logs, and test reports. The update frequency is weekly. Daily documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, completion acceptance supervision reports. Data fields include unique project identifiers, inspection times, rectification requirements, acceptance conclusions, and more. The length of individual documents varies widely, ranging from several hundred-word inspection records to several thousand-word completion acceptance supervision reports.
What constraints do these characteristics impose on the vector model and indexing process
Data sources are scattered and have varying formats, so multi-source data preprocessing workflows must be adapted to avoid format confusion during indexing. The weekly update frequency requires support for incremental indexing to reduce resource consumption from full reindexing. The wide range of document lengths, mixing short and long texts, requires proper chunking parameters to balance semantic completeness and vector generation accuracy. Additionally, documents contain both structured fields and free-form text content, so indexing strategies for the two types of data must be differentiated to ensure accurate matching of business keywords during retrieval.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the wide document length characteristic of this category, balancing semantic completeness and vector accuracy |
| Overlap Length | 100–150 characters | Avoids semantic breaks between chunks and covers key business information across chunks |
maxToken | 4096 token | Matches the input limit of most mainstream vector models and adapts to the splicing needs of long document chunks |
| Similarity Threshold | 0.72–0.80 range | Filters low-match irrelevant documents and accurately recalls quality and safety related content |
| Single Search Limit | Top 10 entries | Controls the scale of retrieval results to avoid redundant information interfering with ledger aggregation |
defaultToken | Calibrated based on actual measurements | Adapts to context length requirements in different scenarios and avoids overflow or insufficient information |
Three common misconfigurations
- Phenomenon: After uploading Excel-format supervision inspection records, the indexing status stays for a long time and no results are returned in searches. Cause: No field extraction rules for Excel data are configured, and core business content is not correctly extracted during vectorization.
- Phenomenon: The indexing status of hidden danger rectification notices uploaded to the knowledge base remains "indexing" for more than one hour. Cause: Incremental indexing configuration is not enabled, and full indexing processing of the large volume of weekly updated documents leads to excessive time consumption.
- Phenomenon: Duplicate rectification notice content appears in retrieval results, and cannot be displayed merged. Cause: The search result merging function is not enabled, and matching rules for duplicate content are not configured.
How to confirm that the configuration is correct
- Upload a single supervision inspection record, check the vector generation log to confirm that the chunking parameters match the configured items.
- Initiate a retrieval request containing "rectification deadline", verify that the number of returned results matches the single search limit setting.
- Import a batch of supervision log documents, check the indexing update status to confirm that the incremental update logic is operating normally.
- Call the vector model interface to verify that the returned vector data matches the configured
maxTokenparameter.
The values provided here 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.