What this category of data looks like
This category of data originates from construction quality and safety supervision systems, construction site real-name platforms, supervision logs, and test reports. Daily documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Each document includes fields such as project number, problem description, rectification requirements, and participating unit information. Data updates occur weekly. Individual document sizes range from a few pages for rectification notices to dozens of pages for completion acceptance supervision reports. Data is stored grouped by project batch and supervision node.
Constraints on vector models and indexing
Diverse data sources and varying formats require vector indexes to support unified parsing of multi-format text. Sources include structured system exports, semi-structured supervision logs, and unstructured test reports. Weekly update frequency requires incremental indexing scheduling to align with business data sync rhythms, avoiding index lag. Wide variation in individual document sizes, from short-text rectification notices to long-text completion acceptance reports, means chunking parameters must balance recall performance for both short and long texts. Additionally, business data is stored grouped by project batch, so indexed grouped retrieval logic must support fast filtering of metadata such as project numbers.
Configuration 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 text density of most documents in this category, balances recall accuracy and indexing efficiency |
| Overlap Length | 100–150 characters | Prevents truncation of core business content such as problem descriptions and rectification requirements when chunking, ensures context integrity |
| Similarity Threshold | 0.72–0.80, calibrated via actual testing | Matches semantic matching accuracy requirements for quality and safety supervision scenarios, filters low-correlation historical supervision records |
| Single Search Limit | Top 10 results | Covers the volume of weekly updated supervision data for a single project, avoids redundant results interfering with on-site inspection |
defaultToken | 4096 | Adapts to input length limits of most mainstream external APIs, ensures complete context transfer for long documents |
maxToken | 8192 | Matches vector generation requirements for long texts such as completion acceptance supervision reports, prevents content truncation |
Three common configuration errors
- Phenomenon: Exported knowledge base backups cannot be restored by project batch, only full import is supported. Cause: No index metadata grouping rules are configured. The backup logic defaults to knowledge base-wide operations, and does not adapt to data stored by project category.
- Phenomenon: After deploying an external API, arm architecture soft routers experience call timeouts or slow responses. Cause: The values of
defaultTokenandmaxTokenare not adjusted to match the input length limits of the external API. Additionally, oversized chunking parameters lead to excessive single vector computation load. - Phenomenon: After importing Feishu multi-dimensional documents and Excel files, index results lack semantic association for test value fields. Cause: Chunking parsing configuration for structured table data is not enabled, so table content is not properly vectorized.
How to confirm configurations are correct
- Run the weekly incremental index sync task, verify that the number of synced data matches the number of project documents updated that day, confirming scheduling logic is effective.
- Search for hidden danger rectification records for a specified project, verify that the project numbers of recalled results match the search criteria, confirming metadata filtering rules are effective.
- Import a single completion acceptance supervision report, check the generated vector chunk content, confirm that core fields are not truncated, confirming chunking configuration is effective.
- View external API call logs, confirm that the values of
defaultTokenandmaxTokendo not exceed interface limits, with no errors returned.
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.