Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Data for this category comes from the engineering construction project approval management system, construction drawing review system, enterprise qualification database, and public credit platform. Daily processed documents include construction permit application files, construction drawing review qualification certificates, quality and safety supervision procedures, completion acceptance record forms, and Excel-format participating construction unit qualification lists. Data is updated on a daily basis. Individual documents contain fields such as project number, participating unit name, qualification level, and approval node status. Some bulk materials include multiple sets of structured entries.
Constraints Imposed by These Characteristics on Vector Models and Indexes
Mixed multi-source data formats require indexes to support multiple file types, and prevent reliance on single-document format adaptation only. Daily update frequency requires configuring incremental index logic, to avoid performance losses from full reindexing. The coexistence of structured fields and multiple entries from bulk Excel files requires retaining contextual associations during chunking, to prevent splitting of qualification information across entries. The strong semantic binding between approval node status and project progress requires vector models to accurately capture the temporal logic of approval workflows. It also requires limiting the recall scope of indexes, to avoid interference from unrelated project data on retrieval results.
Configuration Recommendations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the paragraph length of engineering documents, and avoids splitting complete approval node logic |
| Overlap Length | 100–200 characters | Retains contextual associations between adjacent chunks, and prevents breakage of qualification information across chunks |
| Similarity Threshold | 0.75–0.85 | Filters low-match non-project-related data, and ensures accuracy of retrieval results |
| Single Search Limit | Top 10 entries | Controls the number of returned results, and avoids excessive approval node information interfering with core retrieval |
maxToken | 4096 | Adapts to input limits of mainstream vector models, and prevents semantic loss from truncated long text |
defaultToken | 2048 | Serves as the maximum token count for default chunks, and balances parsing efficiency and semantic completeness |
Three Common Configuration Errors
- Phenomenon: After uploading an Excel-format qualification list, the index status shows completed, but qualification level fields cannot be matched during retrieval. Cause: Excel structured parsing configuration is not enabled, so only plain text is extracted and semantic associations of structured fields are lost.
- Phenomenon: After uploading bulk completion acceptance record forms to the knowledge base, the index status remains stuck without updates. Cause: Incremental index trigger logic is not configured, leading to backlogged full index tasks causing blockages, or the total file size of a single batch upload exceeds 1000 MB, exceeding system processing limits.
- Phenomenon: Retrieval results include non-current project approval materials, and the number of recalled entries exceeds expectations. Cause: The similarity threshold is set too low, failing to filter low-match irrelevant data, or index tags are not added by project number for partitioned retrieval.
How to Verify Correct Configuration
- A test Excel file for construction permit applications is uploaded, the search keyword "qualification level" is used, and returned results are checked for content from corresponding fields.
- Index task logs are viewed, parsing time for bulk files is confirmed to be within a reasonable range, and no timeout errors are present.
- The similarity threshold is adjusted, changes in the number of recalled retrieval results are compared, and the effectiveness of the threshold configuration is verified.
- Vector model call logs are checked, and values for
maxTokenanddefaultTokenare confirmed to not trigger truncation errors.
The values provided are common starting points and should be evaluated against applicable sample data.
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.