What the data for this category looks like
Engineering consulting supervision documents and materials mainly originate from on-site patrol records, on-site spot supervision logs, sub-project acceptance reports, engineering change visa forms, supervision notices, and similar sources. Data comes from the internal archiving system of supervision projects and documents uploaded via on-site mobile terminals. Update rhythm follows on-site supervision activities: daily patrol documents are updated each day, while acceptance-related documents are updated synchronously with project milestones. Each single document includes fields such as unique project number, supervision professional classification, patrol/acceptance date, problem description, rectification requirements, and acceptance result. Document word count ranges from hundreds of characters for short patrol records to tens of thousands of characters for full acceptance summary reports.
What constraints these characteristics impose on vector models and indexes
Data is archived by project, with attached professional classification and unique numbering. This requires indexes to support filtering by project dimension and professional fields, to avoid retrieving irrelevant documents across projects. The wide span of document word counts means fixed chunk lengths cannot adapt to all document types, so flexible adjustment of chunking parameters is needed. Update frequency includes both real-time and batch characteristics, so indexes must support incremental updates to reduce resource consumption from repeated builds. Documents contain mixed structured fields and unstructured text, so vector models must support embedding processing for mixed data.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the wide range of supervision documents, from short patrol records to long acceptance reports, balancing context completeness and chunk granularity |
| Overlap Length | 100–150 characters | Retains cross-chunk context association, preventing key content such as rectification instructions and acceptance requirements from being split and lost |
| Similarity Threshold | 0.72–0.80 | Filters low-relevance non-target project documents, avoiding retrieval results deviating from the query topic |
| Single Search Limit | Top 8 results | Matches the typical number of associated information for a single supervision document, avoiding retrieving excessive redundant content |
defaultToken | 4096 | Adapts to context carrying for long documents, aligning with the default parameter limit of FastGPT v4.17.0 |
| Maximum Context | 12000 characters | Integrates retrieved supervision document content, ensuring compliance with the input length limits of mainstream large language models |
Three common errors
- Phenomenon: Search returns supervision documents unrelated to the current project. Cause: No filtering rule configured using the project unique number, causing the index to retrieve all archived data
- Phenomenon: The interface displays that the index is not ready, and retrieval cannot be initiated. Cause: Source data incremental update has not completed, or the embedding model call timed out before initialization finished
- Phenomenon: Doubao-embedding cannot be used as the index model. Cause: The current FastGPT version’s support for this embedding model was not verified in advance, and required model adaptation configuration was not completed
How to confirm the configuration is complete
- View the index construction log to confirm that the chunk length and overlap length configuration parameters have loaded correctly
- Submit a retrieval request targeting a specific project number, verifying that retrieved results only include supervision documents for that project
- Input a preset test query, checking that the similarity scores of returned results fall within the preset threshold range
- Upload a new supervision document, confirming that the index automatically triggers an update and the newly added document content can be retrieved
The values provided here are common starting points, and should be evaluated 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.