What the data for this category looks like
Data sources for construction machinery declaration materials and file review include project approval declaration documents, equipment factory certificates, phase-by-phase maintenance files, technical parameter sheets, and approval circulation forms. Data is submitted in project batches, with limited updates per single project cycle. Document types include mixed PDF text, Excel structured parameter sheets, and on-site equipment photos. Core fields include equipment model, serial number, rated load, operating hours, approval nodes, and more. Special engineering units such as tons, hours, and MPa are used alongside these fields.
What constraints these characteristics impose on the vector model and indexing link
Mixed document types require the indexing system to support both unstructured text chunking and structured table parsing, to avoid missing critical data in parameter sheets. Single file documents can reach thousands of characters, so chunking strategies must adapt to the input length limits of vector models, preventing critical approval nodes from being truncated. Fields with special units need to retain metadata to ensure precise matching of specific content such as equipment model and rated load during retrieval. Phased data submission rhythm means high-frequency real-time indexing is unnecessary; refresh frequency can be adjusted according to project cycles.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-section text length of construction machinery declaration materials, avoiding exceeding the vector model input limit |
| Overlap Length | 100–150 characters | Preserves contextual association between chunks, preventing critical approval nodes from being truncated |
maxToken | 30000 token | Adapts to the total content volume of a single file, ensuring the large language model can access complete review basis |
defaultToken | 10000 token | Sets a basic recall context quota, balancing retrieval accuracy and resource consumption |
| Similarity Threshold | 0.75–0.85 | Meets the precise retrieval requirements of file review, filtering low-relevance redundant materials |
| Single Search Limit | Top 6 results | Controls resource consumption of retrieval calls, avoiding traffic overload |
| Vector Model Normalization | Enabled | Adapts to non-normalized vector models, complying with the new configuration requirements of FastGPT v4.17.0 |
Three Common Misconfigurations
- The first common issue is that retrieval results display matched files, but the large language model returns no relevant content. This occurs because vector model normalization is not enabled, causing deviation in vector similarity calculation, so matched documents are not properly included in the context.
- The second common issue is that single retrieval calls take too long, or resource throttling errors are triggered. This occurs because reasonable
maxTokenand single search limit values are not set, leading to excessive document content being passed in a single call, exceeding system resource limits. - The third common issue is that chunked text loses critical unit information, leading to incorrect retrieval matching. This occurs because field metadata is not retained during chunking, and parameters with units are directly truncated, preventing precise matching.
How to Verify Proper Configuration
- Access the vector model configuration interface, confirm that the normalization option is enabled, complying with the new configuration requirements of FastGPT v4.17.0.
- Upload a single typical construction machinery declaration file, review the chunk preview result, confirm that text is not excessively truncated and key field information is retained.
- Initiate a retrieval request, verify that the total context token count of the returned results matches the set range of
maxToken. - Simulate a batch retrieval scenario, observe system response status, confirm that no timeout or throttling errors occur.
The configuration values provided are common starting points, and should be evaluated against the specific samples applicable to the target deployment.
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.