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. Data is updated daily. Daily documents include construction permit application materials, construction drawing review qualification certificates, quality and safety supervision procedure documents, completion record forms, and similar content. Data fields cover approval node status, participating construction unit qualification levels, project record numbers, processing time limit nodes, and similar content. Individual document lengths vary widely, ranging from hundreds to thousands of characters.
Constraints Imposed on Vector Models and Indexes by These Characteristics
Data for this category is scattered across multiple business systems. Formats include structured forms, semi-structured approval documents, and unstructured reports. Unified embedding logic must support multi-source data. The daily update frequency means indexes must support incremental synchronization, to avoid excessive time spent on full reconstruction. Document lengths vary widely, from hundreds-of-character qualification certificates to thousands-of-character completion record forms. Reasonable chunking rules must be formulated. Core retrieval dimensions include participating unit qualifications, approval node status, credit levels, and similar content. Vector embedding must accurately match business semantics. Retrieval response speed must be controlled to meet the real-time requirements of window-based processing.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the length range of individual documents in this category, avoid overly long chunks causing embedding failures or overly short chunks damaging semantic integrity, align with the general configuration logic of FastGPT v4.17.0 |
| Overlap Length | 100–150 characters | Compensate for semantic breaks caused by chunking, ensure cross-chunk content such as approval nodes and qualification information is fully captured |
| Similarity Threshold | 0.72–0.80 | Match the semantic matching accuracy required for engineering approval business, filter low-relevance non-approval documents |
| Single Search Limit | Top 8 entries | Balance retrieval recall rate and context window usage, adapt to the fast response requirements of window-based processing |
defaultToken | 1024 | Meet the embedding requirements of core approval fields, align with the general vector embedding parameter range of the industry |
maxToken | 4096 | Adapt to the context length after multiple chunks are spliced, avoid exceeding the model's input limits |
Three Common Configuration Mistakes
- Phenomenon: Retrieval results are empty or irrelevant to business after custom index configuration is applied. Cause: The original document format exported from the approval management system is not adapted to chunking rules, or embedding prompts are not optimized for core fields such as qualifications and approval nodes.
- Phenomenon: Connection timeout or data synchronization failure occurs when calling an external vector database. Cause: Database access parameters that meet FastGPT requirements are not configured, or incremental synchronization is not enabled to adapt to the daily update frequency.
- Phenomenon: Tencent Hunyuan vector model call fails, returning parameter errors. Cause: The model call key and region parameters are not filled correctly, or the token length limits supported by the model are not matched.
How to Verify Correct Configuration
- Upload a single construction permit application document, check if the chunked fragments contain complete core fields such as the applying unit and project name.
- Trigger an incremental synchronization task, view the index update log, confirm that newly added daily approval data is correctly included in the retrieval scope.
- Input business questions such as "How to query participating unit qualifications", verify that retrieval results preferentially return documents from the corresponding qualification database.
- Test calling the configured vector model, confirm that the returned embedding vector format meets the receiving requirements of FastGPT.
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.