What the Data for This Category Looks Like
Data for this category comes from engineering construction project approval management systems, construction drawing review systems, enterprise qualification databases and credit platforms. The update frequency is daily. Daily documents include construction permit applications, construction drawing review qualification certificates, quality and safety supervision procedures, and completion acceptance record forms. Most documents are structured approval forms and unstructured approval text. Fields cover approval nodes, participating construction unit information, qualification levels, acceptance results. Some documents include scanned attachments with no unified fixed format.
Constraints on Vector Models and Indexing
This category’s data is scattered across multiple business systems. Daily incremental updates require indexing processes to support batch synchronization logic. Daily documents include structured forms and unstructured scanned files, so multi-format content extraction must be supported. Information such as approval nodes and participating units’ qualifications has strong correlations. Indexing must retain field-level associations to enable accurate Q&A. Daily update volume is stable, so indexing build time must be controlled to avoid disrupting normal approval business workflows. Additionally, the party building learning Q&A scenario requires linking approval business and party building-related content. Indexing must cover both business processes and party building policy requirements to prevent content fragmentation. Index accuracy must be strictly controlled to prevent issues such as unlicensed construction, construction delays, or failed completion acceptance caused by matching errors.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the paragraph length of approval documents, avoid breaking associations between approval nodes and party building content during chunking |
| Overlap Length | 100–150 characters | Retain cross-chunk approval process and party building requirement information, improve Q&A coherence |
defaultToken | 4096 | Match context length requirements of most party building learning Q&A scenarios, adapt to total text volume of conventional approval documents |
maxToken | 8192 | Support long document processing with attached text, cover long forms such as completion acceptance record forms |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Balance accuracy and recall rate of party building Q&A, avoid interference from irrelevant approval content |
| Single Search Limit | Top 5 entries | Control the number of returned results, focus on core approval and party building-related content |
Three Common Misconfigurations
- Index construction always gets stuck on the final set of tasks. This occurs when the batch synchronization logic for daily incremental updates is not adapted, causing the system to repeatedly retry unfinished incremental packages.
- Knowledge base creation fails and the process gets stuck at the indexing step. This happens when the multi-format parsing module is not configured correctly, making it impossible to process scanned attachments in construction permit applications.
- After adding a new embedding model, indexing completes but search tests throw errors. This is caused by failing to synchronously update the
defaultTokenparameter for model calls, resulting in context length exceeding the model’s supported range.
How to Verify Correct Configuration
- Upload a single completion acceptance record form, check that chunking results retain complete approval nodes and party building-related content with no content truncation.
- Trigger an incremental synchronization task, confirm that the indexing build progress proceeds normally with no long-term stagnation at any stage.
- Input party building-related approval Q&A for testing, verify that returned results include correct approval processes and participating unit qualification information.
- Call the newly added embedding model for search testing, confirm that matching results are returned normally with no error prompts.
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.