Data Overview for This Category
Data for this category comes from housing and urban-rural development construction quality and safety supervision systems, on-site construction real-name management platforms, supervision logs, and third-party inspection reports. Five types of documents are generated daily: supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Data update frequency is weekly. Each single document includes fields such as supervision subject, project number, hidden danger description, rectification deadline, and acceptance conclusion. There is no unified fixed unit, and field content and format adjust dynamically based on document type.
Constraints for Vector Models and Indexes
The characteristics of this data impose constraints on the vector model and indexing workflow: The weekly batch update rhythm requires indexes to support incremental updates, avoiding long downtime caused by full reindexing. Mixed ingestion of multi-type documents requires differentiation of field weights. For example, core conclusion fields in completion acceptance supervision reports need higher matching priority. Text generated on construction sites is often tied to on-site real-world scenes, so chunking logic for unstructured text must be adapted to preserve semantic integrity. Hidden danger rectification documents have strong timeliness, so the retention period of index data must be limited to ensure the accuracy of inquiry responses. Field formats from multiple sources are inconsistent, so standardization processing must be completed before indexing to prevent mismatches of professional terms. Additionally, maxToken must be used to limit the total context length per round, avoiding exceeding the model's input limit.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Preserve semantic integrity for long texts such as supervision logs and inspection reports, avoid semantic breaks across chunks |
| Overlap Length | 100–150 characters | Ensure semantic continuity between chunks, prevent rectification requirements in hidden danger rectification notices from being split and lost |
| Similarity Threshold | 0.75–0.85 | Match the density of professional terms in engineering documents, filter low-relevance inquiry matching results |
| Single Search Limit | Top 8 results | Cover associated data from multiple sources such as supervision inspection records and acceptance reports, ensure comprehensive response information |
maxToken | 4000 characters | Limit context length per inquiry round, adapt to token consumption of construction engineering-related texts |
defaultToken | 200 characters | Set minimum valid length for chunks, filter meaningless short text fragments |
Three Common Misconfiguration Issues
- Phenomenon: A large number of duplicate hidden danger rectification notice entries appear in search results, making it impossible to accurately match the current inquiry project. Cause: The configuration logic for unified indexes and sub-indexes is not clearly defined, and documents from different construction projects are mistakenly split into multiple independent indexes.
- Phenomenon: After directly adding vector data via MongoDB, the retrieval service cannot recall newly added documents. Cause: Direct modification of original stored data does not trigger incremental updates of vector indexes, and the retrieval node still uses old index caches.
- Phenomenon: Parameter incompatibility errors occur when calling domestic vector models, and valid vectors cannot be generated. Cause: The model's input format and output dimension adaptation configuration is not adjusted, and default parameters for OpenAI models are used instead.
How to Verify Proper Configuration
- Import a single supervision inspection record, use the platform preview function to check if the chunked text length and overlap range meet configuration requirements, adjust parameters to match the characteristics of business documents.
- Submit an inquiry related to hidden danger rectification, verify the number of recalled results and similarity matching degree, adjust the threshold and search limit based on business needs.
- Attempt to directly modify vector database data via a third-party tool, verify whether the retrieval service can synchronously update recalled results, confirm that the index update mechanism is operating normally.
- Switch to a domestic vector model to run a test, check if the interface call is successful, confirm that the model configuration parameters are adapted to the current business scenario.
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.