What This Category of Data Looks Like
Data for precast concrete components originates from BIM detailed design files, steel and concrete batch ledgers, mold table and curing kiln operation logs, laboratory test records, and product QR code traceability systems. Daily documents include component detailed drawings, steel cutting lists, mold inspection reports, pouring and vibration records, curing cycle records, and demolding and factory inspection sheets. Data is updated per single component, per batch of materials, and per curing batch in a kiln. Fields include component number, steel specification and model, concrete strength grade, curing duration, demolding strength value, and more. Units include millimeters, megapascals, hours, and other standard units.
Constraints Imposed on Vector Models and Indexes
Precast concrete component data is scattered and updated per single component, per batch of materials, and per curing batch in a kiln. This requires indexes to support incremental updates and batch import of multi-source heterogeneous data. Component data includes mixed content such as engineering drawing text, steel specifications, and strength parameters. When a vector model splits text, it must avoid damaging key information such as steel positions and component dimensions. This prevents split fragments from losing business-related association information. Additionally, bid quotation preparation requires rapid matching of historical data for the same project and same type of components. Index recall must be limited to the component list of the corresponding bid project to avoid irrelevant data interfering with recall results. The system must also adapt to frequently updated ledger data to maintain index real-time performance.
Configuration Settings
This configuration is based on the interface and parameter logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Documents such as precast concrete component detailed drawings and cutting lists have long lengths. Chunking must retain key information such as steel positions and component numbers to avoid breaking business logic during splitting |
maxToken | Set to the selected embedding model/service input limit | This is the configured embedding model's maximum token count, not chunk size or LLM context. Check the selected model or provider documentation. See the v4.17.2 model-field reference |
| Similarity Threshold | 0.72–0.85, calibrated via actual testing | Matches the similarity between component specification parameters and historical quotation data. Avoids overly low values causing irrelevant recall or overly high values missing valid matches |
| Single Search Limit | Top 10 results | Controls the number of recall results during bid quotation preparation, preventing excessive data from interfering with manual verification |
defaultToken | Set from representative sample documents | defaultToken is the default token count for direct text chunking; tune it on representative source documents. See the v4.17.2 model-field reference. It is not a vector dimension, embedding-model input cap, or retrieval/LLM context. |
| LLM context budget | Set within the selected model's documented context window | Budget for the prompt, conversation history, retrieved text, and answer together; do not treat maxToken as this limit |
Three Common Misconfigurations
- Symptom: Index status shows not ready, and retrieval requests cannot be initiated. Cause: Incremental index trigger rules are not configured according to the update rhythm of single components and per-batch materials. Full index loading times out due to excessive data volume.
- Symptom: Retrieval results include a large number of irrelevant component data across projects, failing to match the current bid project requirements. Cause: The index scope is not limited to the component list of the current bid project. Full table data is directly imported as the index source, and no project-level filtering configuration is applied.
- Symptom: Embedding requests fail or the dataset index reports a model incompatibility. Check the selected embedding model, provider response, and dataset index details. If the error names a vector-dimension mismatch, compare the model output with the index requirements;
defaultTokenis a text-splitting token count, not a vector dimension.
How to Verify Proper Configuration
- Check the status indicator in the index management interface, confirm the status is ready, and verify that the number of documents included in the index matches the number of imported component lists.
- Enter the steel specification and strength parameters of a single component as a retrieval term, check whether the recall results include historical component data of the same type, and adjust the similarity threshold to a range that meets business requirements.
- Test incremental import of log data for a single batch of curing kilns, confirm that the index updates automatically without data loss or format errors.
- Verify that a representative document is split as expected and that the selected embedding model returns a successful result. If checking vector dimensions, compare the model output with the dataset index requirements, not
defaultToken.
The values provided are common starting points and should be measured against the reader's own samples.