What Data for This Category Looks Like
Precast concrete component data originates from factories producing PC components, pipe piles, bridge components, and municipal products. Daily documents include component detailed drawings, steel bar cutting lists, mold inspection records, pouring and vibration records, curing records, and demolding and factory inspection reports. Data sources cover BIM or detailed design files, steel bar and concrete batch ledgers, mold table and curing kiln operation data, laboratory test records, and two-dimensional code traceability systems. Update frequency is triggered per individual component, per material batch, and per curing batch in the kiln. Documents contain structured fields and unstructured engineering drawings. Some fields must match production batches and on-site hoisting positions.
What Constraints Do These Characteristics Impose on the Vector Model and Indexing Link?
Multi-source heterogeneous mixed-format data requires indexes to support CAD drawing text extraction and structured field mapping. This avoids parsing failures. The high-frequency update rhythm per individual component, per batch, and per kiln requires configuring incremental index triggering mechanisms. This reduces resource consumption from full reindexing. The business logic that links each component to exactly one on-site hoisting position requires indexes to retain the component's unique identifier field. This ensures retrieval results can directly associate with production batches and installation locations. Retrieval accuracy requirements are strict for core process parameters such as embedded positions and strength grades. Targeted adjustments to chunking strategies are needed to prevent information fragmentation.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text length of component detailed drawings and steel bar cutting lists. Prevents single chunks from holding too many scattered embedded position details |
| Overlap Length | 100–150 characters | Maintains contextual connection between adjacent chunks. Prevents breakage of steel bar sleeve position information during splitting |
| Similarity Threshold | 0.75–0.85 | Matches precise retrieval needs for component parameters. Filters low-relevance records such as curing logs or mold inspection data |
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. |
maxToken | Set to the selected embedding model/service maximum supported input tokens | maxToken is the input-token maximum supported by the selected embedding model/service; verify it in that provider configuration. See the v4.17.2 model-field reference. It is not retrieval/LLM context or a FastGPT-wide fixed cap. |
| Reranking | Enable the private reranking model supported by FastGPT v4.17.0 | Improves retrieval sorting accuracy for core parameters including embedded positions and strength grades |
Three Common Misconfigurations
- Irrelevant ready-mixed concrete delivery records appear in retrieval results. This occurs when documents are not filtered by the precast concrete component category, and no data dimension isolation is applied to the index.
- The number of returned search results falls short of the preset value. This occurs when the similarity threshold is set too high, or the chunk length is too large, leading to an insufficient number of valid chunks.
- The system reports a context-limit error. Inspect the prompt, retrieved text, conversation history, and selected LLM context limit. Embedding
maxTokenapplies to individual embedding inputs, not the combined retrieval and LLM context.
How to Verify Correct Configuration
- Retrieve questions about embedded positions. Confirm returned results include the detailed drawing text and steel bar cutting list content for the corresponding component.
- Trigger an incremental index task. Confirm the system only updates component data for the day's production batches, and does not initiate a full index process.
- Test connection to the private reranking model. Confirm retrieval results prioritize sorting core process parameters.
- View index parsing logs. Confirm mixed-format drawings and ledger documents are correctly split and indexed. Check embedding input limits and LLM context limits separately.
The values listed above are common starting points. They should be evaluated against samples from the target deployment.