What the data for this category looks like
Data related to precast concrete component inspection comes primarily from BIM detailed design documents, steel bar and concrete batch ledgers, mold table and curing kiln operation records, laboratory test reports, and product QR code traceability systems. 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. This category’s data updates per individual component, per batch, or per kiln. Each component has a unique on-site lifting position. Fields include component ID, strength value, embedded part coordinates, batch number, and more. Units include millimeters, megapascals, cubic meters, and more.
Constraints on vector models and indexing from these characteristics
This category’s data updates per individual component, per batch, or per kiln, with tight association logic. It requires vector indexes to support real-time incremental updates, to avoid performance loss from full reconstruction. Documents include long-text engineering drawings and structured tabular data. It requires chunking strategies to adapt to different text structures, and retain key information such as embedded part positions and strength requirements. Data association logic is complex. It requires retrieval to support cross-document association, matching steel bar cutting lists with corresponding component detailed drawings and inspection reports. Single batch data volume is large. It requires indexes to support high-concurrency queries, to meet simultaneous retrieval needs of multiple users.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapt to the text length of component detailed drawings and steel bar cutting lists, avoid including too much irrelevant content in a single chunk |
| Overlap Length | 100–150 characters | Retain cross-chunk context association, avoid key information like embedded part positions being truncated |
| Similarity Threshold | 0.75–0.85 | Match precise fields such as component ID and strength value, filter low-relevance historical records |
| Single Search Limit | Top 10 entries | Control the number of retrieval results, adapt to single batch data volume during inspection and audit |
defaultToken | 4096 token | Adapt to the input limit of the Qwen3-Embedding-8B model, avoid vector generation failures |
maxToken | 8192 token | Meet vector indexing needs for long documents, cover complete content of component detailed drawings |
This configuration guidance is based on FastGPT v4.17.0.
Three Common Configuration Mistakes
- PostgreSQL-related configuration errors occur during Milvus deployment. The root cause is directly using the standard compose file with redundant database dependencies, without adjusting deployment configurations for the lightweight retrieval needs of precast component scenarios.
- Request timeouts occur when connecting to the Qwen3-Embedding-8B model deployed via VLLM. The root cause is not setting a timeout threshold adapted to long texts, leading to uncompleted responses for vector generation requests of large-size documents.
- Incorrect knowledge base disk usage display occurs. The root cause is failing to distinguish storage paths for original files, chunked files, and embedded vectors, confusing occupancy proportions across different data types.
How to Verify Correct Configuration
- Upload a complete component detailed drawing and steel bar cutting list, check if chunked text blocks retain key information such as embedded part positions and strength requirements.
- Submit an embedded part position retrieval request, confirm returned results include corresponding historical inspection records, and similarity scores fall within the set threshold range.
- View the FastGPT storage monitoring panel, confirm storage paths for original files, chunked files, and embedded vectors are correct, and disk occupancy statistics logic matches expectations.
- Test connection to the VLLM-deployed embedding model, confirm vector generation requests return normal results without timeout errors.
The values provided here are common starting points and should be evaluated against the reader's own samples.