What this category's data looks like
Precast concrete component-related data comes from BIM or detailed design files, reinforcement bar and concrete batch ledgers, mold table and curing kiln operation logs, laboratory test reports, and QR code traceability systems. Daily documents include component detailed drawings, reinforcement bar cutting lists, mold inspection records, pouring and vibration logs, curing records, demolding and factory inspection reports. Data updates are triggered per individual component, each batch of materials, or each curing kiln run, with no fixed schedule. Documents contain fields such as precise geometric dimensions, material parameters, and process node records. Some fields are linked to on-site lifting position information.
What constraints do these characteristics impose on vector models and indexing?
The multi-type structured/semi-structured documents, non-fixed update cadence, and fields linked to on-site lifting positions for this category impose multiple constraints on the vector model and indexing process. Diverse document formats require tailored chunking rules to avoid splitting that breaks component geometric parameter or process association logic. Non-fixed update frequencies require the indexing system to support incremental updates triggered per individual component, batch, or kiln run, instead of full reindexing. Fields linked to on-site lifting positions must retain their association during vector encoding, to avoid losing position matching capability during retrieval. Data errors can cause severe production losses, so the accuracy of index recall must match process validation requirements.
How to set the configuration
Configuration settings are based on the vector model and indexing module of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the text density of documents such as component detailed drawings and cutting lists, avoids splitting that breaks process association logic |
| Overlap Length | 50–100 characters | Retains contextual association between adjacent chunks, prevents key process nodes from being split into different chunks |
| Similarity Threshold | 0.75–0.85 | Matches the validation accuracy requirements of precast concrete component process data, filters low-relevance redundant records |
| Single Search Limit | Top 10 results | Balances retrieval efficiency and recall coverage, meets multi-dimensional validation requirements for process documents and exception reports |
| 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. |
| defaultToken | 2048 | Balances the information density of single vectors and retrieval speed, adapts to batch data vectorization processing |
Three common misconfigurations
- Phenomenon: Vectorization task fails, with logs showing rate limit exceeded. Cause: Vectorization concurrency configuration was not adjusted based on the update cadence of individual components and batches, leading to too many requests sent in a short period that exceed service limits.
- Phenomenon: Knowledge base retrieval response time exceeds expectations. Cause: Chunking and indexing configurations were not adjusted for the category's multiple document types, leading to excessive vector data volume recalled per batch, which increases retrieval load.
- Phenomenon: Indexing task remains in "indexing" status indefinitely and cannot complete. Cause: Chunk length was not set reasonably, leading to excessively long single chunk data, which causes vector encoding and index construction time to exceed thresholds.
How to confirm the configuration is properly set
- Upload a typical single document combination, such as a component detailed drawing paired with a reinforcement bar cutting list, to verify that chunking results do not break the association logic of key processes or geometric parameters.
- Simulate batch data vectorization, observe the concurrency status and rate of the task queue, and confirm that no limit exceeded errors are triggered.
- Initiate a process document validation retrieval, check the number and relevance of recall results, and adjust the threshold to meet business validation requirements.
- View the dataset index entries, confirm that incremental updates only add the latest batch of component data, with no duplicate index entries.
Note: The configuration values listed above are common starting points. Adjust them based on your own specific samples and use cases.