What data for this category looks like
Precast concrete component data sources include BIM detailed design documents, rebar and concrete batch ledgers, mold table and curing kiln operation records, laboratory test reports, and component QR code traceability tags. Update frequency follows individual components, each batch of raw materials, and each curing kiln batch. Daily documents include component detailed drawings, rebar cutting lists, mold inspection records, pouring and vibration records, curing cycle records, and demolding and factory inspection reports. Data fields include component unique ID, rebar sleeve batch number, curing duration, demolding strength value, corresponding information for on-site hoisting positions, which are bound one-to-one with on-site installation nodes. This differs from flowable ready-mixed concrete delivered by truck.
What constraints these characteristics impose on source citation and traceability
Because data is updated by individual component, batch, and kiln run dimensions, citation and traceability must precisely match the component unique ID to avoid cross-batch confusion. In scenarios with multiple types of documents, corresponding data sources must be associated by document type. For example, link rebar cutting lists to rebar batch data, and bind curing records to mold table operation data. On-site installation data linked via QR code traceability tags requires cross-system synchronization, so the traceability link must support cross-data source associated queries. Errors directly affect on-site installation and project schedules, so cited data sources must be strongly bound to the component unique identifier, with no mismatches allowed.
How to configure the setup
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
quoteMaxToken | 2000–3000 token | Matches the total character count of single-batch precast component documents, to avoid truncating critical test data |
responseData | Structured JSON format | Adapts to workflow data parsing requirements, ensuring cited fields can be directly called |
detail | Top 6 matching results | Covers core information from multiple document types including component detailed drawings and test records |
| Data source association trigger condition | Match by component unique ID | Aligns with the individual-component update feature of precast component data, to avoid cross-batch confusion |
| Knowledge base cache validity period | 7 days | Matches the update frequency of precast component data, ensuring cited data is from the latest batch |
Three common configuration errors
- Scenario: After setting
quoteMaxTokento 3000, the large language model does not receive context content. Cause: The configured token count exceeds the system default context window limit, causing cited data beyond the limit to be automatically discarded. - Scenario: Uploading HTTP response request data as a citation yields no results. Cause: The response data was not converted to a structured format containing the component unique ID, so it cannot be associated and matched with component data in the existing knowledge base.
- Scenario: After configuring the knowledge base as a global variable, the workflow cannot call the corresponding knowledge base. Cause: The correct knowledge base binding parameter was not passed in the workflow node, and the dedicated data source for precast component data was not associated.
How to confirm the configuration is complete
- Upload single-batch precast component curing records and detailed drawings, verify that the citation source is correctly linked to the corresponding component's unique ID.
- Adjust the
quoteMaxTokenparameter, verify that context content is normally passed to the large language model along with parameter adjustments. - Trigger the workflow to call the knowledge base, verify that the returned results include preset component test data and document information.
- View system logs, confirm that the fields of cited data completely match the preset precast component data fields.
The values provided are common starting points and should be measured against samples appropriate to the specific deployment scenario.