What the data for this category looks like
Business data for precast concrete components originates from BIM detailed design documents, rebar and concrete batch ledgers, mold table and curing kiln operation records, laboratory test reports, and QR code traceability systems. Daily documentation includes component detailed drawings, rebar cutting lists, mold inspection records, pouring and vibration logs, curing cycle records, and demolding and factory inspection reports.
Data is updated per single component, each batch of raw materials, and each curing kiln run. Fields include component ID, rebar specification, embedded position, strength test value, and factory date. Some data takes the form of structured data exported from CAD, while other data exists as paper scans or unstructured documents.
What constraints these characteristics impose on model access and configuration
The data sources for this category are scattered and have diverse formats. Structured ledger data and unstructured engineering drawing files are both included. This requires model access to support multi-format document parsing.
Data updates occur at high frequency, with new data generated for each single component, each batch of raw materials, and each curing kiln run. It is necessary to control model context length to avoid interference from redundant information.
Professional fields such as embedded position and concrete strength test value require the model to have engineering domain semantic understanding capabilities. Severe consequences follow errors, so a balance must be struck between generation accuracy and response speed. Configuration must balance the context window and inference accuracy.
How to set the configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the average length of precast concrete component engineering documents, avoids truncating critical detailed drawing parameters and inspection records |
temperature | 0.2–0.4 | Reduces random generation probability, ensures accurate output of professional content such as rebar specifications and embedded positions |
quoteMaxToken | 2000–3000 token | Retains sufficient original document fragments for traceability verification during quality audits |
AIPROXY_API_ENDPOINT | Fill in according to the deployment environment | Adapts to the access address of the proxy platform, ensures normal model call links |
AIPROXY_API_TOKEN | Key bound to the proxy platform | Completes identity verification, prevents unauthorized calls |
| Workflow/API tool selection | auto | Automatically selects whether to call retrieval tools, adapts to audit scenarios with mixed multiple documents |
Three common errors
- Phenomenon: The model operates normally when tested on the proxy platform, but throws an error when called in FastGPT. Cause:
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not filled correctly in the FastGPT proxy configuration, or the proxy platform does not enable call permissions for the corresponding model. - Phenomenon: The knowledge base prompts that no retrievable content is available during use. Cause: Model call parameters are not configured correctly, or vector generation is not completed when uploading documents, resulting in failure to match retrieval fragments required for audit needs.
- Phenomenon: The generated audit result contains errors in rebar specifications and embedded positions. Cause: The
temperaturevalue is set too high, causing professional term generation to deviate from original document data.
How to confirm correct configuration
- Run a model call test, input a single inspection record fragment for precast concrete components, and verify that the model can correctly return structured audit conclusions.
- Upload a rebar cutting list document, check the vector generation progress and retrieval results to confirm that original fragments meeting audit requirements can be extracted normally.
- View the proxy call log, confirm that there are no spelling errors in the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations, and that no 401 or 404 status codes are returned. - Adjust the
temperatureparameter, compare the embedded position descriptions from two generations, and confirm that output consistency meets expectations after parameter adjustment.
The values provided are common starting points and should be measured against the reader's own samples.