What This Type of Data Looks Like
Data sources include engineering orders, sand, gravel, cement admixture inspection reports, moisture content measurement records, mixing plant control system data, and laboratory test block records. Update frequency follows per vehicle production order, per batch of raw material inspection, and daily ledger summary. Daily documents include mix proportion notice, opening identification, production and transport order, raw material inspection report, and test block strength report. Fields include aggregate moisture content, mix proportion parameters, pouring time period, strength grade, slump requirement, and others. Units include kilograms, cubic meters, percentage, and megapascals. This document is written based on FastGPT v4.17.0.
What Constraints These Characteristics Impose on the "Model Access and Configuration" Link
Prioritize reading the latest version of high-frequency updated production and inspection data when the model is called, to avoid referencing expired mix proportion or moisture content parameters. Limit the retrieval scope for multiple types of professional documents to ensure the model only matches test and order data related to engineering bids. Configure parameters to accurately bind order numbers and corresponding inspection records, to associate bid quotations with the raw material costs of specific orders. Retain all professional parameters in documents fully to prevent content loss due to truncation during retrieval.
How to Set the Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Ready-mixed concrete bid documents include multiple mix proportion sheets, orders, and inspection reports, requiring space to hold complete project background and historical data |
quoteMaxToken | 1500–2000 token | The amount of raw material cost and mix proportion data referenced in a single bid quotation is limited; controlling the reference length avoids redundancy |
maxTokens | 3000–4000 token | The output of bid documents and quotations requires detailed parameter descriptions, so sufficient response length must be reserved |
temperature | 0.1–0.3 | Bid content must be rigorous and accurate; reducing randomness ensures consistency of parameters and values |
AIPROXY_API_ENDPOINT | Fill in according to actual deployment | Adapt to the enterprise's internal network environment, need to fill in the actual deployed API endpoint |
| Workflow/API tool selection | auto | Allow the model to independently choose whether to call the knowledge base, adapting to document retrieval needs for different bid scenarios |
Three Common Mistakes
- Phenomenon: The large language model returns no relevant content, while the background knowledge base index list shows that the mix proportion notice and raw material inspection report have been uploaded. Cause:
quoteMaxTokenis set too small, the length of the retrieved document fragments is insufficient to trigger the model's content matching logic. - Phenomenon: The team management interface cannot filter out members who have used the model, making it impossible to locate the specific calling subject. Cause: The global configuration of team usage logs is not enabled, or the corresponding member is not assigned model call permissions.
- Phenomenon: The slump parameter in the generated bid quotation deviates from the actual inspection value. Cause: The
temperaturevalue is set too high, causing random fluctuations in the parameters generated by the model, which do not fit the fixed parameter requirements of actual production.
How to Confirm the Configuration Is Correct
- Upload a ready-mixed concrete mix proportion notice and corresponding order data, initiate a simulated bid quotation request, and check whether the returned content includes the matching aggregate moisture content correction value.
- View the team management usage logs to confirm that the account initiating the request has corresponding model call permissions, and the log records are complete.
- Adjust the
temperatureparameter to the set range, initiate three identical requests, and check whether the core numerical parameters in the returned results remain consistent. - Test the response length during the call to confirm that the
maxTokensvalue is sufficient to accommodate the complete quotation description and document references.
The provided values are common starting points and should be measured against samples specific to the actual deployment.