What data for this category consists of
Internal office data for infrastructure construction comes primarily from project management systems, construction log systems, personnel attendance ledgers, and material inventory and procurement systems. Project milestones trigger data updates. Daily attendance data updates once per day. Material inventory data updates every 3 to 7 days. A single document typically includes fields such as project number, construction area, work period, associated work team, and material specification. Units include square meters, work days, tons, and person-times. Some documents include on-site photos or construction drawing attachments.
What constraints these characteristics impose during model access and configuration
Business data for infrastructure construction includes multi-dimensional structured fields and long-text attachments. Single document lengths vary significantly. Some consolidated construction log documents can reach several thousand characters. Data update frequencies are inconsistent. Some real-time data requires high-frequency synchronization. Static project ledgers have longer update cycles. The dataset size for this category expands continuously as projects progress. When accessing models, adaptation is required for multi-field recall and long-context processing. Index refresh strategies must also balance data with different update frequencies. This avoids affecting model output accuracy due to data lag or redundancy.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of multi-field, long-text infrastructure project data, avoids losing core information such as construction logs and material lists due to context truncation |
quoteMaxToken | 3000–4000 token | Matches the length of single core documents in infrastructure scenarios, such as construction plans and material ledgers, ensures complete recalled content is passed to the model |
temperature | 0.2–0.4 | Used for standardized question answering in internal office scenarios, reduces output randomness, ensures accuracy of content such as construction specifications and attendance rules |
AIPROXY_API_ENDPOINT | Fill in the locally deployed large model interface address, such as http://localhost:11434/v1 | Adapts to locally deployed large models, meets data privacy requirements in infrastructure scenarios, prevents sensitive project data from being leaked |
toolChoice | auto | Allows the model to automatically select tools based on infrastructure office needs, such as calling knowledge bases to retrieve construction specifications or query material inventory |
maxTokens | 2000–3000 token | Limits the length of single model output, avoids generating overly long construction guidance or report content, adapts to the concise communication needs of internal office |
Three common misconfigurations
- Scenario: After setting
quoteMaxTokento 3000 token, knowledge base recalled content cannot be passed to the large model context. Cause: WhenquoteMaxTokenexceeds the remaining quota ofmaxContext, the system automatically truncates recalled content or skips context passing, causing the model to fail to obtain valid knowledge base information. - Scenario: When deploying FastGPT v4.17.0 with docker-compose and a local Ollama, frequent Request Time errors appear in conversations. Cause: Large model interface timeout parameters are not configured correctly, or the local large model has a slow response speed, exceeding the default request timeout limit of FastGPT.
- Scenario: Configurations are not adjusted for the structured field characteristics of infrastructure scenarios, leading to low recall accuracy of exclusive business fields such as material codes and construction areas. Cause: General configurations do not adapt to the retrieval needs of multi-dimensional business fields in infrastructure scenarios, and cannot accurately match specific information in project ledgers.
How to confirm configurations are set correctly
- Initiate test questions containing core business keywords for infrastructure scenarios, check whether the model output includes valid content retrieved from the knowledge base.
- View request logs in the FastGPT backend, confirm that the response duration of large model requests meets expectations, and there are no frequent timeout errors.
- Adjust the
quoteMaxTokenparameter, verify whether the length of recalled content matches the configured expectation, and there is no abnormal truncation. - Test calling the locally deployed large model interface, confirm that requests can be sent normally and return results that meet business requirements are obtained.
All configuration values listed here are common starting points. They should be evaluated against the specific samples and business requirements of the deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.