What This Category of Data Looks Like
The data for infrastructure construction service consultation and response originates from three core sources: project construction archives, government service approval guidelines, and on-site compliance records. Construction archives contain structured fields such as project number, section scope, construction milestones, and acceptance standards. Government service approval guidelines clarify the handling procedures, required materials, and processing time limits for each approval item. On-site compliance records document daily construction progress and rectification requirements. The data update schedule shifts based on project progress and policy adjustments. Construction archives are updated daily to reflect real-time construction dynamics. Government service guidelines are updated quarterly to align with policy changes. Most documents pair structured fields with long explanatory text. Fields include specific units such as construction volume, approval time limits, and required material counts. Reference only; verify against the target FastGPT release and deployment documentation.
Constraints Imposed by These Characteristics on Model Access and Configuration
Long-text construction archives and government guidelines consume large amounts of context space. Strictly limit the model’s maxContext and maxTokens parameters to avoid context overflow or truncation of core content during calls. The data includes engineering-specific units and structured fields. This requires the model to have domain term recognition capabilities. Select embedding models and large language models adapted to the engineering field. High-frequency updated on-site compliance records require the model’s response time to match the real-time needs of service consultation. Avoid setting an excessively long timeout threshold. In addition, the service consultation scenario requires accurate responses. Control the model’s generation randomness within a low range to avoid generating incorrect construction standards or approval process content.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Meets the context carrying requirements for long documents in the infrastructure construction field, and avoids truncation of core construction standards and approval process content |
maxTokens | 2000–3000 token | Limits the output length of a single response, ensures that service consultation responses are concise and clear, and complies with government response specifications |
temperature | 0.1–0.3 | Reduces generation randomness, and ensures the accuracy of responses to professional content such as construction specifications and approval processes |
quoteMaxToken | 1500 characters | Limits the length of referenced source text, ensures that referenced construction logs or approval guideline content is complete and does not occupy excessive context |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Adapts to access requirements for local or third-party models, and supports docking with engineering-specific models |
AIPROXY_API_TOKEN | Fill in according to the exclusive key generated by the platform | Ensures identity verification security for model calls, and prevents unauthorized access |
Three Common Mistakes
- When creating a knowledge base, locally added Qwen2.5 and BGE-M3 models do not appear in the text understanding model dropdown list. The cause is that the embedding model access configuration is not associated with the vector database settings of the knowledge base, or the API endpoint configuration of the model channel has a path error.
- After connecting a local DeepSeek model, the conversation interface automatically switches to the GPT model, making normal conversation impossible. The cause is that the default model priority is not set to the local model, or the proxy service routing rules incorrectly forward requests to external public model channels.
- A 401 unauthorized error is returned when calling the model. The cause is that the
AIPROXY_API_TOKENparameter is filled in incorrectly, or the key permissions of the model channel are not opened to the access scope of the infrastructure construction scenario.
How to Verify Successful Configuration
- Go to the model channel management page, check that the status of the added engineering-specific model is "Connected", and confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters are filled in correctly. - Create a test knowledge base, upload a construction log document, check whether the configured embedding model appears in the text understanding model dropdown list, and confirm that the vector database index can be generated normally.
- Initiate a test consultation, such as "What is the acceptance process for concealed works for a certain section?", check whether the response references the correct source document content, and that no automatic model switching occurs.
- Adjust the
temperatureparameter to the test range, verify that the randomness of the response content meets expectations, and ensure that no errors appear in professional terms.
Note: The values provided are common starting points, and should be evaluated against the reader's own samples.
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.