What the Data for This Category Looks Like
Internal office data for infrastructure construction primarily comes from project construction ledgers, daily construction logs, material site acceptance receipts, personnel attendance records, equipment operation and maintenance archives, and similar sources. Data update rhythm adjusts based on on-site operations. Construction logs are updated daily. Ledgers and acceptance receipts are updated in real time as materials arrive or personnel change. Documents include structured fields and unstructured content. Structured fields include project number, construction section, material specification, number of attendees, equipment model. Units include tons, cubic meters, person-times, hours, and others. Unstructured content includes URLs of on-site construction photos, transcribed text of handwritten log scans.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Structured fields are numerous and units are clearly defined. Multi-turn dialogue must use the messages parameter to retain key information such as project numbers and material specifications from previous sessions. This prevents the model from confusing data from different construction sections or projects. Unstructured image URLs require explicit instructions in the prompt to attach the correct URL path when referenced. Otherwise, on-site materials cannot be displayed during employee queries. Data update frequency is high. The prompt must specify priority access to the latest knowledge base entries within the current session. This ensures returned construction data matches on-site status. Single sessions have large associated data volumes. The context length of multi-turn dialogue must adapt to the scale of existing data. This prevents information truncation.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
messages | Retain the first 10 rounds of conversation content | Cover multi-turn dialogue context requirements, avoid losing key identifiers for construction projects |
| Maximum Context | 8000–12000 token | Adapt to the scale of existing data for multi-turn dialogue in infrastructure construction, prevent information truncation |
| Single Search Limit | Top 8 entries | Cover core data such as construction ledgers and logs required for single-round queries, reduce redundant returns |
variables | Pass project number and construction section parameters | Achieve session isolation for different projects, ensure accurate matching of data queries |
| Chunk Length | 1000–1500 characters | Match paragraph length of construction documents, retain complete construction logic |
| Query Optimization | Enabled | Automatically optimize query terms for construction terminology, improve knowledge base recall accuracy |
Three Common Misconfigurations
- When integrating with Feishu or WeChat conversations, returned content includes punctuation marks such as # and *. Reason: No Markdown format filtering is specified in the prompt, and no format constraints for conversation output are configured.
- When calling the conversation API, no referenced knowledge base ID is returned. Reason: The knowledge base association field storage for conversation logs is not enabled, or the switch for returning reference identifiers is not configured in the
variablesparameter. - In locally deployed FastGPT, knowledge base image URLs cannot be displayed in conversations. Reason: No image analysis prompt is configured for the model, and the URL is not bound to the correct document field in the knowledge base.
How to Verify Successful Configuration
- Initiate a multi-turn conversation including project number and material specification. Check if the
chatIdand context retained by themessagesparameter include previous project information. - Call the conversation API. Check if the returned result includes relevant identifiers for the referenced knowledge base.
- Upload construction documents containing image URLs. Initiate a conversation. Check if the model reply correctly attaches the corresponding URL.
- Initiate a query including large volumes of structured data. Check that no overflow or truncation occurs in the conversation context.
The values provided are common starting points and should be measured 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.