What this category of data looks like
Rail transit equipment inquiry data comes from three main sources: original equipment manufacturer official technical manuals, railway operation and maintenance department maintenance procedures, and component supplier maintenance documents. The data covers fault codes, maintenance processes, component parameters and other content for locomotives, EMUs, and urban rail vehicles. Teams organize data by train model. Individual documents can reach over ten thousand characters in length. Fields include train model number, fault code, component model, maintenance man-hours, and safety operation specifications. Units include millimeters, hours, times and others. The system updates full data synchronously when new train models launch. It refreshes partial content quarterly when daily operation and maintenance specifications change.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Rail transit equipment data is organized by train model with clear fields. Multi-turn dialogue must confirm the specific train model used by the user in the initial stage. Otherwise, the system cannot accurately match corresponding documents. Fault codes follow strict numbering rules. Prompts must consistently require users to provide standard fault codes. This prevents the system from retrieving irrelevant content due to vague queries. Document update frequencies vary. The knowledge base must synchronize new train model data and daily operation and maintenance specifications in batches. Multi-turn dialogue context must not include expired train model parameters. Individual documents are lengthy. The system must limit the amount of content retrieved in the context window. This avoids redundant data interfering with dialogue logic.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window size | 8000–12000 characters | Individual technical documents for rail transit equipment can be up to over ten thousand characters in length, so the system must retain complete fault troubleshooting dialogue chains |
messages field format | Concatenated in the order of "train model + fault phenomenon + troubleshooting steps" | Matches the categorized archive structure of rail transit equipment data to improve retrieval accuracy |
variables bound parameters | Fixed binding of "train model number" and "fault code" | Restricts core parameters that must be clarified during dialogue to avoid vague queries |
chatId session isolation | Assign independent identifiers to each user | Ensures different users can only view their own dialogue records, aligning with operation and maintenance personnel usage scenarios |
| Single retrieval limit | Top 3 matching documents | Fault data for rail transit equipment has clear entries; excessive retrieval will interfere with dialogue logic |
| Timeout threshold | 300 seconds | Prevents loading timeout for lengthy rail transit equipment technical documents |
| Knowledge base storage limit | 100 MB | Adapts to the total storage scale of rail transit equipment documents to avoid resource overflow |
Three common misconfigurations
- Symptom: Different users see identical dialogue records after logging in, and cannot view their own historical conversations. Cause: The system does not assign an independent
chatIdto each user, so all sessions share the same identifier. - Symptom: The Human field in API response dialogue previews displays as null, and the user's actual input content cannot be viewed. Cause: The system does not correctly pass the user's input content in the
messagesfield, or the parameter format does not meet requirements. - Symptom: Dialogue response is significantly delayed, with a long wait time after input before a result is obtained. Cause: The system does not limit the number of documents retrieved per session, so each dialogue loads excessive redundant rail transit equipment technical documents.
How to verify correct configuration
- Assign different
chatIdvalues to two test users, launch identical fault queries, and confirm each user’s dialogue record only contains their own interactive content. - Call the API to pass the complete
messagesfield including the user’s actual input content, and check if the Human field in the response preview correctly displays the input content. - After configuring the prompt template, launch a fault query that does not specify a train model, and confirm the reply requests supplementary train model number to verify the prompt is effective.
- Import the longest individual rail transit equipment technical document, test that the dialogue context fully loads the document’s key content without truncation.
The values provided here 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.