What data for this category looks like
The document and material data for rail transit equipment primarily originates from R&D design documents, production process files, operation and maintenance records, compliance submission materials, and bidding documents. Data updates are adjusted dynamically based on R&D milestones, production batches, and operation and maintenance cycles. Document update frequency during the R&D phase shifts with design iterations, while maintenance records for the operation phase are updated monthly. Most documents follow a chapter-based structure, with fields including component model, production serial number, technical parameters, and compliance standard number. Units include professional metering identifiers such as millimeters, kilonewtons, and operating hours.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Reference only; verify against the target FastGPT release and deployment documentation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window size | 8000–12000 token | Matches the long-text characteristics of rail transit equipment documents, avoids truncating critical technical parameters |
variables | Pre-set variables for "component model", "production batch", and "document type" | Fixes core retrieval dimensions in multi-turn dialogue, reduces invalid recalls |
messages | Retain the most recent 20 interaction records | Balances context coherence and system response speed, adapts to the iterative needs of multi-turn document drafting |
chatId | Bind to the unique identifier of a single equipment project | Isolates document data across different projects, avoids cross-project information confusion |
| Retrieval recall limit | Top 8 matching results | Matches the core information volume of a single document, avoids redundant output content |
| Prompt template | Calibrated based on actual testing | Adapts to the format requirements of rail transit equipment professional terminology, ensures generated content meets industry specifications |
Three Common Misconfigurations
- Phenomenon: Generated document content exceeds the scope of the knowledge base, including non-preset external professional information. Cause: The prompt fails to clearly restrict the LLM to only use knowledge base recalled content for generation, causing the LLM to call its own general knowledge.
- Phenomenon: Generated operation and maintenance documents include component serial numbers and production batch identifiers from knowledge base paragraphs. Cause: The prompt fails to clearly require hiding internal metadata fields, causing the LLM to directly reference non-public identifiers in retrieval results.
- Phenomenon: Outputs from the custom model differ between the chat interface and workspace. Cause:
chatIdand the project-specific knowledge base collection are not uniformly bound, leading to inconsistent retrieval data sources across the two scenarios.
How to Verify Successful Configuration
- Initiate a document drafting request for a specified component model, verify that the generated content includes the target component’s professional parameters and has no internal identifier fields.
- Upload a professional technical drawing, confirm that the dialog box can load and display the corresponding content normally.
- Use the same custom model to initiate the same request in both the chat interface and workspace, verify the consistency of the two output results.
- Adjust the preset values of the
variables, confirm that the retrieval dimension can be updated in real time during multi-turn dialogue to match the corresponding documents.
The values provided here 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.