What the data for this category looks like
Rail transit equipment policy and regulatory basis data originates primarily from official documents published by the National Railway Administration and the Equipment Industry Department I of the Ministry of Industry and Information Technology. Updates align with the release of new industry policies and standards. Individual documents range from hundreds to tens of thousands of characters. Document structures include modules such as policy document number, issuing authority, effective date, applicable equipment category, and core clause content. Fields include policy unique identifier, release date, applicable scenario, and clause hierarchy. Units use standard document number format, date format, and character units. Different equipment subcategories such as EMUs and urban rail vehicles have exclusive policy entries.
What constraints these characteristics impose on workflow orchestration
Rail transit equipment policy documents have wide length variation, with individual documents reaching tens of thousands of characters. This requires workflow chunking nodes to adapt to long text splitting logic, and prevent single chunks from spanning multiple policy clauses. Policies are updated frequently with new industry regulations. This requires the knowledge base sync interval to be set to a short range, to ensure the timeliness of retrieved content. Different equipment subcategories have exclusive policies. Workflows must pass the variables parameter with the corresponding category identifier to complete precise filtering of the retrieval scope. The policy document number is the unique identification field. This requires the retrieval link to prioritize matching this field, to avoid confusion between policy documents of different levels. Policy clause hierarchies are clearly defined. Workflows must support extracting core content by clause hierarchy, to reduce interference from redundant information on question answering.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the long clause structure of rail transit equipment policy documents, and prevents single chunks from spanning multiple policy clauses |
| Overlap Length | 100–200 characters | Ensures contextual coherence of policy clauses, and avoids splitting core logical connections between chunks |
| Single Search Limit | Top 8 entries | Covers multi-clause content related to policies, while controlling the total context length |
| Maximum Context | 6000 characters | Matches the core information volume of policy Q&A, and aligns with the processing limit of general large models |
| Variable Filtering | Filter by equipment type | Corresponds to the category identifier passed via the variables parameter, to precisely limit the retrieval scope |
| Retry Mechanism | Retry 2 times after failure, with a 30-second interval | Addresses scenarios such as knowledge base sync delays or temporary interface fluctuations |
Three common misconfigurations
- After a workflow runs, it returns "No relevant policies matched". The cause is failure to pass the filter parameter for the corresponding equipment type via the
variablesparameter, leading the retrieval scope to cover policy documents from unrelated categories. - The tool call module returns no tool results. The cause is that the knowledge base sync interval is set too long, and the latest released rail transit equipment policy documents have not been updated in a timely manner.
- The code execution module throws a "ModuleNotFoundError" error. The cause is failure to install the dependency libraries required for policy data processing in the Python runtime environment of the FastGPT container.
How to verify successful configuration
- Pass a preset equipment type parameter, run the workflow, and check if retrieval results only include policy documents for the corresponding category.
- Manually trigger a knowledge base sync, wait for sync completion, then run the workflow to verify that latest released policy content can be retrieved.
- Import preset dependency libraries in the code execution module, run test code, and confirm no missing module errors.
- Check if the
nodeIdof the corresponding node matches the binding relationship in the workflow orchestration, to troubleshoot abnormal node positioning issues. - In FastGPT v4.17.0, simulate a temporary interface fluctuation test scenario, and verify that the workflow retry mechanism triggers normally and resumes operation.
The provided values are common starting points and should be evaluated against individual deployment 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.