What this category of data looks like
Railway operation policy and regulatory basis data is mainly sourced from normative documents, industry operation procedures, and safety management standards officially released by the National Railway Administration and China State Railway Group Co., Ltd. Data updates are completed synchronously when new policies are released. Most documents use a chapter-based structure. They include fields such as document number, issuing entity, effective date, specific clauses, and applicable scenarios. Some documents include associated information such as applicable sections and execution frequency. Field units include kilometers, passenger trips, operation duration, and other relevant units.
What constraints do these characteristics impose on the "multi-turn dialogue and prompt words" link?
The chapter-based structure of railway operation policy documents requires that context connections be maintained during multi-turn dialogue. The messages parameter must retain all key information from conversation history to avoid mixing up clauses across chapters. The synchronous data update feature requires the dialogue system to regularly sync the latest policy versions to prevent returning expired clauses. The applicable sections, execution requirements and other information included in the fields require prompt words to clearly define dialogue scenarios to avoid referencing policies across regions or non-corresponding scenarios. The overall long length of the documents requires that multi-turn context be truncated appropriately to avoid exceeding the model's processing limit, while ensuring accurate recall of target clauses.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the clause length of single chapters in railway policy documents, avoids splitting cross-clause content within a chunk |
| Maximum Context Length | 6000–8000 characters | Adapts to the overall length of railway policy documents, retains key context for multi-turn dialogue |
| Conversation Log Retention Period | 30–90 days | Meets the need for historical conversation review in railway operation scenarios, while controlling storage usage |
| Question Optimization Switch | Enabled | Optimizes policy keywords in vague user questions, improves the accuracy of clause recall |
| Single Recall Limit | Top 8 entries | Matches the clause density of railway policy documents, avoids recalling excessive irrelevant content |
| Reranking Switch | Enabled | Adjusts the relevance ranking of recalled clauses, prioritizes returning policy content that best fits the current dialogue |
Three common mistakes
- Phenomenon: Single conversation response time exceeds 10 seconds, and the interface shows loading timeout. Cause: The single recall limit is set too high, or the reranking switch is not enabled, resulting in an excessive amount of associated data processed by the model.
- Phenomenon: Historical dialogue cannot continue, and the system prompts that the corresponding context cannot be found. Cause: The
chatIdparameter is not configured correctly, leading to a break in conversation context association. - Phenomenon: The policy content generated in the dialogue includes expired clauses that do not match the currently effective policies. Cause: The railway policy documents in the knowledge base are not regularly synchronized and updated, resulting in outdated data versions.
How to confirm the configuration is correct
In FastGPT v4.17.0, the following actions can be used to verify that the configuration is correct:
- Upload a railway operation policy document, and test whether the chunked content retains complete single-chapter clauses without cross-chunk splitting.
- Initiate multi-turn dialogue, verify that historical dialogue can associate context using the
chatIdparameter, and conversation logs can be viewed within the configured retention period. - Submit a vague question, verify that the system's automatically optimized keywords can accurately recall the corresponding policy clauses, and the results can bind applicable scenarios via the
variablesparameter. - Review the number of single recall results, confirm that it does not exceed the configured single recall limit, and the ranking meets expected standards.
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.