Data Characteristics
Intelligent triage policy data primarily originates from internal institutional documents. These include regulations, standard operating procedures (SOPs), job descriptions, and medical service guidelines. Documents are typically in PDF, Word, or internal knowledge base pages. Update frequency is low, usually quarterly or annually, with ad-hoc updates for policy changes or process optimizations. Document structures feature chapters, articles, and appendices. Content is rigorous, containing specialized terminology, medical abbreviations, and legal clauses. Fields include department names, policy numbers, effective dates, revision versions, specific operating steps, responsible parties, and risk warnings. Units are typically text descriptions, dates, and numbers.
Constraints on Multi-Turn Conversations and Prompts
The rigorous and specialized nature of intelligent triage policy documents requires multi-turn conversations to precisely understand user intent. Avoid vague answers and ensure accurate policy interpretation. Low update frequency means knowledge base construction must prioritize version management to ensure retrieval of the latest effective information. Extensive specialized terminology and abbreviations in documents require prompt design to incorporate synonym expansion and context understanding. This addresses diverse user query styles. Policy questions often involve sequential operational procedures. The system must track the user's focus across multiple turns, guiding them to complete information, rather than limiting interaction to single questions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8 | Policy questions often require longer contexts to understand user intent and historical queries. |
Chunk size | 500–800 characters | Policy text paragraphs are long. Shorter segments can break semantic meaning, while longer segments can affect retrieval efficiency. |
Recall count | 5–8 entries | Ensures coverage of relevant policy clauses, providing sufficient candidates for subsequent re-ranking. |
Similarity threshold | 0.75 | Guarantees the accuracy of retrieved content, preventing interference from irrelevant policies. |
Rerank result count | 2–3 entries | Focuses on the most relevant policy clauses, reducing the model's processing burden. |
Temperature (Temperature) | 0.1–0.3 | Policy questions require rigorous results. Lower temperatures help generate deterministic answers. |
Common Pitfalls
- A "400 status code (no body)" error appears in the conversation. This indicates tool call failure, typically due to mismatched external tool interface parameters or network request exceptions.
- After a user deletes conversation content, the backend logs are not simultaneously deleted. This leads to data inconsistency. The log management module is decoupled from frontend operations and lacks linked cleanup.
- Global variables saved in previous historical conversations cannot be loaded in new conversations. The workflow design lacks a cross-session persistence mechanism for global variables.
Verification
- Simulate user questions about a specific policy process. Check if the system accurately understands and provides correct policy clause references.
- Test queries with different phrasing, including specialized terminology and everyday language. Verify if the system consistently retrieves relevant policy content.
- In multi-turn conversations, check if the system continues to provide relevant and coherent policy information based on the focus of previous turns.
The values provided are common starting points. Measure them against your 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 2026-09-21.