Data Characteristics
High-value consumable policies and SOP documents typically originate from internal medical institution management departments, such as medical affairs, equipment, and procurement centers. These documents have a relatively stable update frequency, generally following annual reviews or policy changes. They are not frequently updated. Documents are often in PDF or Word format, containing detailed approval processes, usage specifications, procurement catalogs, supplier information, and pricing standards. Fields often include consumable codes, names, specifications, units, billing units, registration certificate numbers, manufacturers, clinical departments, and usage permissions. Note that the unit and billing unit may differ; for example, procurement might be by "box," while usage is billed by "item."
Constraints on Multi-Turn Conversations and Prompts
The stability of high-value consumable policy documents means that knowledge base content maintenance costs are relatively low after initial construction. However, accurate document parsing is critical. Detailed field information, such as consumable codes and registration certificate numbers, requires precise matching in multi-turn conversations to avoid confusion from similar names or vague descriptions. The inconsistency between units and billing units means that when answering questions about cost or usage, the AI must differentiate and correctly cite them. This requires prompt design to guide the model to focus on these subtle differences. Additionally, process-oriented content, such as approval steps, requires the model to understand context and reason to support in-depth follow-up questions about specific process steps in multi-turn conversations. Response speed is also important; rapid access to accurate information is a core requirement for engineers, especially in urgent situations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | High-value consumable policy documents often have long paragraphs with multiple regulations. This length helps maintain contextual completeness. |
Recall count (Recall Count) | Top 8 | Ensures coverage of various related policy clauses, especially for maintaining information continuity in multi-turn conversations. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | The professional nature of policy texts requires a high degree of matching to avoid recalling irrelevant content. |
Rerank result count (Rerank Return Count) | Top 5 | Further filters policy clauses most relevant to the current conversation intent, improving answer accuracy. |
maxContext | 4000–8000 Token | Policy Q&A often involves multi-turn follow-ups and detail confirmation, requiring a longer context window to maintain conversational coherence. |
ENABLE_FILE_PARSING | true | Ensures the system can parse uploaded PDF or Word policy files, which is fundamental for acquiring knowledge. |
Common Pitfalls
- Symptom: Uploaded policy files are consistently not recognized or parsed by the knowledge base, preventing answers based on file content. Reason: The
FILE_PARSING_TIMEOUT_SECONDSparameter is set too short. Large policy files fail to complete parsing within the allotted time. - Symptom: In multi-turn conversations, the AI "forgets" consumable or policy details discussed in the previous turn after the second or third turn. Reason: The
maxContextparameter is set too small, causing historical conversation information to be truncated too early, and the model cannot retain sufficient context. - Symptom: When asked about the billing unit for a specific consumable, the AI's answer unit does not match the actual usage unit or provides a vague answer. Reason: The prompt does not explicitly guide the model to differentiate between "procurement unit" and "billing unit," preventing the model from effectively identifying and distinguishing these two types of information during retrieval.
How to Verify Configuration
- Upload a consumable policy document containing complex approval processes and multi-unit definitions. Confirm the file parsing status shows "success."
- Conduct at least three turns of conversation regarding a high-value consumable procurement process. Verify if the AI can accurately remember and cite the consumable name and process steps mentioned in previous turns.
- Randomly select a high-value consumable from the policy. Ask about its procurement unit and billing unit. Verify if the AI's answer matches the specific fields in the document.
- Simulate an engineer's urgent query about a consumable usage specification. Evaluate if the AI's response speed meets the requirement for quickly obtaining information.
The values provided 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 2026-09-21.