Data Characteristics for This Category
Mental health regulation and SOP data primarily originate from internal medical institution management procedures, guidance documents from national and local health authorities, drug instructions from regulatory bodies, and clinical pathway guidelines. These documents are typically in PDF, Word, or internal knowledge base formats. Update frequency is relatively low, mostly quarterly or annually, with occasional monthly updates for urgent policy changes. Document structures are rigorous, containing numerous clauses, flowcharts, approval forms, and medical terminology. Fields include diagnostic criteria (e.g., ICD-10 codes), treatment plans, drug dosages (in mg, ml), follow-up cycles, and risk assessment levels. Data is highly specialized, dense with terminology, and complex in its interrelations.
Constraints from These Characteristics on Tool Calling and Plugins
The specialized and complex nature of mental health regulation documents requires high precision in semantic understanding for tool calling to avoid incorrect answers due to misinterpreting terminology. The low update frequency means that initial knowledge base construction needs ample historical data import. Subsequent maintenance must focus on version control and incremental update mechanisms to ensure query result timeliness. Flowcharts and approval forms in documents demand structured information extraction capabilities from plugins, such as parsing process steps and key nodes from non-text elements. Furthermore, fields involving drug dosages and risk assessment levels dictate that tools must correctly identify units and perform range checks when processing numerical data to support accurate medication advice or risk warnings.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
maxContext | 2000–3000 characters | Mental health SOPs often contain long paragraphs and multi-step descriptions, requiring a larger context window to maintain semantic integrity. |
Chunk size (Chunk Length) | 400 characters | Appropriate chunk length helps the RAG model more accurately retrieve segments containing key clauses and process steps. |
Recall count (Retrieval Count) | Top 8 | Regulatory documents have strong interrelations; increasing retrieval count helps cover multiple relevant clauses, improving answer comprehensiveness. |
Similarity threshold (Similarity Threshold) | 0.75 | The medical field demands high precision. A higher similarity threshold filters out irrelevant retrieval results, reducing misinformation. |
Rerank result count (Reranked Return Count) | 5 items | Based on a high retrieval volume, reranking selects a small number of the most relevant items, improving the quality and conciseness of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF or Word documents can be time-consuming; extending the parsing timeout ensures complete file processing. |
Three Common Pitfalls
- Tool calls return null values, resulting in missing critical data in answers. This happens when plugins fail to correctly identify and extract table or flowchart information from documents, leading to empty structured fields.
- The system responds slowly to user queries or even times out. This occurs when knowledge base files are too large and not effectively pre-processed, leading to excessive computation during text embedding and retrieval.
- Drug dosage questions show unit errors or numerical mismatches, where output dosage units are inconsistent with actual values or exceed safe ranges. This happens when tools fail to correctly parse numerical fields with units in documents or do not perform numerical range validation.
How to Confirm Correct Configuration
- For core regulatory clauses, verify through questions whether the tool can accurately identify and quote the original text, checking consistency between the quoted content and the original document.
- Randomly select SOP documents containing flowcharts and verify whether plugins can extract key steps and decision nodes, presenting them in a structured manner.
- Input queries involving numerical questions like drug dosages and diagnostic criteria. Check if the returned numerical values and units are correct and compare them with standard documents.
- Simulate queries under high concurrency to monitor if system response times are within acceptable limits, ensuring the performance of the knowledge base and plugins meets expectations.
The values given 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.