Data Characteristics
Nursing management policy data primarily originates from internal hospital regulations, standard operating procedures (SOPs), job descriptions, quality management documents, and nursing department meeting minutes. These documents are typically in PDF, Word, or scanned image formats. Update frequency is relatively low, usually quarterly or annually, with occasional ad-hoc updates for policy changes or major events. Document structure primarily consists of chapters, articles, and appendices. Content is rigorous, containing extensive professional terminology and normative statements. Fields and units involve personnel titles, shift schedules, drug dosage units (e.g., mg, ml), time units (e.g., hours, minutes), and procedure step numbers. High precision and consistency are required.
Constraints on Deployment and Upgrades
The low update frequency of nursing management policy documents means a significant initial data import effort but less pressure for subsequent incremental updates. The rigorous document structure and professional terminology require FastGPT's text splitting strategy to be more refined, avoiding the severance of key concepts or operational steps. The presence of PDF and scanned image formats necessitates robust OCR capabilities to ensure accurate text extraction. The precision requirements for fields and units mean that knowledge base construction should consider the recognition and association of numbers and specific units to support questions about specific operational parameters. Furthermore, the complex hierarchical relationships within policy documents require FastGPT to have strong contextual understanding and multi-turn conversation capabilities, allowing users to deeply query specific clauses or related policies.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 100 MB | Policy documents may contain numerous images and charts, leading to large file sizes. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Ensures each segment contains a complete policy item or operational step, preventing semantic fragmentation. |
Recall count (Recall Count) | Top 5 entries (top 5) | Policy Q&A requires high accuracy; recalling more relevant context helps improve answer quality. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Balances recall and precision based on specific test results, typically between 0.75–0.85. |
Rerank result count (Reranked Return Count) | Top 3 entries (top 3) | Further filters the most relevant items from the recall results, reducing the model's burden of processing irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Parsing large PDFs or documents with complex tables can be time-consuming. |
Common Pitfalls
- After importing the knowledge base, queries for key policy items return empty or incomplete results. This manifests as "no results found" or "incomplete answers." The cause is failed document parsing or improper text splitting, leading to critical information not being correctly indexed.
- For scanned policy documents containing numerous tables or flowcharts, OCR recognition results show garbled text or missing information. This appears as "answer content inconsistent with the original" or "missing key data." The cause is insufficient OCR engine capability for complex layouts or a lack of manual proofreading.
- AI answers to questions involving specific numerical values or units are vague or incorrect, such as "drug dosage unclear." The cause is the knowledge base's failure to effectively identify and associate numbers and unit fields in policies during construction, leading to semantic understanding deviations.
Verification
- Randomly select 10 core policy documents to verify whether all their content has been successfully imported into the knowledge base, and check for parsing errors or omissions.
- Conduct multi-turn questioning tests for complex items, multi-level policies, and regulations containing tables to ensure the AI can accurately understand and provide complete, consistent answers.
- Design test questions that include specific numerical values and professional units to verify whether the AI can precisely cite and correctly process this information in its answers.
Note: The values provided are common starting points and should be measured against specific 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.