Data Characteristics
Clinical guidance system data primarily originates from internal hospital regulations, standard operating procedures (SOPs), clinical guidelines, medical insurance policies, drug instructions, and various announcements. Most of these documents are unstructured text, typically in PDF, Word, or internal web page formats. Update frequency varies: core clinical guidelines and medical insurance policies are relatively stable, usually revised quarterly or annually. However, drug catalogs, department schedules, and temporary notices update more frequently, potentially weekly or even daily. Document structures commonly include titles, sections, clause numbers, body text, and attachments. Fields often cover disease names, symptom descriptions, department names, treatment processes, drug names, dosage units, and precautions. Dosage and time units (e.g., mg, ml, hours, days) are critical pieces of information.
Constraints from Data Characteristics on Deployment and Upgrade
The data characteristics of clinical guidance systems impose several requirements on deployment and upgrade. The prevalence of unstructured documents necessitates robust file parsing capabilities in FastGPT's data preprocessing stage to accurately extract text content. High-frequency updates for drug catalogs and temporary notices require the system to support incremental updates and version management, avoiding full data reconstruction with every update. Complex document structures and diverse field units mean the RAG (Retrieval-Augmented Generation) system needs more refined text segmentation strategies and entity recognition capabilities to accurately match clauses containing critical information like dosage and time during Q&A. Furthermore, deployment environments must consider the sensitivity of medical data, potentially requiring offline deployment or operation within an intranet. This places higher demands on the stability of database connections and image building processes.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 100 MB | Medical policy documents can be large, containing many images or tables. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF or Word documents can be time-consuming; sufficient time must be allocated. |
Chunk size (Segment Length) | 800–1200 characters | Ensures a single segment contains complete policy clauses or operating steps for semantic integrity. |
Recall count (Number of Retrieved Items) | Top 5 | Policy Q&A requires high accuracy; retrieve more relevant context for the model. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Balance precision and recall to retrieve relevant clauses while filtering irrelevant information. |
Rerank result count (Number of Reranked Items) | Top 3 | Reduces the number of tokens processed by the model while ensuring the most relevant core policy clauses are returned. |
Common Pitfalls
- Docker container fails to connect to the database: The service reports a database connection error upon container startup. This usually occurs because the Docker image was not correctly configured with the database connection string or the
DB_URLenvironment variable during packaging, preventing the container from resolving or accessing the host's database service address. - Retrieval results show only a single question or keyword: Even if the original query contains multiple retrieval points, the system displays only one. This is typically due to FastGPT version differences or configuration issues. Older versions might default to showing only one main retrieval term, while newer versions should support multi-keyword display or optimization in
Retrieval Settings. - Partial functionality issues after offline deployment: Components relying on external services (e.g., model downloads, font rendering) do not function correctly. This happens when not all dependencies are fully packaged during offline deployment, or external resources are not cached locally, preventing the system from accessing necessary resources in an environment without internet access.
Verification Steps
- Upload a PDF clinical guideline containing complex tables and diagrams. Check if file parsing is successful and verify that content is fully extracted.
- Use FastGPT's administration interface to perform Q&A tests on newly uploaded policy documents. Verify that critical information such as disease symptoms, drug dosages, and operating procedures can be accurately retrieved and used to generate answers.
- Simulate a policy update scenario by uploading a revised SOP and performing an incremental update. Confirm that the system can identify and process the updated content, and that previous versions remain traceable.
Note: 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.