Data Characteristics
R&D documents in hospital operations typically include clinical trial protocols, research reports, ethics approval documents, Standard Operating Procedures (SOPs), and equipment maintenance manuals. These documents originate from various sources, such as internal R&D departments, partner institutions, and external regulatory bodies. Update frequencies vary; clinical protocols might undergo multiple revisions during a trial, while SOPs update regularly based on regulatory changes or internal process optimizations. Document structures are primarily PDF and Word formats, containing numerous tables, charts, and unstructured text. Fields involve patient IDs, drug dosages, observation indicators, and equipment models. Units cover international standards (e.g., mg, mL) and commonly used clinical units (e.g., mmHg, bpm).
Constraints on Dialogue Logging and Auditing
The complexity and diversity of hospital R&D documents impose specific requirements on dialogue logging and auditing. First, heterogeneous data from multiple sources can introduce uncertainties during parsing. Logs must detail input, output, and potential error information for each parsing stage to facilitate issue traceability. Second, uncertain document update frequencies imply potential version discrepancies in the knowledge base. Auditing must clearly identify the knowledge version underpinning a dialogue to ensure response accuracy and timeliness. Third, documents containing sensitive information (e.g., patient data) require log records to comply with data security and compliance standards. Auditing must verify data access permissions and usage scope. Finally, the presence of extensive specialized terminology and measurement units requires logs to clearly show the model's understanding and processing of this content, providing a basis for model optimization.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
logLevel | INFO | Captures critical operations and error information, balancing performance and auditing needs. |
logRetentionDays | 90 days | Meets most regulatory requirements for historical data traceability while controlling storage costs. |
maxContext | 4000 token | Accommodates the context length of R&D documents, ensuring dialogue coherence and preventing loss of key information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles complex parsing of large PDF or Word documents, preventing processing failures due to timeouts. |
embeddingModel | text-embedding-ada-002 or equivalent performance model | Ensures accurate understanding of specialized terminology and complex semantics, improving recall quality. |
auditLogFields | userId, query, response, sourceDocs, timestamp, knowledgeVersion | Records key dialogue elements for compliance review, issue tracing, and knowledge base version verification. |
Common Pitfalls
- A dialogue workflow shows failure, but the model backend has response logs. This typically occurs due to unstable network connectivity between FastGPT and the model service, or an internal FastGPT workflow configuration error, preventing the model response from being correctly returned to the workflow for processing.
- Dialogue logs for yesterday and today are empty. This might be due to improper log storage service configuration, such as an unwritable storage path, or the logging function being accidentally disabled.
- The model provider page shows no call logs. This often results from insufficient API key permissions, or the model provider's logging service not being correctly enabled, preventing FastGPT from obtaining or displaying detailed model-level call records.
Verification Steps
- In the FastGPT backend application dialogue, input typical R&D document-related questions. Check if the dialogue logs contain the complete user query, model response, and cited knowledge document sources.
- Attempt to upload a PDF document with complex tables and specialized terminology. Observe whether the parsing logs record the detailed parsing process, including identified fields and units.
- Simulate an abnormal situation, such as providing an unparsable file or triggering a known workflow error. Then, check if the logs contain corresponding error messages and stack traces, and verify error codes.
- Regularly check log storage space usage. Ensure log files are stored and cleaned according to the configured retention policy to prevent storage overflow.
Note: The values provided are common starting points. Measure 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.