Data Characteristics in This Category
Psychiatric R&D documents draw from diverse sources, including clinical trial reports, patient medical records, genomic data, proteomic data, and neuroimaging reports. These documents have varying update frequencies. Clinical trial data typically updates regularly throughout the trial period, while basic research data may have new findings at any time. Document structures include common formats like PDF and DOCX, but also a large volume of unstructured text, such as scanned handwritten doctor's notes, and semi-structured data exported from electronic medical record systems. Specificity in fields and units is notable: diagnostic criteria usually follow DSM-5 or ICD-11, involving symptom descriptions, scale scores (e.g., HAMA for Hamilton Anxiety Rating Scale, PANSS for Positive and Negative Syndrome Scale), and biomarker concentrations (e.g., serum S100B protein concentration in μg/L).
Constraints Imposed by These Characteristics on "Conversational Logging and Auditing"
The complexity of psychiatric R&D document data sources requires conversational logs to detail the parsing process and origin of different document types for traceability. The unpredictable data update frequency necessitates an auditing function that can distinguish between new and old document versions and timestamp parsing results. The large volume of unstructured and semi-structured data can lead to parsing failures or information loss. Therefore, logs must precisely record the type and location of parsing anomalies, such as "OCR recognition error" or "field matching failure." Furthermore, the specialized nature of diagnostic criteria and scale scores requires logs to record the identification and standardization process of these professional terms to ensure data consistency. Fields with specific units, like biomarkers, require unit validation during parsing and auditing, and any unit conversion operations must be logged to prevent data misuse.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
LOG_LEVEL | INFO | Records key operations and potential issues, balancing log volume and detail. |
MAX_LOG_AGE_DAYS | 180 days | Meets R&D compliance traceability requirements, covering common project cycles. |
PARSE_ERROR_THRESHOLD | 0.05 | Tolerates minor parsing errors, but triggers an alert if exceeded, e.g., "field is empty" or "unit mismatch." |
AUDIT_INTERVAL_HOURS | 24 hours | Daily sample audit of key data parsing results to promptly detect potential deviations. |
CONTEXT_WINDOW_SIZE | 4096 tokens | Ensures capture of complete symptom descriptions and scale interpretation context. |
RETRIEVAL_TOP_K | 10 items | Retrieves sufficient relevant information snippets for analysis in complex medical case documents. |
Three Common Pitfalls
- Plugin loading failure or parameter not displayed: If a custom plugin's
manifest.jsonfile path is incorrect, or if the plugin's internalinputandoutputparameter definitions do not conform to FastGPT's API specifications, its input and output parameters will not display correctly in workflow tasks. - Service inaccessible after startup: Docker container port mapping configuration errors, such as mapping container internal port 3000 to an already occupied host port, or firewall rules blocking access to the FastGPT service port, prevent the frontend from connecting to the backend service.
- External system integration message desynchronization: Incorrect callback address configuration for external applications like Feishu, or a failure in FastGPT's internal message queue service, results in messages being successfully sent to the external system but not synchronized in FastGPT's message records.
How to Confirm Correct Configuration
- Check the "System Logs" module in the FastGPT backend to ensure that records of document uploads, successful parsing, and failed parsing are displayed correctly under the configured
LOG_LEVEL. - Upload a test document containing complex symptom descriptions and scale scores to the "Knowledge Base." Use the "Debug" function to view parsing results and confirm that scale fields like
HAMA_SCOREand units likeμg/Lare correctly identified and structured. - Trigger several parsing errors via API or the frontend interface (e.g., upload a corrupted PDF file). Observe whether error messages such as "parsing failed" or "field missing" appear in the "System Logs," and check if the error logs include the document ID and error type.
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.