Data Characteristics for This Category
Preclinical safety assessment data primarily originates from pharmacology and toxicology laboratory reports, pathology analysis reports, and biological sample testing reports. This data updates relatively infrequently, typically generated in batches upon completion of a research phase. Document structures are complex, often including numerous charts, raw data appendices, specialized terminology, and abbreviations. Fields involve dosage, administration route, animal species, observation indicators (e.g., body weight, organ coefficients, blood biochemical indicators), pathological descriptions (e.g., tissue lesion severity, incidence), and statistical results. Units vary (e.g., mg/kg, g, mL, kPa, mmol/L, μm), and unit inconsistencies may occur across different report sources. Data often contains extensive unstructured text descriptions, such as detailed records of animal behavior and vital sign changes.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The complexity of preclinical safety assessment data requires HTTP interfaces with robust file processing capabilities and flexible data parsing mechanisms. Since documents are often in formats like PDF and Word, support for various file types and content extraction is necessary. Low data update frequency means real-time requirements for interface design are not high, but data completeness and accuracy are critical. Diverse fields and units increase the difficulty of data mapping, requiring custom parsing rules or preprocessing scripts to standardize data. The large volume of unstructured text content challenges text segmentation and embedding model selection for subsequent knowledge base construction, potentially requiring longer context windows or specialized medical domain embedding models. When integrating external systems, consider data docking protocols with Laboratory Information Management Systems (LIMS) or Electronic Lab Notebooks (ELN) to ensure accurate and traceable data flow.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Preclinical safety assessment reports, especially those containing high-resolution images and raw data appendices, can have large individual file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF or Word documents, particularly scanned documents requiring OCR, can be time-consuming. |
maxContext | 4096 tokens | Preclinical safety assessment reports have high content density, containing extensive specialized terminology and related information, requiring a longer context to maintain semantic coherence. |
Segment Length | 800–1200 characters | Considering that reports may contain multiple related paragraphs, an appropriately long segment helps preserve semantic integrity and reduces the risk of critical information being split. |
Retrieval Count | Top 10 | Ensures coverage of multiple key sections or data points relevant to the query within the report, especially for complex queries. |
Similarity Threshold | Calibrate by measurement | The threshold needs experimental determination based on the specific embedding model and dataset characteristics to balance recall and precision, avoiding excessive filtering or introducing too much noise. |
HTTP_REQUEST_TIMEOUT | 30000 milliseconds | Data synchronization or retrieval of large reports from external LIMS/ELN systems may involve lengthy network transmission and backend processing times. |
Common Pitfalls
- HTTP requests return a 504 Gateway Timeout error: This usually occurs when an external system takes too long to process a request, exceeding FastGPT's or an intermediary proxy's default timeout settings.
- After uploading a large PDF report, the content parsing node outputs empty or incomplete results: The file is too large or contains complex structures (e.g., nested tables, scanned images), exceeding the file parser's processing capacity or configured memory limits.
- When calling a workflow via API to pass knowledge base variables, the workflow fails with a "Knowledge base does not exist" error: The variable name or value is not correctly mapped to the expected knowledge base ID or name in the workflow, preventing the system from finding the corresponding knowledge base.
Verification Steps
- Upload a typical preclinical safety assessment PDF report and verify that its content is fully and accurately parsed and segmented into knowledge base chunks.
- Configure an HTTP interface via a workflow to simulate fetching test data from an external LIMS system. Verify that data fields are mapped and processed as expected.
- Perform knowledge base retrieval tests for specific technical terms and key data points within the report. Observe the relevance and completeness of the retrieved results and adjust the similarity threshold as needed.
Note that the values provided are common starting points and should be measured 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.