Data Characteristics
Preclinical safety assessment data originates from toxicology, pharmacokinetics, and pathology reports. These documents are typically PDFs, Word files, or structured text like Study Data Tabulation Model (SDTM) files. Data updates are infrequent, usually occurring at study milestones or before regulatory submissions. Document structures are complex, containing specialized terminology, dosage information, individual animal data, statistical results, and graphics. Fields include animal ID, dosage (mg/kg), observation metrics (e.g., body weight, organ coefficients, hematology parameters), abnormality descriptions, and pathological diagnoses. Units are largely standardized, but minor variations across studies require careful identification.
Constraints on HTTP Interfaces and External Systems
The complex structure and specialized nature of preclinical safety assessment documents require HTTP interface designs that handle diverse file types and content parsing. Infrequent document updates mean real-time external system requirements are low. However, data consistency and version management are critical. Specific fields and units require external systems to perform effective unit identification and standardization after data extraction to prevent misinterpretations from unit confusion. Large graphics and tabular data in HTTP transfers require consideration of file size and transmission efficiency, potentially needing pre-processing or chunked transfers. External systems must be highly stable to ensure reliable data acquisition and processing before critical regulatory submissions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Preclinical safety assessment reports often contain numerous images and tables, leading to large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDF and Word document parsing can be time-consuming; this prevents timeouts. |
maxContext | 32000 | Preclinical safety assessment reports are dense and specialized, requiring a larger context window for understanding. |
Chunk size | 800–1200 characters | Ensures each segment contains sufficient information to understand specialized concepts and context. |
Similarity threshold | 0.78 | High precision is required for preclinical safety assessment terminology; a high threshold recalls more relevant passages. |
Rerank result count | Top 10 entries | Ensures critical information is not overlooked due to initial retrieval ranking biases. |
Common Pitfalls
- Receiving an
AIPROXY_API_ENDPOINT or AIPROXY_API_TOKEN is not seterror when calling an external interface. This typically indicates incorrect environment variable configuration, preventing FastGPT from connecting to its proxy service and affecting external API calls. - File parsing failure or empty content after uploading a file via the HTTP interface. This can occur due to incompatible file encoding, unsupported file formats, or special characters in the file content that interrupt the parser.
- Mismatched data structures from external system data retrieval, leading to subsequent processing errors. This often results from external system API version updates or improper request parameter settings, causing incorrect or incomplete data returns.
Verification Steps
- Upload a typical preclinical safety assessment PDF report. Verify normal parsing and vector generation. Check the knowledge base for segmented content.
- Use FastGPT's debug interface to simulate HTTP interface calls. Confirm the external system correctly receives requests and returns data in the expected format.
- Query specific technical terms and dosage information from the report. Verify FastGPT accurately understands and references relevant passages from the knowledge base.
- Check external system logs to confirm FastGPT's HTTP requests were successful and response times are within acceptable limits.
The values provided are common starting points. Measure against your own samples to determine optimal settings.
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.