HTTP Interface and External Systems for Preclinical Safety

Data Characteristics

Preclinical safety evaluation data originates from internal pharmaceutical research and development management systems, experimental reports from Contract Research Organizations (CROs), and regulatory databases. This data updates infrequently, typically with project progress or regulatory revisions, rather than in real-time. Document structures are primarily structured and semi-structured. These include experimental reports in PDF format, SOP documents in Word format, and raw data tables in Excel format. Experimental reports typically contain key fields such as dose, administration route, animal species, and observation indicators. Units strictly follow GLP guidelines, for example, mg/kg, μg/mL, g, days. SOP documents describe operational procedures, standards, and responsible parties.

Constraints Imposed by Data Characteristics on "HTTP Interface and External Systems"

The low update frequency of preclinical safety evaluation data means external systems do not require frequent synchronization. Timed tasks or manual triggers are sufficient. The structured and semi-structured nature of the documents requires HTTP interfaces to have robust file parsing capabilities, especially for text extraction and structured processing of PDF and Word documents. The strict unit and field specifications in experimental reports demand strong data validation capabilities from the interface. This ensures data consistency and accuracy during transmission and processing. Furthermore, due to data sensitivity, security authentication and Access Control List (ACL) mechanisms for the interface require high attention to prevent unauthorized access and data leakage.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`1200`	Ensures complete recall of critical information from SOPs and reports.
`Chunk size` (Segment Length)	`800–1000` characters	Balances semantic completeness of text with segment processing efficiency.
`Similarity threshold` (Similarity Threshold)	`0.75–0.80`	Improves the accuracy of question-answering results and reduces irrelevant information interference.
`Recall count` (Recall Count)	`Top 8 entries` (Top 8)	Covers more potentially relevant document segments, improving recall rate.
`PARSE_FILE_TIMEOUT_SECONDS`	`600` seconds	Accommodates parsing time for large PDF experimental reports or SOP documents.
`API_KEY_TTL`	`24` hours	Balances security with ease of use, refreshing keys periodically.

Common Pitfalls

Calling the API returns a 403 error code. This indicates incorrect API_KEY configuration or ACL permissions.
The model's response includes irrelevant experimental data or processes. This can happen if the Similarity threshold (Similarity Threshold) is set too low, recalling irrelevant document segments.
After text parsing, some critical fields are missing or garbled. This occurs when specific PDF or Word document formats are not pre-processed, preventing the parser from correctly identifying text encoding or structure.

Verification Steps

Call the interface and upload a typical safety evaluation report in PDF format. Check the knowledge base's slice preview in FastGPT to confirm that key fields like dose and animal species are correctly extracted.
Ask FastGPT a question about a specific safety evaluation process. Observe whether the model's answer accurately cites clauses from the SOP document and verify that the cited citation points to the correct document.
Simulate high-concurrency calls to the HTTP interface. Observe system response time and error rate. Ensure parameters like PARSE_FILE_TIMEOUT_SECONDS can support actual usage requirements and that no 504 Gateway Timeout errors occur.

The values provided are common starting points. Measure them against your own samples to determine optimal configurations.

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.