HTTP Interface and External Systems for Preclinical Safety

Data Characteristics

Preclinical safety assessment data primarily originates from pharmacology, toxicology, and pharmacokinetics experimental reports. This data is typically structured or semi-structured. Sources include internal laboratory systems, reports from Contract Research Organizations (CROs), and compliance databases. Update frequency is relatively low, usually occurring in batches as projects progress, for example, after each key experimental stage. Document structures are complex, containing detailed experimental designs, procedures, raw data, statistical analysis results, and conclusions. Fields cover compound information, dosage, administration routes, animal models, observation indicators, and pathological findings. Units involve milligrams per kilogram (mg/kg), micromoles (µmol), days, and percentages (%), often accompanied by complex abbreviations and specialized terminology.

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

Preclinical safety assessment data volumes are often large. A single report can contain hundreds of pages of detailed information. This requires HTTP interfaces to handle large file uploads and downloads. Data updates are infrequent but large in volume, necessitating a carefully designed incremental synchronization strategy to avoid redundant transfers and data duplication. Complex document structures mean that data parsing requires more sophisticated logic to extract key fields, potentially using regular expressions or specific parsing libraries. The specialized and diverse nature of fields, especially units and abbreviations, places high demands on external system data mapping and standardization to ensure FastGPT correctly understands and utilizes this information. Additionally, data sensitivity is high, requiring encrypted interface transmission and robust authentication mechanisms.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`3000–4000 characters`	Preclinical safety assessment reports are dense, requiring a longer context window for complete information.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Individual safety assessment reports can be large, containing high-resolution charts and extensive raw data.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing complex PDF or Word documents can be time-consuming, requiring a longer timeout.
`Segment Length`	`800–1200 characters`	Maintains semantic integrity of paragraphs, preventing truncation of critical experimental data or conclusions.
`Recall Count`	`Top 10`	Ensures retrieval of sufficient relevant experimental data and background information, improving answer accuracy.
`Similarity Threshold`	`0.75`	Preclinical data demands high precision; a high threshold helps filter out irrelevant segments.

Common Pitfalls

When FastGPT calls an external API, returned data fields are empty. This often occurs because the external system's JSON structure does not match FastGPT's expected mapping path, or data types are incompatible.
Uploading large experimental report files results in a 413 Request Entity Too Large error. This happens due to size limits on the request body imposed by FastGPT or its underlying web server.
Frequent 429 Too Many Requests status codes occur during concurrent requests to external databases. This is because the external database or API has call rate limits, and appropriate request queue management or backoff strategies are not implemented.

Verification Steps

Upload a typical preclinical safety assessment report (e.g., a PDF with charts and tables). Check if FastGPT successfully parses and indexes it.
Use FastGPT's knowledge base Q&A feature to ask questions about key experimental data and conclusions in the report. Verify the accuracy and completeness of the answers.
Use FastGPT's debugging tools to inspect HTTP request and response logs for interactions with external systems. Confirm data transfer formats and status codes are normal.
Simulate high concurrency scenarios. Observe external interface response times and error rates to ensure system stability under pressure. Adjust parameters like max_retries as needed.

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.