Deployment and Upgrade for Preclinical Safety Assessment

Data Characteristics

Preclinical safety assessment data comes primarily from laboratory research reports, toxicology reports, pharmacokinetic reports, and safety evaluation reports generated during drug development. These documents are typically in PDF, Word, or Excel formats. Content includes animal experiment data, pathological descriptions, and biomarker analysis results. Data update frequency is relatively low, occurring mainly at key project milestones, such as submission of interim reports or issuance of final study reports. Document structure is highly standardized, adhering to GLP (Good Laboratory Practice) requirements. Documents include detailed experimental designs, methods, results, discussions, and conclusions. Common fields include animal ID, dosage (mg/kg), observation indicators (e.g., body weight in g, organ coefficient in %), detection time points (h or d), and various physiological and biochemical indicator values and units.

Constraints on Deployment and Upgrade

The standardization and specialized nature of preclinical safety assessment documents impose specific requirements on FastGPT deployment and upgrade. First, documents contain numerous specialized terms and abbreviations. The model needs strong domain understanding, possibly requiring custom glossaries or domain-specific model fine-tuning. Second, document updates are infrequent but may involve extensive data revisions and additions. The system requires efficient incremental update and version management mechanisms to ensure knowledge base timeliness and accuracy. Complex table and chart data in documents challenge file parsing capabilities, requiring optimized parsing strategies to correctly extract structured information. Finally, data sensitivity is high. The deployment environment must meet strict data security and compliance requirements, such as data isolation, access control, and audit logs, especially in multi-center collaborations or external agency reviews.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Preclinical safety assessment reports often contain many images and charts, leading to large file sizes.
`Chunk size` (Chunk Length)	`800 characters` (characters)	Ensures individual text blocks contain sufficient context while avoiding excessive length and information redundancy.
`maxContext`	`32000 tokens`	Addresses lengthy descriptions and multi-paragraph related information that may appear in complex reports.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Parsing large PDF or Word documents can be time-consuming, requiring a longer timeout.
`Similarity threshold` (Similarity Threshold)	`0.75`	Improves the precision of recall results, reducing interference from irrelevant information.
`Rerank result count` (Reranked Return Count)	`Top 5 entries` (top 5)	Focuses on the most relevant document segments, enhancing answer specificity.

Common Pitfalls

File upload parsing error, with a 413 Request Entity Too Large message. This may occur if the UPLOAD_FILE_MAX_SIZE configuration is too small to handle large safety assessment report files.
After upgrading FastGPT, the model fails to correctly identify specific biomarker names in preclinical safety assessment reports. This may occur if existing domain dictionaries or model fine-tuning configurations were not correctly migrated or reloaded during the upgrade.
When AIProxy is enabled, calls to a locally deployed Ollama model return null values. This may occur if the Ollama model's API interface is misconfigured or its response format does not match AIProxy expectations.

Verification Steps

Upload a typical preclinical safety assessment report (e.g., a PDF document with charts and tables). Check if file parsing is successful and if key fields are correctly extracted into the knowledge base.
Query using specialized terms from the report. Observe if the model accurately understands and recalls information from relevant document segments.
Check system logs to ensure no parsing and processing-related error messages, such as Timeout or Memory Exhausted, appear.
Verify the knowledge base indexing status through the FastGPT management interface. Confirm all safety assessment documents are successfully indexed and retrievable.

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.