Database and Operations for Preclinical Safety Evaluation

Data Characteristics

Preclinical safety evaluation R&D document data originates from pharmacology and toxicology research reports, GLP (Good Laboratory Practice) raw laboratory records, special reports, pathology reports, and statistical analysis reports. These documents typically exist as PDFs, Word files, or scanned images. Content includes animal experiment designs, dosing regimens, observation indicators, pathology slide descriptions, toxicity reaction assessments, and biomarker data. Data update frequency is relatively low, with bulk generation occurring after each study phase. Document structure is highly complex, containing numerous tables, charts, text descriptions, and specialized terminology. Field types are diverse, including structured numerical values (e.g., dose, dosing frequency, body weight, blood indicators), semi-structured descriptive text (e.g., symptom observations, pathological diagnoses), and highly unstructured image information. Units strictly adhere to international pharmacopoeia or industry standards, such as mg/kg, g, ml, μmol/L, and ℃, often accompanied by abbreviations or special symbols.

Constraints on Database and Operations

The complex structure and diverse data types of preclinical safety evaluation documents require a database with robust unstructured data processing capabilities and flexible schema design. Numerous tables and charts necessitate efficient image recognition and table parsing technologies, converting them into queryable structured data. The prevalence of specialized terminology and abbreviations requires knowledge bases to support domain-specific dictionaries and ontologies, ensuring semantic understanding accuracy. Low data update frequency means optimizing bulk import and incremental update strategies to reduce resource consumption. Strict adherence to and diversity of units require data cleaning and standardization processes to accurately handle unit conversion and identification, avoiding data ambiguity. Furthermore, because data involves experimental animal ethics and drug safety, there are extremely high requirements for data integrity, traceability, and security, necessitating strict permission management and version control mechanisms.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Preclinical safety evaluation reports often contain many images and charts, resulting in large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Image recognition and table parsing for large PDF files or scanned documents can be time-consuming.
`maxContext`	`2000 characters`	Ensures capture of complete experimental descriptions or pathological diagnoses, preventing semantic breaks.
`Chunk size`	`800–1200 characters`	Balances context completeness and model processing efficiency, accommodating long descriptions in reports.
`Recall count`	`Top 10 entries`	Guarantees recall of sufficient experimental details and relevant data, improving retrieval accuracy.
`Similarity threshold`	`0.75`	The preclinical safety evaluation domain requires high precision in text matching, avoiding interference from irrelevant information.

Common Pitfalls

Knowledge base content updates do not display, and query results remain outdated. This occurs when knowledge base index rebuilding or cache refresh mechanisms are not correctly triggered, leading to data inconsistency between the database and search service.
Uploading large safety evaluation report files results in an HTTP 500 error or connection timeout. This happens when file parsing exceeds PARSE_FILE_TIMEOUT_SECONDS or the server's post_max_size limit.
After configuring a specific AI model, creating a new "Question Classification" node fails. This is due to incorrect model interface configuration or incompatibility between the selected model and FastGPT version v4.9.14, leading to model call failure.

Verification Steps

Upload a preclinical safety evaluation PDF report containing complex tables and charts. Verify that table content is correctly parsed into the knowledge base and can be structurally queried.
Through the FastGPT management interface, check that system parameters such as UPLOAD_FILE_MAX_SIZE and PARSE_FILE_TIMEOUT_SECONDS align with recommended values.
For newly imported data into the knowledge base, execute at least 5 test cases involving specialized terminology and numerical queries. Verify the accuracy and completeness of recall results.

The values given 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.