Data Characteristics
Preclinical safety evaluation data originates from pharmacology and toxicology research reports, GLP (Good Laboratory Practice) raw laboratory records, internal research reports, and regulatory submission documents. These documents have a low update frequency, typically at project milestones like project initiation, interim summaries, and pre-submission. Document structures are complex, containing large amounts of unstructured text, tabular data, charts, and appendices. Fields and units are highly specialized, for example: "dosing (mg/kg)," "route of administration (po, iv, ip)," "observation indicators (body weight, organ coefficient)," and "pathological findings (histopathological description)." These fields often appear as free text within reports, and units are sometimes implicitly included in descriptions.
Constraints Imposed on Deployment and Upgrade
The low update frequency of preclinical safety evaluation documents means that model training and knowledge base indexing do not require continuous high-frequency updates. Batch processing or periodic update strategies are suitable. The complex document structure requires FastGPT to have robust multimodal parsing capabilities, particularly for extracting content from tables and charts. Highly specialized fields and units necessitate strict entity recognition and standardization during data preprocessing to ensure accurate structured output. The large volume of free text and specialized terminology challenges the domain adaptability of embedding models and retrieval algorithms, requiring optimization for the biomedical field. Deployment must consider private environments to meet data security and compliance requirements. During upgrades, workflow and knowledge base migration compatibility is crucial; old workflow configurations and knowledge base data need to be smoothly imported into the new version.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Safety evaluation reports are often large, containing many images and charts. This ensures sufficient upload capacity. |
Chunk size (Segment Length) | 800–1200 characters | Accommodates long descriptions and complex logic in safety evaluation documents, maintaining contextual coherence. |
Similarity threshold (Similarity Threshold) | 0.75 | Preclinical safety evaluation terminology is precise. A higher threshold reduces irrelevant retrievals, improving accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDF document parsing is time-consuming. Extending the timeout prevents failures due to parsing interruptions. |
Recall count (Retrieval Count) | Top 8 | Ensures coverage of multi-dimensional information in safety evaluation reports while controlling context window size. |
embeddingModel | text-embedding-ada-002 | Suitable for specialized domain texts, ensuring semantic understanding capabilities. |
Common Pitfalls
- Document parsing failure or content loss: Parsing results may show incomplete file content or empty specific sections. This can be due to abnormal PDF structures, complex nested tables, or images, causing the parser to time out or fail to recognize content correctly.
- Knowledge base indexing prolonged stagnation: The knowledge base status displays "Indexing" and remains unchanged for a long time. This can be caused by excessively large individual documents or too many concurrent parsing tasks exceeding system resource limits.
- Workflow malfunction after upgrade: After importing old workflows into a new version, nodes may report errors or produce abnormal output. This can be due to incompatibilities in workflow configuration items or node logic between new and old versions.
Verification Steps
- Upload and parse a typical preclinical safety evaluation report. Check that the extracted text, tables, and image descriptions are complete and accurate, especially for key dosage, indicator, and conclusion fields.
- Use FastGPT's search function to retrieve information using specialized terms from safety evaluation reports. Verify that the relevance and accuracy of retrieval results meet expectations, confirming the reasonableness of the similarity threshold.
- Simulate a preclinical safety evaluation report update scenario. Test the knowledge base incremental update function to confirm that updated information is correctly indexed and queryable.
- After deployment, test the stability of API interfaces, especially for parsing and querying large documents. Observe response times and success rates to ensure normal operation under high load.
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.