Data Characteristics for This Category
Supplier audit data in the biomedical industry typically originates from supplier qualification documents, production records, quality management system files, on-site audit reports, and historical collaboration evaluations. The update frequency for this data varies. Qualification documents may update annually, while production records generate in real-time per batch. Audit reports are usually periodic or ad hoc. Document structures are complex. They include PDF contracts and scanned images, Word quality manuals, and Excel production batch data. Fields and units are highly specialized. Examples include "batch number," "production date," "expiration date," "inspection report number," and "GMP certification status." Units involved include dates, batch numbers, percentages, temperature units (Celsius or Fahrenheit), and pressure units (Pascals). Mixed Chinese and English terms or abbreviations are common.
Constraints on Deployment and Upgrade from These Characteristics
Diverse sources and complex formats of supplier audit data require FastGPT deployments to have robust file parsing capabilities. This ensures accurate extraction of critical information from scanned PDFs, Word documents, and Excel spreadsheets. The asynchronous nature of data updates necessitates flexible data synchronization strategies in the deployment solution. For instance, core qualification documents may undergo periodic full updates, while production batch data uses incremental or event-triggered updates. Specialized fields, units, and mixed Chinese-English terms demand higher model comprehension from FastGPT. Deployments must ensure the model is adequately trained or fine-tuned for biomedical domain-specific terminology. Furthermore, audit data often contains sensitive information. Deployment and upgrade processes must strictly adhere to data security and compliance requirements, including stringent access control and data encryption.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 100 MB | Audit reports and qualification documents often contain many images and scanned files, leading to large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing complex scanned PDFs and large Excel files can take considerable time. |
Chunk size | 800–1200 characters | Ensures context integrity while accommodating the paragraph structure of audit documents. |
Recall count | Top 8 entries | Increases the probability of recalling critical information from multiple relevant documents in complex audit scenarios. |
Similarity threshold | 0.75 | Reduces the risk of recalling irrelevant or weakly relevant information, improving result accuracy. |
LLM_MODEL_NAME | gpt-4o | Handles specialized terminology and complex logic to produce high-quality consultation results. |
Common Pitfalls
- The system reports "username or password error" and login fails. This occurs when the database connection string or administrator account information is not correctly configured after deployment.
- After uploading an audit report, the system displays "text content extraction invalid." This happens when FastGPT's file parsing service fails to correctly identify or process specific scanned PDF formats, often due to missing OCR plugins.
- FastGPT is deployed on two computers. One can use a password-free link, but the other cannot access it. This indicates network configuration or firewall policy restrictions on external access, or the
FASTGPT_URLenvironment variable is not correctly set to a publicly accessible address.
Verification Steps
- Upload a complex PDF audit report containing multiple tables and scanned images. Check if the text content is extracted completely and accurately.
- Query using specific biomedical terminology. Evaluate the AI platform's understanding of specialized vocabulary and the accuracy of its responses.
- Attempt to access the deployed FastGPT instance via a password-free link in different network environments. Confirm access permissions and stability.
- Check system logs to confirm that the file parsing service does not show timeout or error messages.
The values provided are common starting points and should be measured against specific 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.