Data Characteristics for This Category
Data sources for high-value consumable registration documents include product technical requirements, inspection reports, clinical evaluation reports, instructions for use, labels, and manufacturing process documents. These documents are typically in PDF, Word, or scanned image formats, with varying degrees of structural organization. Core technical parameters and registration certificate information remain relatively stable. However, content such as instruction revisions or clinical evaluation updates may require irregular updates due to regulatory changes, product iterations, or supplementary clinical data. Update frequency ranges from several months to several years. Document content often involves specialized terminology, biocompatibility indicators, mechanical performance parameters, sterilization methods, and shelf life. Precision in units of measurement (e.g., millimeters, milligrams, unit dose, action time) is critical.
Constraints on Deployment and Upgrade from These Characteristics
The complex document structure and specialized terminology of high-value consumable data demand high recall accuracy for text segmentation and embedding models. The uncertain update frequency requires the system to support flexible incremental knowledge base updates, avoiding resource consumption and time costs associated with full rebuilds. The presence of numerous images and scanned documents necessitates integrating efficient OCR capabilities during FastGPT deployment to ensure effective extraction of unstructured information. Strict compliance requirements make data accuracy central. Therefore, post-deployment knowledge base validation mechanisms and version management features are essential to ensure traceability and correctness of generated content. Optimizing data processing workflows is necessary for identifying units and key fields.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Ensures handling of declaration documents containing numerous charts and scanned images. |
Chunk size (Segment Length) | 800–1200 characters | Balances semantic integrity of specialized high-value consumable text with recall efficiency. |
Recall count (Number of Recalls) | Top 8 entries (Top 8) | Increases recall scope to cover more relevant technical details and regulatory clauses. |
Similarity threshold (Similarity Threshold) | 0.75 | Reduces false recall rates and improves the accuracy and relevance of answers. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (600 seconds) | Addresses parsing time for large PDF documents or complex Word documents. |
maxContext | 4096 tokens | Ensures the model can accommodate sufficient context to understand specialized terminology and related information. |
Three Common Mistakes
- After uploading files for knowledge base training, the data processing step remains empty for an extended period. This usually occurs because the
PARSE_FILE_TIMEOUT_SECONDSconfiguration is too low, causing the system to time out when processing large or complex documents, leading to parsing failure. - After local deployment, the chat function works normally, but knowledge base query results lack professionalism or contain unit errors. This may be because the text segmentation strategy failed to effectively retain critical terminology, parameters, and unit information from high-value consumable data.
- After migrating an older FastGPT version to a new environment, some documents cannot be indexed correctly or have missing content. This may stem from incorrect handling of storage volume mounting or incomplete data synchronization during database migration, leading to loss of knowledge base files or invalid paths.
How to Confirm Correct Configuration
- Upload a high-value consumable technical requirements PDF containing multi-page tables and images. Check if the knowledge base segment preview accurately extracts and displays table content and image captions, ensuring OCR functionality is correct.
- Ask questions about product names and key performance indicators (with units). Verify if the model can recall precise numerical values and unit information from the knowledge base and compare it with the original document to ensure information consistency.
- Simulate a regulatory update by uploading a revised instruction manual. After an incremental update, ask questions about the revised content. Confirm that the system can identify and prioritize the latest version of information, verifying the effectiveness of the update mechanism.
- Check system logs to ensure no
PARSE_FILE_TIMEOUTorDOCUMENT_PROCESS_ERRORmessages related to file parsing and knowledge base construction appear.
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.