Data Characteristics
High-value consumable registration and declaration data comes from various sources. These include product technical requirements, registration inspection reports, clinical evaluation reports, instruction manuals, and label samples. Documents typically exist in multiple formats like PDF, Word, and Excel, with varying degrees of content structure. Data update frequency correlates with product lifecycles and regulatory requirements. For example, product technical requirements may revise annually due to standard updates, and clinical evaluation data might supplement post-market re-evaluation. Fields and units are highly specialized, covering material parameters (e.g., tensile strength MPa), biological indicators (e.g., cytotoxicity grade), and physical dimensions (e.g., catheter diameter mm). Different countries or regions may use different expressions and units for the same indicator.
Constraints on HTTP Interfaces and External Systems
The multi-source and heterogeneous nature of high-value consumable declaration data requires robust file type parsing capabilities for HTTP interfaces. Accurate extraction of content from unstructured documents like PDFs and Word files is fundamental for subsequent knowledge base construction. The periodicity and regulatory dependency of data updates mean external systems must support scheduled or event-triggered interface calls to ensure knowledge base timeliness. Differences in specialized fields and units necessitate standardized processing of interface return data. This includes uniform unit conversion from different sources or semantic parsing of specific fields. Furthermore, due to the sensitive nature of declaration data, interface security authentication and permission control are essential considerations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000–4000 characters | Ensures accommodation of longer paragraphs in high-value consumable declaration documents, preventing critical information truncation. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | High-value consumable declaration files are often large, requiring sufficient parsing time. |
embeddingModel | text-embedding-ada-002 | This model performs well in medical text comprehension, effectively capturing the semantics of professional terminology. |
Chunk size (Segment Length) | 800–1000 characters | Balances semantic completeness and recall efficiency, adapting to the detailed descriptions typical in declaration documents. |
Recall count (Recall Count) | top 8 | Increases recall scope to cover potentially scattered key information points in high-value consumable declarations. |
externalApiUrl | Set according to the actual external data source API address, e.g., https://api.example.com/v1/medical_devices | The actual address of the external system interface, used to obtain the latest regulatory or product data. |
Common Pitfalls
- Symptom: HTTP interface calls frequently result in
504 Gateway Timeouterrors. Reason: Individual high-value consumable declaration files are too large, or external system processing is complex, causing the interface response time to exceed the gateway's default limit. - Symptom: Knowledge base retrieval results show confusion or inconsistency in professional terminology units. Reason: Data returned by the external system was not standardized for units, and the knowledge base directly ingested fields with different units.
- Symptom: Knowledge base document content in FastGPT is empty or incomplete after parsing. Reason: The file parser failed to correctly process certain specific PDF or Word document formats, such as those containing complex tables, images, or embedded special fonts.
Verification Steps
- Upload typical high-value consumable declaration files via the FastGPT management interface. Check if the parsed document content is complete and free of garbled characters.
- Call the configured HTTP interface. Check if the returned JSON data structure meets expectations and verify that units for specific professional fields are unified.
- Conduct question-answering tests in FastGPT against the knowledge base. Ask questions related to high-value consumable technical parameters and regulatory requirements. Evaluate the accuracy and comprehensiveness of the answers, comparing them with original documents.
- Check FastGPT system logs. Confirm that HTTP interface calls have no errors and that data synchronization tasks execute at the expected frequency.
The values provided are common starting points. Measure them against your 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.