Data Characteristics for This Category
Imaging equipment data primarily originates from technical manuals, repair guides, operation instructions, and software update logs provided by equipment manufacturers. These documents typically have a low update frequency, occurring mainly when new equipment models are released, major software versions are updated, or significant defects are discovered. Document structures commonly include equipment model, serial number, technical parameters (e.g., imaging principles, resolution, scanning speed, radiation dose), software version, error codes, troubleshooting steps, calibration methods, and maintenance cycles. Data fields involve specific units like kV (kilovolt), mA (milliampere), s (second), mm (millimeter), Gy (Gray), and contain numerous equipment-specific terms and abbreviations. Document formats are diverse, with PDF, HTML, and XML files being common. Some data may be embedded in CAD drawings or specialized diagnostic software databases.
Constraints Imposed by These Characteristics on "Deployment and Upgrades"
The low update frequency of imaging equipment data means that after deployment, the knowledge base requires relatively infrequent incremental updates. However, each update might involve a large volume of documents, necessitating efficient file parsing capabilities. The specific units and specialized terminology in the documents require FastGPT's tokenizer and embedding model to accurately recognize these domain-specific terms, preventing recall failures due to misinterpretation. The presence of multiple document formats like PDF, HTML, and XML demands compatibility from the file parsing module. Structured or semi-structured information, such as equipment serial numbers and error codes, requires extraction through preprocessing or configuration of specific parsing rules to support precise queries. The deployment environment must consider storage and processing capabilities for large document files, as well as smooth migration of existing knowledge base indexes during upgrades to ensure service continuity.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Technical manuals and repair guides can be large, ensuring complete document uploads. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDFs or complex HTML documents requires significant file parsing time. |
Chunk size | 800–1200 characters | Retains sufficient context while preventing excessively long segments that reduce recall accuracy. |
Recall count | Top 5 entries | Imaging equipment issues often require precise matches; multiple recalls increase hit rate. |
Similarity threshold | 0.75 | High precision is needed for domain-specific terminology; a relatively high threshold reduces irrelevant results. |
Rerank result count | Top 3 entries | Re-ranking based on initial recall further improves the accuracy of the final results. |
Common Pitfalls
- During testing, a 403 status code with no response body appears. This typically results from improper access control policies or reverse proxy configurations in the deployment environment, leading to FastGPT API access being denied.
- After upgrading the version, calling the file upload API results in uploaded file data and indexes remaining incomplete for an extended period. This might be due to misconfigured new parsing service components or indexing services, or an excessively low file parsing timeout setting, causing a backlog in the backend processing queue.
- New model options are not found in the channel configuration. This usually indicates an incompatibility between the FastGPT version and the model provider's API version, or incorrect configuration of the model service provider's API key and endpoint.
How to Confirm Correct Configuration
- Upload imaging equipment technical documents in various formats (PDF, HTML, XML) and verify that they parse correctly and create indexes in the knowledge base.
- Use typical query statements containing equipment models, error codes, and technical parameters to confirm the system accurately recalls relevant document snippets.
- Simulate uploading multiple large files concurrently and observe the time taken for file processing queues and index creation, ensuring it remains within acceptable limits.
- After uploading files via the API, query the knowledge base status or file processing logs to confirm that file data and indexes have been processed.
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.