Data Characteristics for This Category
Surgical robot product data comes from various sources. These include product manuals, operation guides, maintenance handbooks, clinical reports, and software update logs. Documents are typically in PDF, Word, or structured XML formats. Data updates occur at a stable frequency, usually quarterly or semi-annually, coinciding with new product releases, software iterations, or clinical data additions. Document structures often contain extensive technical terms, parameters, safety regulations, and operational procedures. Fields like precision error range, surgical indications, consumable batch number, and maintenance cycle are common. Units frequently include millimeters (mm), degrees (°), Newtons (N), and hours (h). Accurate data parsing is critical.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The highly specialized and structured nature of surgical robot product data imposes specific constraints on knowledge base construction during deployment. Documents often contain numerous charts and complex layouts, requiring robust PDF and rich-text processing capabilities from the document parser to accurately extract key information. Update frequency is stable, but each update may involve significant document revisions, making efficient incremental update mechanisms essential. Precise recognition of technical terms and parameters demands strong domain adaptation from the model, potentially requiring customized embedding models. Furthermore, accurate identification of fields such as consumable batch number and maintenance cycle directly impacts subsequent consultation accuracy. Therefore, data cleaning and entity extraction configurations must be exceptionally precise to ensure FastGPT accurately understands and responds to product and reagent inquiries.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Surgical robot documents often contain many images and charts, leading to large individual file sizes. |
Chunk size (Chunk Length) | 800 characters | Ensures complete technical parameters or operational step descriptions are preserved during document segmentation. |
Recall count (Recall Count) | 10 | Increases recall coverage to address multiple relevant technical details that may be involved in user queries. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures recalled results are highly relevant to the query intent, reducing interference from inaccurate information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF files can take a long time; this avoids parsing failures due to timeouts. |
Rerank result count (Reranked Return Count) | 5 | Improves the precision of the final displayed results through reranking, building on a high recall rate. |
Common Pitfalls
- Key fields are empty or missing after document import. This often occurs because the document parser fails to correctly recognize complex table structures or the context of specialized terminology.
- When a user queries for "consumable batch number," the system returns irrelevant content. This indicates insufficient entity recognition capability in the embedding model or a lack of effective indexing for such information in the knowledge base.
- Some historical configuration parameters become invalid after a FastGPT upgrade, leading to service abnormalities. This typically results from changes in configuration item names or expected values during version iterations, and a failure to update them promptly causes compatibility issues.
Verification Steps
- Upload a PDF product manual containing complex tables and diagrams. Check if the parsed segments are complete and free of garbled text.
- Enter multiple queries containing specialized fields like
precision error rangeandmaintenance cycle. Verify that the returned results accurately mention the corresponding values and units from the document. - Simulate an incremental update for new product documentation. Observe if the knowledge base responds correctly to queries for both new and old product information after the update.
Note: The values provided are common starting points. Measure performance against your own samples to determine optimal settings.
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.