Data Characteristics in this Domain
Surgical robot R&D documents originate from various sources. These include product design specifications, mechanical and electrical schematics, software code comments, clinical trial reports, risk assessment documents, and regulatory compliance files. Document update frequency is high, especially during R&D iteration cycles, with frequent new versions generated by design changes, software updates, and test results. Document structure includes extensive unstructured text descriptions, alongside tabular parameter lists, drawing annotations, and code snippets. Fields involve engineering parameters such as precision, torque, response time, and material strength, as well as clinical indicators like success rate and complication rates. Units cover both the International System of Units (SI) and industry-specific engineering units, such as millimeters, Newton-meters, milliseconds, and percentages.
Constraints Imposed by these Characteristics on Model Access and Configuration
The characteristics of surgical robot R&D documents impose specific requirements on model access and configuration. High update frequency means the model must support incremental training or rapid re-indexing mechanisms to ensure knowledge base timeliness. The variety of data types (text, tables, code, drawing annotations) in documents requires the model to have multimodal understanding capabilities, or at least effectively process tables and code segments during text RAG. The presence of numerous engineering parameters and clinical indicators makes precise recognition and extraction of numbers, units, and specific terminology critical. This requires models to sufficiently cover relevant domain knowledge during pre-training or fine-tuning. Furthermore, parsing regulatory compliance files demands high accuracy, traceability of citations, and logical reasoning capabilities, requiring the model to distinguish between factual statements and inferred conclusions. Therefore, configuration must focus on chunking strategies, embedding model selection, and recall optimization.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters (characters) | Balances semantic completeness with embedding efficiency. Avoids diluting key information in long chunks and losing context in short chunks. |
Chunk overlap (Chunk Overlap) | 100–200 characters (characters) | Ensures context information is not lost at chunk boundaries, improving retrieval recall. |
Recall count (Recall Count) | Top 5–8 entries (top 5–8) | Balances recall breadth with the computational cost of subsequent re-ranking, ensuring coverage of key information. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Prevents interference from low-relevance results while maintaining flexibility for semantic diversity. |
Rerank result count (Rerank Return Count) | Top 3 entries (top 3) | Focuses on the most relevant and accurate information, reducing the burden of processing irrelevant information for the model and improving response speed. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles complex parsing of large design documents and reports, preventing processing failures due to timeouts. |
Three Common Pitfalls
- Model returns inaccurate engineering parameter values or missing units: This often results from insufficient understanding of numbers and units during the embedding model's pre-training, or a chunking strategy that separates critical values from their units.
- Knowledge base retrieval results fail to reflect the latest information after document updates: This may occur if incremental indexing is not enabled, or if the index update frequency is set too low, failing to process new document versions in a timely manner.
- API calls return a 404 error: This typically indicates incorrect
baseURLorauthorizationconfiguration, failing to correctly point to the model service endpoint or provide valid authentication information.
How to Confirm Correct Configuration
- Select representative documents containing different data types (text, tables, code). Upload them through the knowledge base test interface and preview the chunking to confirm logical segmentation and complete key information.
- For specific engineering parameters and clinical indicators, construct queries containing these terms. Observe if the model's recall results include accurate values and units, and check their positions in the original text.
- Simulate a document update scenario. After uploading a new version of a document, use queries to verify if the knowledge base has updated to the latest information and can correctly retrieve new content.
- Check FastGPT backend logs to ensure no 4xx or 5xx status code errors occurred during model calls, and that response times are within an acceptable range.
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.