Data Characteristics
Surgical robot quality documentation primarily includes technical specifications, user manuals, maintenance guides, software validation reports from equipment manufacturers, and internal SOPs (Standard Operating Procedures) and calibration records from medical institutions. These documents have a low update frequency, typically released with new product models or major software upgrades. Document structures are complex, containing extensive specialized terminology, diagrams, flowcharts, and tables. Examples include instrument lists, fault code tables, and calibration parameter tables. Fields and units exhibit strong industry-specific characteristics, such as "force feedback accuracy (±0.05 N)," "joint repeatability (0.02 mm)," and "system latency (<50 ms)." Precise numerical values and unit recognition are critical. Some documents also include handwritten annotations or scanned images.
Constraints Imposed by These Characteristics on Document Parsing and Chunking
The complex structure and specialized nature of surgical robot quality documentation demand high parsing capabilities. Embedded diagrams and tables, especially those containing critical parameters and fault information, require specific handling to ensure data integrity. Standard text extraction may lead to the loss of key information. Low update frequency means the knowledge base will be stable once parsed, making initial parsing accuracy paramount. Specialized terminology and precise numerical units require the parser to correctly identify and retain this information, preventing inaccurate recall due to improper tokenization or unit loss. The presence of scanned images and handwritten annotations challenges OCR (Optical Character Recognition) accuracy and multilingual support, particularly when processing Chinese annotations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters | Ensures a single chunk contains a complete technical description or operating procedure, avoiding redundancy from excessive length. |
Chunk Overlap Length (Chunk Overlap Length) | 50–100 characters | Maintains contextual continuity, preventing critical information from being truncated at chunk boundaries. |
OCR_ENABLE | TRUE | Processes scanned documents and documents with text in images, ensuring all text is retrievable. |
OCR_LANGUAGE | chi_sim+eng | Covers both Chinese and English, common languages in technical documentation, especially for English terminology. |
TABLE_EXTRACTION_MODE | ROW_BASED | For tabular data, row-based extraction better preserves the structured information of tables, facilitating subsequent Q&A. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates large technical manuals or PDFs containing numerous diagrams, preventing parsing timeouts. |
Three Common Mistakes
- Some tabular data in the knowledge base cannot be effectively recalled after parsing. This occurs when the correct table extraction mode is not enabled or configured, leading to table content being treated as plain text or ignored.
- When processing documents with many scanned pages, the system prompts "text content is empty" or "too few characters identified." This usually means the OCR function is not enabled or the OCR engine's language pack is not correctly configured.
- When querying specific parameters, such as "force feedback accuracy," recall results lack relevant numerical values or units. This indicates the tokenizer failed to correctly identify specialized terminology and numerical units, resulting in imprecise indexing.
How to Confirm Correct Configuration
- Upload a PDF document containing complex tables and diagrams. Verify that table rows and column data are fully displayed in the knowledge base and confirm recall accuracy of table content through questioning.
- Upload a high-quality scanned document. Observe the text content in the knowledge base after parsing and check if all page text, especially any Chinese annotations, is correctly recognized.
- For specific technical terms and numerical values with units in the document, use search and questioning to verify precise matching and recall, and evaluate the contextual completeness of the recall results.
The values provided are common starting points and should be measured against the reader's 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.