Database and Operations for Structured Parsing of Surgical

Data Characteristics

Data generated during surgical robotics R&D primarily comes from design documents, experiment reports, test records, preclinical study data, and regulatory compliance files. This data typically exists as unstructured text (e.g., Word, PDF), semi-structured tables (e.g., Excel, CSV), and some structured data (e.g., CAD model parameters, sensor readings). Data updates frequently, especially during design iterations and testing phases, with new versions potentially generated weekly or even daily. Document structures often include sections like system architecture, module functions, bill of materials, and interface definitions in design documents; experiment objectives, methods, results, analysis, and conclusions in experiment reports; and test cases, steps, expected results, actual results, and defect descriptions in test records. Field and unit specificities reflect the rigor of the medical device domain. For example, precision units are often micrometers (μm), force units are Newtons (N), and angle units are degrees (°) or radians (rad), often accompanied by tolerance ranges and measurement uncertainties.

Constraints on Database and Operations

The unstructured and semi-structured nature of surgical robotics R&D documentation requires robust text retrieval and parsing capabilities from the database. High-frequency data updates mean the database needs to support efficient incremental indexing and version management to ensure information timeliness and traceability. The large number of charts and images in documents places higher demands on file storage and preprocessing, requiring the ability to identify and extract key data or text from these visuals. The strictness of units like precision and tolerance, along with domain-specific vocabulary (e.g., endo-wrist, laparoscopic), means traditional keyword matching is insufficient. Advanced semantic understanding capabilities are necessary to ensure recall accuracy. Furthermore, medical device compliance requires strict access control and audit logs for all R&D data, ensuring data security and integrity. Therefore, database selection and operational strategies must balance data processing capability, update efficiency, security, and compliance.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	R&D documents often contain many images and charts; individual files can be large. This prevents upload failures.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing complex PDF and Word documents can be time-consuming. This provides sufficient time to prevent timeouts and ensure complete content extraction.
`Chunk size` (Chunk Size)	`800–1200 characters`	Balances semantic completeness and recall efficiency. Avoids context loss from chunks that are too short and imprecise recall from chunks that are too long.
`Recall count` (Recall Count)	`Top 10–15 entries`	Ensures coverage of multiple highly relevant information points, providing enough candidates for subsequent re-ranking and improving R&D personnel's information acquisition efficiency.
`Similarity threshold` (Similarity Threshold)	`0.75` (Cosine Similarity)	Medical device R&D demands high precision. A higher threshold filters out less relevant or generic results, focusing on core technical details.
`Rerank result count` (Re-rank Return Count)	`Top 5 entries`	After re-ranking, presenting a small number of the most relevant items directly to R&D personnel avoids information overload and improves decision-making efficiency.

Common Pitfalls

Tool call database connection error tool_calls or message: 400: This typically results from incorrectly referenced variables or type mismatches in SQL queries, preventing the backend database from parsing a valid query.
Local FastGPT tool call to database connection yields no output: Common causes include network configuration issues where the FastGPT container cannot access the target database instance, or incorrect host, port, username, password configurations in the database connection string.
Database query plugin execution fails, but manual SQL input succeeds: This often happens when FastGPT does not adequately escape or validate user input or model-generated content when constructing dynamic SQL, leading to SQL injection risks or syntax errors.

Verification Steps

Upload a typical surgical robotics R&D document with complex charts and long text (e.g., Design Verification Report.pdf). Confirm the file is fully parsed and key information fields (e.g., precision metrics, material model) are correctly extracted.
Execute cross-document query tasks, such as "Find all actuator models mentioned in Product Design Specification V2.0 and validated in Preclinical Test Report." Verify the system accurately recalls relevant document snippets.
Simulate high-concurrency query scenarios. Observe database and FastGPT service response times to ensure no significant delays occur during daily R&D team use. Set a query concurrency level and monitor the response_time metric.

Note: 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.