Deployment and Upgrade for Home Medical Quality Documents

Data Characteristics

Home medical device quality document data originates from internal departments (R&D, production, quality management, regulatory affairs) and external sources (suppliers, regulatory bodies). These documents include design inputs/outputs, risk management reports, test verification reports, production process control records, non-conformance handling records, complaint and adverse event reports, product specifications, user manuals, registration certificates, and GSP/GMP compliance files. Update frequency depends on product lifecycle, regulatory changes, and market feedback. Updates are typically concentrated during product design iterations, production process changes, annual audits, or quality incidents, with fewer ongoing daily updates. Document structures are often standardized reports, records, checklists, or manuals. They contain significant structured or semi-structured data, such as model numbers, batch numbers, serial numbers, test parameters, acceptance criteria, deviation descriptions, and corrective actions. Fields and units are highly specialized and consistent, for example, mm, mg/dL, ℃, kPa. Many fields require precision to several decimal places.

Deployment and Upgrade Constraints

The specialized and standardized nature of home medical device quality documents requires FastGPT to have robust structured information extraction capabilities during data ingestion. This ensures accurate identification and association of key fields across various documents. The concentrated and periodic update pattern means the system must support bulk imports and incremental updates, while maintaining data consistency and version traceability. For example, when product design changes lead to updates in multiple related documents, the system needs to efficiently identify and synchronize updates to affected knowledge blocks. Strict field and unit requirements in documents demand high precision from the model's understanding and generation, avoiding unit confusion or misinterpretation of numerical values. Furthermore, regulatory compliance requires the knowledge base to be highly reliable and auditable, with strict requirements for deployment environment security and data isolation. For instances deployed on-premises or in private cloud environments, such as ollama running large models, stable connectivity with FastGPT services must be ensured. Request Time timeout issues must be handled to guarantee real-time document parsing and knowledge retrieval.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Considers that a single quality report or batch production record may contain numerous images and charts, leading to large file sizes.
`Chunk size` (Chunk Length)	`800–1200 characters` (characters)	Ensures that complex technical descriptions and specialized terminology in quality documents maintain contextual integrity, preventing semantic fragmentation.
`Similarity threshold` (Similarity Threshold)	`0.85`	Improves the precision of knowledge retrieval, reducing false recalls of irrelevant or low-similarity technical specifications.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Handles parsing large PDFs or documents containing scanned images, preventing timeout errors due to extended parsing times.
`maxContext`	`4096`	Accommodates more contextual information to address complex queries involving cross-references to multiple relevant quality standards or test results.
`Rerank result count` (Reranked Results Count)	`Top 5 entries` (top 5)	Prioritizes the most relevant key quality control points or regulatory clauses for the query, improving engineer search efficiency.

Common Pitfalls

Frequent Request Time errors or response delays during conversations: This usually indicates high network latency or insufficient model inference resources between the locally deployed ollama large model and the FastGPT service.
Missing or incorrectly parsed field values in retrieval results: This often occurs when document structure changes or custom parsing rules are not updated in time, leading to specific formats of model numbers, batch numbers, or test results not being extracted correctly.
Inability to call function call or tool usage failure: This is common when the LLM service configured in one-api or ollama does not have function call capabilities correctly enabled, or when the proxy tool is incompatible with the FastGPT version.

Verification

Upload various types of home medical quality documents (PDF, DOCX, XLSX). Check if the parsed knowledge blocks are complete and if key fields like product model, batch number, and test parameters are correctly identified.
For complex queries, such as "Provide all risk assessment items regarding electrical safety from the risk management report for model XYZ-001 batch 20240101," verify that the retrieval results are accurate and contain complete information.
Simulate a function call, for example, querying the non-conformance handling record for a specific batch. Confirm that the function call is triggered and returns the expected data format.
Monitor system logs to ensure that the frequency of PARSE_FILE_TIMEOUT_SECONDS and Request Time-related error logs during daily operation is below an acceptable threshold.

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.