Model Integration and Configuration for IVD Diagnostic

IVD Data Characteristics

IVD diagnostic reagent registration documents come from diverse sources. These include clinical trial reports, performance verification reports, product technical requirements, instructions for use, labels, and risk management reports. Data updates are infrequent, typically occurring during product development, registration changes, or regulatory policy adjustments. Document structures are rigorous, often in PDF or Word formats, containing structured text, numerous tables, charts, and specialized terminology. Fields involve batch numbers, expiration dates, detection principles, intended uses, main components, and storage conditions. Units cover concentrations (e.g., g/L, mmol/L), temperature (°C), time (min, h), and ratios, requiring high precision.

Constraints on Model Integration and Configuration from Data Characteristics

Infrequent IVD data updates mean model training or knowledge base construction does not require frequent refreshing. However, initial data cleaning and annotation accuracy are critical. The rigorous document structure, including tables and charts, demands strong document parsing capabilities from the model, especially for extracting and understanding table content. Specialized terminology and precise field units place high demands on the model's semantic understanding and entity recognition. This requires targeted vocabulary expansion and domain adaptation. Due to potentially large data volumes and sensitive information, local model deployment or selecting cloud models with advanced data security is necessary. Incorrect data extraction or understanding can lead to compliance issues in registration documents. Therefore, the model's ability to handle details is key.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000–16000 tokens`	IVD declaration document sections are long; the model needs to process sufficient context at once to minimize information loss.
`Chunk size` (Segment Length)	`500–800 characters` (characters)	Ensures each text block contains enough semantic information while avoiding excessive length that reduces model processing efficiency.
`Recall count` (Recall Count)	`10–15 entries` (items)	Declaration documents have strong interconnections; increasing recall helps cover more potentially relevant information.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adjust the threshold based on actual recall performance to balance recall rate and accuracy, avoiding interference from irrelevant information.
`Rerank result count` (Rerank Return Count)	`5–8 entries` (items)	Precisely filters out the most relevant few pieces of information, improving the usability of the final result.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	IVD declaration files can be large, and parsing can take a long time, requiring ample processing time.

Common Pitfalls

Model testing errors display API Key invalid or Ollama connection refused. This usually indicates an incorrect API key configuration, or the local Ollama service is not running correctly or its port is occupied.
After the business system integrates with the FastGPT API, chat records for different users become mixed. This occurs when the business system does not correctly pass or differentiate user IDs during API calls, preventing FastGPT from isolating different session contexts.
The model errors when processing tasks involving file parsing, while regular chat functions work correctly. This may be due to the file parsing module (e.g., image understanding model) not being correctly installed or configured, or parsing failure due to an unsupported file format.

Configuration Verification

Upload a typical IVD diagnostic reagent PDF document. Check the knowledge base segmentation results to confirm that text and table content are accurately extracted and semantically complete.
Ask key questions related to IVD declaration documents. Observe whether the model's answers accurately cite the original document and whether the cited content is highly relevant to the question.
Simulate concurrent access by multiple users. Verify that different user session contexts are independent and not interfered with, ensuring data isolation.
Use documents containing images or complex tables for questions. Confirm that the model can correctly parse and understand the information within them, without parsing errors or information omissions.

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.