Knowledge Base Retrieval and Recall for Home Medical Device

Data Characteristics for this Category

R&D document data for home medical devices primarily comes from product design specifications, test reports, clinical validation data, user feedback records, and regulatory compliance files. This data has a relatively low update frequency, typically aligning with product iteration cycles. Document structures commonly use chapter-based Word or PDF formats, containing numerous charts, measurement data, and technical parameters. Fields often involve specialized terminology such as biocompatibility, electrical safety, EMC (electromagnetic compatibility), and mechanical performance. Units strictly follow international standards like millimeters (mm), volts (V), amperes (A), hertz (Hz), and pascals (Pa), demanding high precision. These documents frequently include extensive references to standard clauses and traceability requirements.

Constraints on "Knowledge Base Retrieval and Recall" from these Characteristics

The low update frequency of home medical device R&D documents means knowledge base construction must prioritize historical version management to ensure retrieval accuracy and traceability. Complex charts and specialized terminology in documents challenge traditional text chunking, requiring more intelligent parsing strategies to identify and extract key information. Strict unit and precision requirements mean simple keyword matching may not capture semantic associations during retrieval. Numerical ranges and unit conversions need consideration. Furthermore, the strong correlation of regulatory compliance files requires the knowledge base to effectively link associated standard clauses during recall, preventing isolated information. If internal URL links within documents are not correctly crawled and parsed, knowledge base integrity is affected.

Configuration Settings

Configuration Item	Recommended Value	Rationale for this Value
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	R&D documents often contain many images and charts, resulting in large file sizes. Sufficient upload space is necessary.
`Chunk size` (Chunk Length)	`800–1200 characters` (characters)	Ensures each chunk contains enough contextual information while preventing individual chunks from becoming too long and semantically dispersed.
`Recall count` (Recall Count)	`15 entries` (items)	Considering the complexity and interconnectedness of professional documents, increasing the recall count improves the coverage of relevant information.
`Similarity threshold` (Similarity Threshold)	`0.75`	For specialized terminology and precise numerical values, a higher similarity threshold helps filter for more accurate results.
`Rerank result count` (Reranked Return Count)	`5 entries` (items)	After a higher recall count, reranking selects the most relevant few results for users to prioritize.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Processing large PDFs or Word documents with complex structures can be time-consuming; extending the timeout is necessary.

Three Common Mistakes

Symptom: When previewing chunks, some PDF documents display "Unable to read file content." Reason: This usually occurs because the PDF file is encrypted, corrupted, or uses a non-standard encoding, preventing the FastGPT internal parser from processing it correctly.
Symptom: After configuring the knowledge base, retrieval results lack critical technical parameters or unit information. Reason: Document parsing failed to effectively identify text in charts, table data, or did not standardize the extraction of numbers and units, leading to information loss during vectorization.
Symptom: After upgrading FastGPT, creating a knowledge base using a URL consistently reports cannot fetch internal url. Reason: New versions may have adjusted URL crawling strategies or security mechanisms, potentially causing failures when fetching certain internal or specific format URLs. Network configuration or URL format needs checking.

How to Confirm Proper Configuration

Select a batch of representative R&D documents (including charts, tables, and specialized terminology). Upload them to the knowledge base and individually check if chunk previews are normal and content is complete.
Perform precise searches for key technical parameters within documents (e.g., "maximum output power 15W," "withstand voltage 250V"). Check if recall results include this information and its context.
Simulate a regulatory compliance query scenario. Enter a standard clause number and verify if recall results effectively link to relevant test reports or design specifications.
Check log files to confirm no significant PARSE_FILE_TIMEOUT or Cannot fetch internal url errors occurred during file parsing, ensuring document processing stability.

Note: The values provided are common starting points. Measure them against specific samples to determine optimal settings.

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.