Rehabilitation Equipment Product Deployment and Upgrades

Data Characteristics for This Category

Rehabilitation equipment data sources primarily consist of product manuals, technical handbooks, user guides, maintenance manuals, and clinical case studies provided by manufacturers. These documents often come in PDF, Word, or scanned image formats. They contain equipment models, functional parameters, operating procedures, troubleshooting information, compatibility details, and consumable lists. Data update frequency is relatively low, typically coinciding with product iterations or regulatory updates. Document structures often present technical parameters in tables, and operating steps are frequently illustrated with flowcharts or step-by-step instructions. Some critical parameters include specific units, such as power (W), frequency (Hz), pressure (kPa), or dimensions (mm).

Constraints Imposed by These Characteristics on "Deployment and Upgrades"

The characteristics of rehabilitation equipment documentation impose specific requirements on FastGPT's deployment and upgrades. The prevalence of tables and mixed text-image content in documents demands high-accuracy recognition from the text extraction module, especially for parsing structured table data. The low update frequency means initial knowledge base construction might involve a large volume of historical documents, but subsequent incremental updates will be fewer. This necessitates attention to version management and differential update mechanisms. Diverse file formats and complex document structures make configuring parameters like PARSE_FILE_TIMEOUT_SECONDS and UPLOAD_FILE_MAX_SIZE particularly critical to prevent parsing failures or timeouts. Precise unit and numerical information requires a chunking strategy that effectively retains context, avoiding truncation during chunking that could affect recall accuracy.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for This Value
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Rehabilitation equipment documents are often large, containing numerous images and charts. Increasing the file upload limit prevents upload failures due to oversized files.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex PDFs and scanned documents requires longer parsing times to avoid timeout errors.
`Chunk size` (Chunk Length)	`800–1200 characters` (characters)	Retains the complete context of technical parameters, operating steps, and other information, preventing critical data from being truncated.
`Overlap Length`	`100–150 characters` (characters)	Ensures contextual continuity at chunk boundaries, improving recall coherence.
`Recall count` (Recall Count)	`Top 5 entries` (top 5)	Rehabilitation equipment inquiries often require precise technical details. Appropriately increasing the recall count can improve relevance.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures the precision of recalled content, filtering out irrelevant general information.

Three Common Mistakes

After uploading documents, some table data might not be chunked correctly or content might be missing. This can occur if the text extraction module has insufficient support for complex table structures or if PARSE_FILE_TIMEOUT_SECONDS is set too short, causing parsing to be incomplete.
The FastGPT container fails to start, with logs showing Reached the max retries. This is typically due to incorrect database connection parameter configuration, such as ZILLIZ_URL or MILVUS_HOST pointing to an unreachable or unauthorized address.
After an upgrade, queries for parameters of certain specific equipment models return empty results. This might happen if the new version has adjusted document parsing or indexing logic, leading to invalid indexes for older documents or some fields not being extracted correctly.

How to Verify Configuration

Upload a rehabilitation equipment technical manual containing complex tables and mixed text-image content. Check if the chunked content of this document in the knowledge base is complete, especially whether table data is correctly identified and chunked.
For multiple equipment models, use query statements that include specific parameters or operating steps. Verify that the recall results are accurate and highly relevant, and check if the returned content includes precise numerical values and units.
Simulate a user consultation process by asking questions about troubleshooting or maintenance. Confirm that FastGPT can provide reasonable suggestions or solutions based on the knowledge base content.

Note: The values provided are common starting points. Measure them against your 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.