Data Characteristics
Rehabilitation equipment data comes from various sources. These include product manuals, technical handbooks, maintenance guides, clinical application cases, and regulatory compliance documents provided by manufacturers. Documents are primarily in PDF format, with some containing scanned images. Data update frequency is relatively low. Updates occur centrally during new product releases or technical upgrades. Individual products show little change within their lifecycle. Document structures, such as product manuals, often include fixed sections like model number, technical parameters, functional descriptions, operating procedures, precautions, and maintenance. Fields and units involve physical quantities like power (W), voltage (V), frequency (Hz), dimensions (cm), weight (kg), and load capacity (kg). Descriptive fields include treatment modes, target populations, and contraindications.
Constraints on Knowledge Base Retrieval and Recall
Scanned image content in rehabilitation equipment documents challenges the knowledge base's text extraction capabilities. This can lead to critical parameters and descriptive information not being effectively indexed. Documents contain extensive technical parameters and specialized terminology. Tokenization strategies must accurately identify and preserve the integrity of these proper nouns. Retrieval accuracy for unique identifiers like product models and serial numbers is crucial. Exact matching is necessary. Due to infrequent updates, an efficient incremental update mechanism is required to handle small numbers of new or revised documents after initial knowledge base construction. Additionally, functional similarities between different products are high. Retrieval must differentiate subtle technical differences to avoid generalized recall.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Paragraphs in rehabilitation equipment documents are typically long, containing complete technical descriptions or operating steps. This length helps preserve context. |
Chunk Overlap Length (Segment Overlap Length) | 100 characters | Ensures sufficient overlap between adjacent segments. This covers critical information spanning paragraphs, especially in parameter lists or step descriptions. |
Recall count (Recall Count) | Top 8–12 items | Rehabilitation equipment inquiries often involve multiple related technical points. Increasing the recall count helps cover more comprehensive information and improves hit rates. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | For technical documents, a higher similarity threshold is needed to ensure the precision of recall results and avoid interference from irrelevant information. |
Rerank result count (Rerank Return Count) | Top 5 items | After reranking, the most relevant few items are prioritized for display. This allows users to quickly access core information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Rehabilitation equipment manuals may contain many images or complex layouts. Extending the parsing timeout ensures complete document processing. |
Common Pitfalls
- Symptom: Retrieval results show many irrelevant or low-relevance product details. Reason: Tokenization strategies failed to effectively identify specialized terminology, or the similarity threshold was set too low, leading to overly broad recall.
- Symptom: Some critical parameters or table contents cannot be retrieved. Reason: Original PDF files were scanned images, text extraction failed, and this information was not correctly indexed.
- Symptom: When a user queries a specific product model, the system cannot recall information for that product, or the recalled information is incomplete. Reason: The knowledge base failed to correctly handle string-represented line breaks in documents during segmentation, leading to critical information being truncated or incorrectly segmented.
Configuration Validation
- Select typical rehabilitation equipment product manuals. Upload them to the FastGPT knowledge base and review the text extraction results. Verify that key parameters, models, and descriptions are complete.
- Design a series of queries including product models, technical parameters, and common faults. Observe the accuracy and completeness of recall results. Evaluate the relevance threshold of recalled items.
- Perform retrieval tests on documents containing scanned images or complex tables. Confirm that critical information within this content can be effectively recalled.
- Simulate user queries for product maintenance and troubleshooting scenarios. Check if the system provides accurate operating steps and precautions. Verify that the order of reranked results is logical.
The values provided are common starting points. They 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.