Vector Models and Indexing for Rehabilitation Equipment

Data Characteristics for This Category

Rehabilitation equipment data primarily comes from product manuals, technical specification documents, user manuals, clinical reports, and market research reports. Data update frequency is relatively stable, typically changing with product iterations or regulatory updates, usually quarterly or semi-annually. Document structures often include sections like equipment model, functional parameters, target users, contraindications, and maintenance in product manuals, exhibiting semi-structured characteristics. Technical specification documents focus more on detailed physical dimensions, electrical parameters, material composition, and safety standards, containing numerous numerical fields. Units involve physical quantities such as millimeters, kilograms, watts, hertz, volts, and newtons, as well as international standard identifiers (e.g., ISO, IEC).

Constraints Imposed by These Characteristics on Vector Models and Indexing

Numerical parameters and specialized terminology in rehabilitation equipment data require vector models to effectively capture semantic and contextual relationships, avoiding simple word frequency counting. For example, numerical differences in the same parameter (e.g., "maximum load capacity") across different models are crucial for user selection, requiring the model to distinguish these subtle differences. Section divisions in document structures indicate logical relationships between information. Chunking should maintain completeness to avoid splitting critical information. The moderate update frequency means the knowledge base requires regular full or incremental updates to ensure information timeliness. Furthermore, diverse units of measurement and standard identifiers demand entity recognition and standardization during text preprocessing to prevent information mismatch due to inconsistent units.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Size)	`800–1200 characters`	Paragraphs in rehabilitation equipment documents are often long and contain detailed descriptions; this length helps maintain contextual integrity.
`Chunk overlap` (Chunk Overlap)	`100–200 characters`	Ensures sufficient overlap between adjacent chunks to capture key information and relationships across paragraphs.
`embeddingModel`	`text-embedding-ada-002` or `m3e`	Possesses strong semantic understanding capabilities, suitable for processing specialized terminology and parameter descriptions.
`maxContext`	`3000 Tokens`	Provides sufficient context to handle complex product comparisons or multi-faceted inquiries that may arise from user questions.
`Recall count` (Recall Count)	`Top 5–8 items`	Rehabilitation equipment inquiries often require comparing multiple products or obtaining information from various angles; increasing recall quantity helps improve accuracy.
`Similarity threshold` (Similarity Threshold)	`0.75–0.82`	Ensures relevance while recalling subtle differences between various product models, preventing omissions.

Three Common Mistakes

After uploading to the knowledge base, if the status remains "indexing" for a long time, it is usually because the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, causing large PDF or Word files to time out during parsing.
Rate limit errors during vectorization, with logs showing RateLimitExceeded, occur when the EMBEDDING_RATE_LIMIT configuration is too high or not configured, leading to excessive requests to the embedding service in a short period.
After a user query, if critical parameters (e.g., "load capacity") are missing or inaccurate in the recall results, this may be because numerical parameters and their corresponding units were incorrectly split into different text chunks during document chunking, leading to incomplete semantics.

How to Verify Correct Configuration

Upload typical product manuals and technical handbooks. Check if the knowledge base index status completes normally and verify that key parameters and descriptions are complete in the chunk preview.
Use queries containing specific models, parameters, and feature combinations. Verify that the recall results include correct product information and check if the Recall count (Recall Count) meets expectations.
For key information in the query results, check its location in the original document. Confirm that the Similarity threshold (Similarity Threshold) setting accurately captures relevant content and excludes irrelevant interference.
Simulate high-concurrency upload and query scenarios. Observe system logs to confirm the absence of RateLimitExceeded or other abnormal errors, verifying system stability.

The values provided are common starting points. Measure them against your own samples for optimal performance.

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.