Vector Models and Indexing for Rehabilitation Equipment

Data Characteristics

Rehabilitation equipment quality documentation originates from product design specifications, manufacturing process flows, test reports, user manuals, regulatory compliance statements, and post-market surveillance records. Document updates depend on the product lifecycle and regulatory revisions, typically occurring during product iterations, critical component changes, or regulatory policy adjustments. The document structure is primarily unstructured text, containing extensive technical jargon, parameter lists, flow chart descriptions, and diagrams. Technical parameter fields, such as "rated power," "operating voltage," and "dimensions," often include specific units like watts (W), volts (V), or millimeters (mm). Some documents also include complex fault codes and diagnostic logic.

Constraints Imposed by These Characteristics on Vector Models and Indexing

The characteristics of rehabilitation equipment documentation impose specific requirements on vector models and indexing. First, the specialized terminology, abbreviations, and complex parameter descriptions in the documents require vector models with strong semantic understanding. Models must differentiate synonyms and interpret technical meanings within context. Second, while document update frequency is not high, each update may involve changes to critical parameters or compliance clauses. This necessitates an indexing system that supports incremental updates and ensures updated documents are reflected in recall results promptly. Third, the complex document structure, including numerous tables and figures, means pure text extraction may lose critical information. Therefore, preprocessing must effectively identify and convert this structured data. Accurate matching of parameter fields and units is crucial for quality inspections. This directly impacts the accuracy and precision of recall, requiring the index to precisely locate numerical information with specific units.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Balances context completeness and vector recall efficiency, preventing information overload or scarcity in a single segment.
`Chunk overlap` (Segment Overlap)	`50–100 characters`	Ensures semantic continuity at segment boundaries, especially when describing complex processes.
`Recall count` (Recall Count)	`Top 8–12 entries`	Covers potentially highly relevant document snippets, providing sufficient candidates for subsequent reranking.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adjusts according to specific business requirements for precision and recall. Start testing around `0.75`.
`Rerank result count` (Rerank Return Count)	`Top 3–5 entries`	Focuses on a small number of highly relevant, high-quality pieces of information, reducing interference with the final answer.
`Embedding Model`	`text-embedding-v3-large` or equivalent	Provides stronger semantic understanding, performing well with specialized terminology and long texts.

Three Common Mistakes

Query results do not include critical technical parameters or unit information. This occurs because document preprocessing fails to effectively identify tables or specific parameter field formats, leading to information loss during indexing.
When querying about regulatory revisions or product upgrades, recall results still show old version information. This happens because the knowledge base lacks effective incremental update or version management mechanisms, preventing new documents from timely replacing or associating with old document indexes.
For complex queries related to fault diagnosis, the returned results are vague and do not provide specific steps or codes. This is due to the vector model's insufficient understanding of long sentences and logical relationships, or a Chunk size (Segment Length) that is too short, causing context information to be fragmented.

How to Confirm Correct Configuration

Select several representative rehabilitation equipment documents, including technical parameters, troubleshooting, and compliance statements. Perform keyword and phrase queries. Observe whether the recalled content is complete and accurate, especially for parameter values and units.
Use documents containing the latest regulatory updates or product iteration content for testing. Verify that query results prioritize the latest version information and accurately answer related changes.
For predefined complex fault scenarios, construct multi-turn dialogues or complex questions. Check if the system can integrate information from multiple document snippets to provide clear, step-by-step diagnostic advice, and evaluate its consistency with actual operation manuals.
Periodically perform small-scale random sample queries on the knowledge base. Compare recall results with human-judged relevance. Adjust Similarity threshold (Similarity Threshold) and Recall count (Recall Count) based on business needs.

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.