Vector Model and Indexing for Rehabilitation Device R&D

Data Characteristics

Rehabilitation device R&D documents include product design specifications, Bills of Material (BOMs), test reports, draft user manuals, regulatory compliance files, and preclinical study reports. These documents originate from internal R&D departments, suppliers, and regulatory bodies. Core design documents and BOMs update frequently during R&D iterations, potentially weekly or monthly. Test reports and regulatory files generate at specific product lifecycle stages, with lower update frequencies. Document structures often mix chapter titles, diagrams, and lists for technical specifications. Test reports contain extensive structured data tables and unstructured experimental records. Fields and units are highly specialized. For example, "applied torque" uses N·m, "degrees of freedom" uses DOF, and material strength parameters often use MPa.

Constraints on Vector Models and Indexing

The specialized nature and diverse structure of rehabilitation device documents challenge vector models. Extensive technical terms and abbreviations require precise semantic understanding to avoid recall bias from vocabulary differences. Mixed layouts, especially interleaved diagrams and text, necessitate multi-modal processing or fine-grained text segmentation to ensure contextual completeness. Varying update frequencies demand indexing strategies that efficiently support partial updates, avoiding full rebuilds. For instance, frequent updates to core design documents require rapid reflection in the index, while low-frequency regulatory file updates allow longer indexing cycles. Precise units and numerical values are critical for retrieval accuracy, requiring vector models to differentiate numerical variations and identify associated physical quantities.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Balances semantic completeness with model processing capacity, preventing information loss or insufficient context from overly long or short segments.
`Chunk overlap` (Segment Overlap)	`50–100 characters`	Ensures contextual continuity between paragraphs and handles cross-paragraph semantic dependencies.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Balances recall and precision. Rehabilitation device specialization requires high precision.
`Recall count` (Recall Count)	`Top 8–12 items`	Ensures sufficient candidate documents while avoiding interference from irrelevant information.
`maxContext`	`3500 tokens`	Matches mainstream large language model context windows, ensuring the model fully understands retrieved content.
`PARSE_FILE_TIMEOUT_SECONDS`	`180 seconds`	Accommodates time-consuming parsing of large or complex documents, preventing parsing timeouts.

Common Mistakes

System prompts "Vector model configuration invalid" or "Failed to initialize vector model." This occurs when the model_name selected in the channel configuration does not match the actual channel_type, or API Key permissions are insufficient.
Retrieval results contain many document snippets unrelated to the query keywords. This symptom, where Recall count (Recall Count) is high but relevance is low, often results from a Similarity threshold (Similarity Threshold) set too low, failing to filter out low-relevance content effectively.
After document updates, the AI assistant's responses still rely on old information. This can happen if the index is not updated promptly, or if Indexing Mode is configured for manual mode and a rebuild was not triggered.

Verification Steps

Upload a batch of test documents containing specialized terms and key parameters. Query using these terms. Check if the returned results include relevant document snippets and verify their semantic association.
Query paragraphs containing tabular data or diagram descriptions. Verify that recall results accurately capture this structured or semi-structured information.
Modify a key parameter in an already indexed core design document. Immediately query to confirm the modified information appears in the retrieval results, verifying index update timeliness.
Check the embedding_token_count metric. Ensure the token count generated after document segmentation meets expectations, avoiding too many or too few tokens due to segmentation issues.

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.