Vector Models and Indexing for Imaging Equipment

Data Characteristics for This Category

Imaging equipment (e.g., CT, MRI, ultrasound diagnostic devices) registration and declaration documents feature distinct structured and semi-structured characteristics. Data sources are diverse, including product technical requirements, instruction manuals, clinical evaluation reports, test reports, risk management reports, and software verification reports. Update cycles typically align with product upgrades, regulatory revisions, or the release of clinical trial results. Updates are infrequent but have significant impact. Document structures are complex, containing numerous charts, specialized terminology, acronyms, and specific formatting requirements. Fields and units strictly adhere to medical device industry standards, such as dose units mSv, magnetic field strength Tesla, and image resolution dpi. Data often involves a mix of different modalities (e.g., DICOM, JPEG, PDF).

Constraints Imposed by These Characteristics on Vector Models and Indexing

The complex document structure of imaging equipment data requires vector models to effectively process multi-modal information and understand contextual relationships. A single text segmentation strategy may be insufficient to capture critical information. The dense use of specialized terminology and acronyms demands higher semantic understanding from the model, ensuring embedding vectors accurately differentiate similar concepts. Although update frequency is low, each update may involve extensive interdependent revisions. Index reconstruction must consider the efficiency and completeness of incremental updates. Strict field and unit requirements mean preprocessing may be necessary before vectorization to standardize data formats, avoiding semantic deviations caused by inconsistent units. Additionally, the presence of numerous charts suggests vector models need some image-text understanding capabilities, or that chart content should be converted into vectorizable text descriptions during preprocessing.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`embeddingModel`	`qwen3-embedding-8b` or `m3e-base`	Balances understanding of Chinese specialized terminology with deployment flexibility.
`Chunk size` (Segment Length)	`500–800 characters` (characters)	Accommodates longer descriptive paragraphs in technical documents, maintaining contextual completeness.
`Chunk Overlap Length` (Segment Overlap Length)	`50–100 characters` (characters)	Ensures semantic continuity at segment boundaries, preventing information loss.
`Recall count` (Recall Count)	`10–15 entries` (items)	Improves the recall rate of relevant document snippets for complex queries, covering multi-dimensional information.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Balances recall precision and completeness, filtering irrelevant information and reducing noise.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds` (seconds)	Addresses the time required for parsing large PDFs or image-heavy documents.

Three Common Mistakes

Inaccurate or missing critical information in query results after building the knowledge base. The returned document snippets deviate significantly from the query intent. This usually happens because Chunk size (segment length) is set too short, causing critical information to be fragmented, or the embeddingModel inadequately understands specific specialized terminology.
Locally deployed index models fail to load or call correctly. Logs show connection errors or model initialization exceptions. This may be due to an incorrect FASTGPT_EMBEDDING_URL configuration, or the local model's API interface does not match FastGPT's expectations.
Query results do not reflect the latest content promptly after updating the knowledge base; old information is still recalled. This may be because a full rebuild of the knowledge base was not performed, or the incremental update strategy failed to correctly identify and process modified documents.

How to Confirm Correct Configuration

Upload a typical document (e.g., a product technical requirement for imaging equipment). Check the knowledge base segment preview to ensure key technical parameters, chart descriptions, and other information are correctly segmented with complete context.
Perform query tests on core technical parameters, fault codes, and regulatory clauses. Verify the relevance and accuracy of recall results, and observe if similarity scores are within a reasonable range.
Simulate a document update process. Modify some key information, then rebuild the knowledge base. Query the modified points to confirm the updated content is effective and old information no longer interferes.
Check system logs to ensure no embedding model call failures or file parsing timeouts occurred during knowledge base construction.

The values provided are common starting points and should be measured against the reader's 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.