Knowledge Base Retrieval and Recall for Imaging Equipment

Data Characteristics

Imaging equipment R&D documents originate from diverse sources. These include design specifications, test reports, risk analysis files, clinical validation data, user manuals, and maintenance instructions. Document update frequency depends on the product lifecycle stage. Updates can range from weekly during prototype development to quarterly or annually for mature products. Document structures typically contain numerous charts, CAD model references, specific physical parameters (e.g., spatial resolution lp/mm, signal-to-noise ratio SNR), electrical parameters (e.g., tube voltage kVp, tube current mA), and biocompatibility reports. Fields include extensive specialized terminology and abbreviations, such as DICOM standard, MTF curve, and FOV range. Units involve physical quantities (e.g., mm, kg, V), radiation dosage (e.g., mGy, Sv), and time units (ms).

Constraints for Knowledge Base Retrieval and Recall

The multi-source and complex structure of imaging equipment R&D documents require robust multi-modal processing capabilities in the knowledge base. This ensures effective indexing of charts and specialized terminology. High document update frequency challenges the knowledge base's real-time synchronization and incremental update mechanisms. This prevents recall of outdated information. Specific physical parameters and professional abbreviations in documents make precise matching and semantic understanding critical. This necessitates customized word embedding models or domain dictionaries to differentiate SNR meanings in various contexts. Furthermore, imaging equipment R&D involves strict regulatory compliance. This demands extremely high accuracy and traceability for recall results. Any mis-recall can lead to severe compliance issues. Therefore, the recall strategy must achieve a delicate balance between recall rate and precision.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`800–1200 characters`	Balances semantic completeness with vector embedding efficiency. Avoids diluting key information with overly long text.
`Overlap Length`	`100–200 characters`	Ensures semantic continuity between paragraphs. Prevents truncation of critical information.
`Recall count` (Recall Count)	`Top 8–12 entries`	Balances recall breadth with the computational load of subsequent re-ranking.
`Similarity threshold` (Similarity Threshold)	Calibrated by measurement	Requires evaluation using `F1` scores against the specific R&D document dataset.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates potentially long parsing times for large test reports and design documents.
`maxContext`	`32000 tokens`	Ensures sufficient context to accommodate multiple recall fragments and user queries.

Common Pitfalls

Recall results contain numerous irrelevant general technical documents. This occurs when professional domain vocabulary lacks effective weight configuration, allowing general terms to interfere with recall.
Critical parameter values (e.g., kVp range, mA settings) are missing or inaccurate in recall results. This happens due to insufficient extraction of numerical data from embedded text in charts and unstructured data, leading to information loss.
After a knowledge base update, the application still returns content from older specifications. This is because the knowledge base's incremental synchronization mechanism is not enabled or incorrectly configured, causing index-source data inconsistency.

How to Verify Configuration

Select a batch of test questions containing specific physical parameters and professional abbreviations. Verify that recall results include these key pieces of information and check their accuracy.
Upload a new or revised R&D document. Execute relevant queries and confirm that recall results reflect the latest document content.
For a document containing complex charts and tables, verify the knowledge base can correctly extract and index key data points and associated descriptions.
Through the FastGPT backend's "Knowledge Base Management" interface, check if parameters like Chunk size (Chunk Length) and Overlap Length match the expected settings.

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.