Data Characteristics for this Category
Imaging equipment product data comes from diverse sources. These include product manuals, technical specifications, operation guides, maintenance manuals, and clinical application cases. Manufacturers typically release these documents. Updates are relatively stable, occurring mainly with new product releases, firmware upgrades, or regulatory changes. Document structures are usually chapter-based. They contain many images, charts, structural diagrams, and detailed parameter lists. For example, an MRI equipment manual details magnetic field strength (unit: Tesla), gradient field strength (unit: milliTesla/meter), scan sequence names, and image resolution (unit: pixels). It also includes waveform diagrams and scan protocol examples. CT equipment documents cover detector types, X-ray tube voltage (unit: kilovolts), current (unit: milliampere-seconds), and scan speed (unit: seconds). These documents generally use rigorous professional terminology and precise numerical descriptions.
Constraints from these Characteristics on "Document Parsing and Chunking"
The chapter-based structure and numerous diagrams in imaging equipment documents mean that chunking by natural paragraphs can lead to semantic discontinuity. Key technical parameters and professional terms are spread across text, tables, and image captions. Chunking must capture this core information. The images and complex layouts in documents demand higher accuracy in text extraction. For example, if table parameters are not parsed correctly, it directly impacts subsequent question-answering accuracy. Imaging equipment products are highly specialized. Many parameters have specific units and value ranges. Chunking should preserve the association between these values and units. Avoid splitting them into different chunks, which affects contextual understanding. Document updates are infrequent. However, each update may involve core performance indicators or safety regulations. The system must identify and prioritize these critical revisions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters | Balances semantic completeness and recall efficiency. Avoids overly long chunks diluting key information and overly short chunks losing context. |
Maximum Paragraph Depth (Max Paragraph Depth) | 3 | Imaging equipment documents typically have clear chapter and section structures. This depth covers most levels and preserves structured information. |
Index Size | 128 | Ensures an appropriate granularity for indexing. Effectively distinguishes details of different equipment models, functions, or parameters. |
Model Identify Paragraphs | enabled | Uses the model to identify logical paragraph boundaries in documents. Improves parsing accuracy, especially for technical manuals. |
API_TIMEOUT_SECONDS | 600 seconds | Imaging equipment manuals can contain many pages. File parsing takes longer. Increasing the timeout prevents interruptions. |
Parsing Type | PDF | Imaging equipment documents are often distributed in PDF format. Select a PDF-optimized parser to improve mixed text and image parsing. |
Three Common Mistakes
- A large amount of garbled text or missing key parameters after document parsing: This occurs because documents contain complex charts or non-standard fonts that the default parser cannot correctly recognize.
- Inability to accurately answer parameter questions about a specific device model in a conversation, returning only generic descriptions: This happens when document chunks are too large, and specific model parameters are diluted by generalized information, leading to imprecise recall.
- System unresponsive for a long time or returning a
504 Gateway Timeouterror after uploading a large PDF manual: This is due to the large file size or parsing complexity exceeding the defaultAPI_TIMEOUT_SECONDSrequest timeout.
How to Confirm Correct Configuration
- Randomly select 5 imaging equipment documents of different models for parsing. Check if the parsed text content is complete and free of garbled characters. Specifically verify that key technical parameters (e.g., magnetic field strength, voltage, pixels) are extracted correctly.
- For the parsed documents, ask precise questions about specific device functions or parameters, such as "What is the X-ray tube voltage of a certain CT device?". Check if the system provides accurate answers including units. Evaluate if the recalled knowledge chunks contain the required information.
- Attempt to upload a complex document with many tables and images. Observe if the parsing process completes smoothly without timeouts or parsing failures. Check the completeness of table content in the parsing results.
- Compare the document chunking results. Check if a logical paragraph (e.g., the description of a specific functional module) is completely contained within one or a few chunks. Avoid excessive semantic splitting.
Note: 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.