What the data for this category looks like
Medical device investment research data sources include National Medical Products Administration public registration certificates, clinical trial filing materials, industry association released technical standards, manufacturer public product manuals and technical white papers, medical insurance negotiation payment documents, and more. Update frequency varies by document type: registration certificates update with approval processes, industry standards are revised annually, clinical trial data is released in stages alongside trial phases, and product manuals update with product iterations. Document structures include long-form technical descriptions, multi-column structured parameter tables, and scattered policy notices. Fields include medical device classification codes, registration certificate numbers, clinical indications, technical parameters, release dates, validity periods, and more. Some fields have clear measurement and coding units.
What constraints do these characteristics impose on model access and configuration?
Medical device investment research data has a high proportion of long text, numerous structured fields, and strict unit requirements. These impose the following constraints: Long document parsing requires longer timeout times to avoid mid-process interruptions. Accurate extraction of structured tables requires enabling the corresponding parsing switch; otherwise, key parameters will be lost. Field units and coding require the model to have professional term recognition capabilities, so a vector model adapted to professional corpus must be configured. Multi-source data with different update rhythms requires configuring an incremental index update strategy to avoid full-volume repeated parsing. File volumes vary widely, from a few KB of policy notices to hundreds of MB of clinical trial datasets, so maximum upload and parsing volume limits need to be adjusted.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Medical device documents include long-form clinical trial reports, which take longer to parse. This avoids interrupting the parsing process due to timeout |
CHUNK_SIZE | 800–1200 characters | Balances segmentation accuracy for long technical descriptions and structured tables in medical device documents. Excessive chunk size reduces retrieval accuracy, while insufficient chunk size increases context overhead |
RECALL_TOP_K | Top 8–12 results | Investment research requires covering multi-dimensional product parameters and clinical data. Too many results increase model load, while too few results may miss key information |
SIMILARITY_THRESHOLD | 0.75–0.85 | Medical device parameters and indication descriptions are highly professional. A higher matching threshold is needed to avoid irrelevant retrievals |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets upload requirements for large files such as registration certificate attachments and clinical trial datasets |
STRUCTURED_TABLE_PARSE_ENABLE | Enabled | Medical device documents contain a large number of structured parameter tables. Enabling this setting allows accurate extraction of fields and units |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require targeted analysis. Testing on local samples is recommended before finalizing settings.
Three Common Mistakes
- Scenario: When uploading large medical device registration certificate attachments, the interface prompts a network error and returns status code 413. Cause: The
UPLOAD_FILE_MAX_SIZEparameter is not adjusted, and the file volume exceeds the default limit. - Scenario: In FastGPT 4.14.3, model icons in the workflow canvas do not display, and the configured index model cannot be selected. Cause: The FastGPT service has not been restarted to load the newly configured model channel, or the API key for the model channel is configured incorrectly.
- Scenario: After locally deploying MinerU, calling the PDF parsing interface returns empty parsing results, and the log shows a connection timeout. Cause: The correct local port of MinerU is not filled in the FastGPT parsing configuration, or the system firewall blocks port communication.
How to Confirm the Configuration is Complete
- Upload a single medical device registration certificate PDF, check whether the registration certificate number, validity period and other fields are accurately extracted in the parsing results, and verify the correctness of the fields and units.
- Go to the model channel management page, confirm that the status of the added index model is normal, with no error prompts, and matches the current FastGPT version.
- Run a test workflow, upload a small medical device clinical trial summary, check that the number and relevance of retrieved documents meet expectations, and adjust the number of recalled results and similarity threshold to appropriate ranges.
- View the system operation logs, confirm that there are no timeout or error records for the parsing interface and model call interface, and port communication is normal.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.