What the Data for This Category Looks Like
Data sources include the National Medical Products Administration medical device registration database, public clinical trial results, manufacturer R&D pipeline announcements, medical insurance payment catalogs, and others. Update frequency fluctuates with regulatory requirements and R&D progress. Bulk updates trigger when new registration certificates are issued or clinical trial results are published.
Document structure falls into two categories: structured fields (such as registration certificate number, manufacturing enterprise, scope of application, validity period) and unstructured text (such as clinical trial method descriptions, clinical benefit analysis). Some device parameters include physical units, like pressure thresholds, imaging resolution, scan slice thickness, and others.
Constraints on Context and Token Processing
The multi-field structured nature of medical device investment research data leads single recall results to carry large amounts of parameter information with units. This increases token usage for a single context.
Unstructured clinical trial reports and R&D pipeline analysis texts can each occupy thousands of tokens. Additionally, investment research scenarios require recalling multiple sets of comparative data across manufacturers and models. The total length of a single recall context easily exceeds the limits of large models.
Furthermore, frequently updated R&D pipeline and clinical data require context recall to prioritize the latest versions. This prevents valid information from being truncated due to old data occupying tokens.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 12000–18000 characters | Adapts to the total length of a single long medical device document after splitting combined with multiple recall results, avoiding exceeding the large model’s context window limit |
maxTokens | 8000–16000 | Adapts to the total input and output token limits of mainstream large models, reserving sufficient space for generated tokens |
recallTopK | Top 8–12 results | Balances the multiple sets of comparative data required for medical device investment research and token usage. Too many results will exceed the large model’s limits, too few will lose critical information |
rerankTopN | Top 4–6 results | Filters low-relevance recall results through reranking, reduces invalid token usage, and retains core comparative data |
chunkSize | 1000–1500 characters | Adapts to the parameter-with-unit and long sentence structures found in medical device documents. Splitting too short will break parameter associations, while splitting too long will lead to excessive token usage per single chunk |
similarityThreshold | 0.75–0.85 | Filters low-relevance medical device parameters and text, preventing invalid context from occupying tokens |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. Testing on internal samples is recommended before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When setting
recallTopKto above 20 ormaxContextto above 20000 characters, the large model cannot receive context content, and the returned result only contains basic instructions. Cause: The total context token exceeds the large model’s supported input window. FastGPT automatically truncates the context and loses the context identifier, causing the large model to fail to match the recalled content. - Phenomenon: When uploading a single medical device clinical trial report exceeding 50000 characters, parameter fields are broken and units are lost after parsing. Cause: The
chunkSizeparameter is not adjusted. The default split length cannot adapt to structured parameter blocks in long texts, causing parameters to be split into different chunks and unable to be fully associated during recall. - Phenomenon: When batch launching medical device investment research analysis requests, some requests return a 503 status code or are automatically discarded. Cause: The system concurrency limit is not properly configured. The number of concurrent requests exceeds the token processing resources reserved by the system, causing context transmission interruptions.
How to Confirm Proper Configuration
- Users can access the knowledge base configuration page and verify whether
maxContextmatches the input token limit of the currently used large model. A single medical device registration certificate document can be uploaded, and the total character count of the recall preview can be checked to confirm it falls within the configured range. - Users can launch an investment research analysis request, and check whether the large model’s returned result includes core fields such as the medical device’s registration certificate number and scope of application. The number of recall results can also be verified to match the expected configuration.
- Users can upload a single long-text clinical trial report, and check whether the parsed chunk list retains complete parameter blocks and unit information without splitting breaks.
- Users can launch a batch request test, and confirm that the number of concurrent requests does not exceed the concurrency limit configured by the system. No requests should be automatically discarded.
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.