Data Characteristics for This Category
Pharmacovigilance data in academic promotion primarily originates from clinical study reports, post-marketing surveillance data, regulatory agency safety updates, and medical literature. This data exists in both structured (e.g., adverse event reporting forms, drug registration databases) and unstructured formats (e.g., medical journal articles, conference abstracts). Update frequency is high, especially for new drugs or significant safety events, with daily or weekly updates possible. Document structures vary, including PDF clinical study reports, Word or HTML drug inserts, and XML or JSON database export files. Fields cover patient demographics, medication history, adverse event descriptions (including MedDRA coding), severity, and causality assessment. Units commonly include mg, g, IU for dosage; days, weeks, months for time; and times/day, times/week for frequency.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
High update frequency requires tool calling to support real-time or near real-time data synchronization. This prevents information lag from affecting the accuracy of promotional content. Diverse document formats and structures mean plugins need robust document parsing capabilities to extract key information. For example, accurately identifying adverse event incidence and correlation from PDF reports. The specialized and standardized nature of fields (e.g., MedDRA coding) requires tool calling to understand and process these professional terms, potentially mapping and extending them via external knowledge bases. Furthermore, complex logic like causality assessment necessitates tool calling to trigger deeper analytical models or expert systems. Standardized handling of units like dosage and time is crucial for accurately conveying pharmacological information in promotional content, avoiding misunderstandings due to unit confusion.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 2048 tokens | Accommodates key information from common adverse event reports while balancing model processing efficiency. |
Recall count (Recall Count) | Top 8 entries (Top 8) | Balances recall rate and relevance, ensuring sufficient context for tool calling decisions. |
Similarity threshold (Similarity Threshold) | 0.78 | Filters out low-relevance documents, improving tool calling accuracy and preventing false triggers. |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds (120 seconds) | Handles parsing time for large clinical study reports or complex structured documents, preventing timeouts. |
maxFunctionCallAttempts | 3 times (3 times) | Allows the model to retry after an initial call failure, increasing tool calling success rate. |
Max Function Call History Turns | 5 Turns (5 turns) | Maintains a sufficiently long conversation history for the model to correctly understand user intent and trigger tools in multi-turn interactions. |
Three Common Pitfalls
- Tool calling returns a 400 status code with no response body. This usually indicates incorrect request parameter format or missing authentication information.
- The model, despite supporting function calls, performs poorly and fails to trigger the expected tools. This might be due to an unclear
tool_descriptionor aschemathat does not match the actual API definition. - Errors occur when connecting to SSE interfaces requiring
headerauthentication. This typically means FastGPT's tool configuration did not correctly pass customheaders, or the authentication mechanism does not align with SSE stream expectations.
How to Verify Correct Configuration
- Simulate user queries and observe logs for expected tool call requests. Check if request parameters align with the configuration.
- Verify that the data structure returned after a successful tool call matches expectations, especially for key fields (e.g., adverse event type, frequency) and their format.
- For frequently updated data sources, regularly execute test cases to ensure tool calls retrieve the latest information and verify consistency with original data sources.
- Compare model responses with and without specific tool calls enabled to assess the contribution of tool calling to improving the accuracy and relevance of academic promotion content.
The values provided are common starting points and should be measured against specific 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.