Tool Calling and Plugins for Neurodegenerative Disease

Data Characteristics for this Category

Data for neurodegenerative disease protocols and SOP documents primarily originates from guidelines, standard operating procedures, and research reports published by medical institutions, pharmaceutical R&D departments, clinical trial centers, and regulatory bodies. These documents have a relatively low update frequency, typically revised every few months to several years, following policy adjustments or new research developments. Document structures are often hierarchical text, containing extensive specialized terminology, abbreviations, charts, and cross-references. Common fields include disease classification codes, drug dosage and administration routes, diagnostic criteria, treatment plans, ethical review numbers, clinical trial phases, and adverse event reporting formats. Units are precise, such as drug concentration units like mg/mL, time units like hours, days, years, and biomarker concentrations like pg/mL.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The low update frequency of neurodegenerative disease protocol documents means that initial data synchronization and subsequent incremental update strategies for knowledge base construction require attention to avoid frequent redundant indexing. Complex hierarchical structures and cross-references demand robust document parsers that preserve semantic relationships for accurate subsequent question answering. The large volume of specialized terminology and abbreviations requires tool calls to accurately identify and map to corresponding knowledge graphs or external databases. For example, the abbreviation AD must be correctly resolved as Alzheimer's Disease. The precision of fields and units necessitates strict validation of input and output formats when executing plugin functions like drug dosage calculations or treatment cycle inferences, preventing errors due to unit confusion. For instance, when processing the drug dosage field, accurate unit conversion must be ensured.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`8000`	Neurodegenerative disease protocol documents are often lengthy, requiring a larger context window to capture complete semantics.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing PDFs or Word documents with numerous charts and complex layouts can be time-consuming; this avoids parsing timeouts.
`Segment Length`	`800–1200 characters`	Ensures each segment contains sufficient information while avoiding excessive length that could reduce retrieval efficiency, balancing the completeness of specialized terminology context.
`Retrieval Count`	`Top 5`	Considering the specialized and precise nature of the documents, increasing the retrieval count improves the probability of relevant recall.
`Similarity Threshold`	Calibrated by measurement	Adjust based on the similarity distribution of the specific corpus to ensure retrieved results are relevant and not redundant.
`PLUGIN_EXECUTION_TIMEOUT`	`60 seconds`	External tool calls (e.g., drug calculators) may involve complex logic or slow external interface responses; this provides sufficient execution time.

Three Common Mistakes

When calling an external model, a 401 Unauthorized error occurs because the API key is incorrectly configured or has expired.
When using the Fetch API to call the /api/v1/chat/completions interface, a CORS policy error is encountered because the frontend request domain does not match the FastGPT service deployment domain, and the server has not correctly configured CORS headers.
Plugin execution returns an empty or incorrectly formatted result, indicated by a missing plugin_output field or data type mismatch. This happens when the plugin code does not strictly return results according to the defined data structure, or the external service interface data structure has changed and was not synchronized.

How to Verify Configuration

Upload a neurodegenerative disease SOP document containing complex charts and specialized terminology. Check the completeness and accuracy of segmented content in the knowledge base after document parsing.
Test a question-answering scenario involving drug dosage calculation. Observe tool call logs to confirm that the plugin_output field returns the expected calculation result and that units are correct.
Use an external Fetch API to simulate user questions. Check if the /api/v1/chat/completions interface responds normally and without CORS-related error messages.
Conduct multi-turn questioning on the knowledge base, including queries about disease diagnostic criteria, treatment plans, and adverse event handling. Verify the accuracy and relevance of the question-answering results, and adjust the retrieval count and similarity threshold based on actual performance.

The values provided are common starting points and should be measured against the reader's 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.