Tool Calling and Plugins for Rare Disease Products

Data Characteristics for This Category

Rare disease product data comes from diverse sources. These include clinical trial reports, drug monographs, medical journals, patient registries, and regulatory approval documents. Data update frequencies vary. New drug development, clinical research advancements, and expanded indications lead to information changes. Some core data updates infrequently, but new research findings and adverse event reports may be released in real-time. Document structures are often standardized. Drug monographs are typically in PDF format, containing fixed fields like indications, dosage and administration, contraindications, and adverse reactions. Clinical trial reports may include more complex charts and statistical data. Fields and units are specific: dosages often use milligrams (mg) or micrograms (µg), treatment durations use days, weeks, or months, and adverse event rates are often percentages. Patient data may involve complex and high-dimensional information such as gene sequences and biomarkers.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The multi-source and complex nature of rare disease data demands robust tool calling and plugins. For example, when processing large volumes of unstructured or semi-structured documents (like hundreds of pages of PDF clinical reports), file parsing tools such as doc2x need efficient and stable parsing capabilities. This prevents parsing failures due to excessively large files or abnormal formats. The uncertainty of data updates means tools must consider timeliness when recalling information. This may require integrating multiple data sources and setting data freshness policies. The specialized and diverse nature of fields requires plugins to accurately identify medical terminology, dosage units, and time periods during information extraction, avoiding semantic misunderstandings. High-dimensional patient data, such as genetic information, may require specialized bioinformatics tools for preprocessing before general plugins can effectively utilize it.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	1000 MB	Accommodates individual clinical trial reports or drug monographs that can be several hundred megabytes.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Ensures tools like `doc2x` have sufficient time to process complex, multi-hundred-page PDF documents.
`maxContext`	800–1200 characters	Rare disease descriptions often involve multiple pieces of information, requiring a longer context window to maintain semantic integrity.
`Similarity threshold` (Similarity Threshold)	0.75	Rare disease product names or symptom descriptions may have subtle differences, requiring a higher threshold for precise recall.
`Recall count` (Recall Count)	Top 5	Ensures that a limited query covers multiple highly relevant rare disease products or research information.
`Rerank result count` (Reranked Return Count)	3	Further refines recall results, focusing on the most directly relevant few items to improve information retrieval efficiency.

Common Pitfalls

File parsing tools error out when processing extremely large PDF files. For instance, doc2x fails to parse PDFs with hundreds of pages but works fine with dozens of pages. This usually happens because PARSE_FILE_TIMEOUT_SECONDS is set too short or the file size exceeds the UPLOAD_FILE_MAX_SIZE limit.
Tool calling logs are missing or incomplete. Calls occur but are not recorded. This may be due to improper log level settings or issues with the log storage backend.
In a local deployment environment, specific plugins (e.g., PDF parsing plugins) show a Cannot read properties of undefined error on the page. This often indicates that local environment dependencies are not correctly installed or are version incompatible, leading to plugin initialization failure.

How to Verify Configuration

Upload and parse a rare disease clinical trial report PDF file with a large number of pages (e.g., over 300 pages). Observe if the parsing process is smooth, without timeouts or errors, and check the completeness of the parsed content.
Query for rare disease product names or indications. Verify that the returned results include expected specialized terminology, dosage information, and clinical data. Check if Recall count (Recall Count) and Rerank result count (Reranked Return Count) match the configured expectations.
Through system logs or monitoring interfaces, confirm that each tool call (e.g., file parsing, information extraction) has a corresponding success or failure record, including necessary parameters and return result information.
Test edge cases. Use rare disease data documents containing special characters, complex tables, or many images to verify the compatibility and stability of tools and plugins.

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.