Tool Calling and Plugins for Solid Tumor Regulations

Data Characteristics for This Category

Solid tumor-related regulations and SOP documents originate from various sources. These include national drug administration agencies, hospital ethics committees, clinical trial institutions, and pharmaceutical company internal R&D guidelines. Update frequencies vary. National policies and regulations may be revised annually. Clinical trial protocols or internal SOPs might undergo multiple version iterations within a project cycle, based on practical situations.

Document formats are diverse. Regulatory documents are often in PDF. Internal guidelines can be in Word or Markdown. Some online standards are published in HTML. Content structures typically include introductions, definitions, responsibilities, processes, and appendices. They heavily cite medical terminology, drug names, dosage units, and time periods. For example, chemotherapy protocols explicitly state drug dosages (e.g., mg/kg), administration cycles (e.g., every 3 weeks), and adverse event grading standards (e.g., CTCAE v5.0).

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The data characteristics of solid tumor regulation documents impose specific requirements on tool calling and plugin design.

First, varying update frequencies necessitate knowledge base support for version management and incremental updates. This ensures the timeliness and accuracy of recalled information. For instance, when new clinical guidelines are released, older SOPs may need to be deprecated or updated promptly. Tool calls must trigger corresponding knowledge base update processes.

Second, diverse document formats require robust file parsing plugins. These plugins must accurately extract structured and unstructured information from various formats, including PDF and Word. This prevents critical information loss due to format parsing failures.

Additionally, documents contain specific medical terminology and measurement units. Text processing and semantic understanding stages require preprocessing with specialized dictionaries or named entity recognition tools. This improves subsequent question-answering accuracy. For numerical information like dosages and cycles, tool calls must identify and perform unit conversions or range checks. Examples include converting mg/kg to total dosage or determining if a treatment cycle complies with regulations.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Balances semantic completeness and information density per segment, avoids diluting the topic with long paragraphs.
`Recall count` (Recall Count)	`top 8–12 entries`	Covers multiple aspects of regulations, improves relevance of recall.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Balances recall precision and quantity, reduces irrelevant results.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates parsing time for large PDF or Word documents, prevents timeout failures.
`maxContext`	`4000 tokens`	Adapts to the longer context requirements in regulatory Q&A, maintains coherence.
`Rerank result count` (Reranked Return Count)	`top 5 entries`	Prioritizes the most relevant information, enhances user experience.

Common Mistakes

429 Too Many Requests status code occurs when calling external APIs. This happens when concurrent request volume exceeds the target API's rate limits, indicating a lack of effective request frequency control.
Key fields in tool call results are empty or incorrectly formatted. This typically occurs when plugins fail to correctly identify structured information during target data parsing. Examples include not handling nested fields in JSON responses or not converting to the expected data type.
Outdated regulatory content appears in Q&A results. The root cause is an un-updated knowledge base, or an update mechanism that failed to effectively deprecate old document versions. This leads the model to recall invalid information.

How to Confirm Correct Configuration

Perform multi-round Q&A tests on core regulatory documents. Verify if Q&A results accurately cite specific clauses, dosages, or cycle information from the documents.
Simulate concurrent scenarios. Observe tool call logs for HTTP 429 errors. Adjust concurrent control strategy thresholds based on error frequency.
Regularly upload new versions of regulatory documents. Verify the validity of new and old versions through retrieval. Ensure the knowledge base update process correctly covers and deprecates outdated content.
Check the completeness of tool call results. Ensure all expected fields (e.g., drug name, dosage, administration route) are correctly extracted and populated.

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.