Tool Calling and Plugins for SMO Quality Documents

Data Characteristics for This Category

Site Management Organization (SMO) quality documents include clinical trial protocols, informed consent forms, ethics approvals, investigator brochures, standard operating procedures (SOPs), training records, and calibration certificates. These documents are typically in PDF, Word, or scanned image formats. Data sources are primarily sponsors, CROs, research institutions, and internal generation. Updates are frequent, especially for protocol revisions, SOP updates, and personnel training records, potentially changing weekly or monthly. Document structures are rigorous, containing extensive technical terms, regulatory numbers, version numbers, dates, signatures, multi-level headings, and chapter numbers. Common fields and units include dosage units (mg/kg), time units (days, weeks), temperature units (°C), and various medical indicator units. Numerical precision and compliance requirements are extremely high.

Constraints Imposed by These Characteristics on "Tool Calling and Plugins"

The rigor and high update frequency of SMO quality documents impose specific requirements on tool calling and plugins. For example, accurate identification of regulatory and version numbers directly impacts compliance assessment, requiring plugins with precise text extraction capabilities. Diverse document formats (PDF, Word, scanned images) necessitate robust file parsing during preprocessing. OCR accuracy is critical for scanned documents. Frequent updates demand efficient knowledge base synchronization mechanisms to prevent information errors due to outdated document versions. Furthermore, medical terminology and measurement units in documents require the model to accurately understand context during tool calls and maintain unit consistency when passing parameters. This avoids serious consequences from unit conversion errors or confusion. For documents involving approval processes, plugins must trigger external workflows, such as calling approval system APIs to upload documents or update statuses.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	SMO documents, especially PDFs with images and charts, can be large.
`maxContext`	`8000 tokens`	Quality documents have strong contextual relevance; lengthy documents require larger context windows.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large or complex format documents (e.g., scanned PDFs) can take a long time.
`Chunk size`	`800–1200 characters`	Ensures each segment contains sufficient information while avoiding excessive length or loss of context.
`Recall count`	`Top 5 entries`	Improves recall of relevant information, covering potentially dispersed key points in documents.
`Similarity threshold`	`Calibrate by actual measurement`	Requires multiple rounds of testing based on specific document content and retrieval needs to balance precision and recall.

Three Common Mistakes

Calling an API returns 400 Bad Request with the message knowledgeId is invalid: The provided knowledge base ID does not match the ID expected in the workflow or tool configuration, or the ID does not exist.
Workflow execution times out, and logs show file parsing failed: The file format is complex or the file is too large, exceeding PARSE_FILE_TIMEOUT_SECONDS or memory limits, causing OCR or text extraction to interrupt.
Parameter values returned after a tool call are empty or in the wrong format: Extraction rules for relevant fields in the document are inaccurate, failing to correctly identify medical units or values, leading to invalid parameters being passed to external tools.

How to Confirm Correct Configuration

Upload typical SMO quality documents (e.g., signed PDF SOPs) and verify that files are successfully parsed and chunked into the knowledge base.
Call the workflow via API, passing a specific knowledge base ID, and verify that the workflow correctly references the knowledge base content.
Build a workflow that includes tool calls, simulate user queries, and check if the tool can correctly extract parameters based on document content and trigger external APIs.
Check external API call logs to confirm that passed parameters (e.g., drug name, dosage, batch number) match document content and that units are correct.

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.