Tool Calling and Plugins for Stem Cell Therapy Regulations

Data Characteristics

Stem cell therapy regulations and Standard Operating Procedure (SOP) documents primarily originate from regulatory bodies like the National Medical Products Administration (NMPA), the National Health Commission, industry associations, and internal institutional guidelines. These documents are typically in PDF, Word, or scanned image formats. They feature a rigorous structure, extensive terminology, numbering, flowcharts, and tables. Update frequency is relatively low, mainly occurring when policies or technical guidelines change. Document fields include, but are not limited to: cell type, preparation process, quality control standards, clinical indications, ethical review requirements, and adverse reaction monitoring indicators. Units cover cell counts (e.g., 10^6 cells/kg), time periods (e.g., 24 hours, 3 months), concentrations (e.g., μg/mL), and temperatures (e.g., ℃).

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The rigorous nature and high density of specialized terminology in stem cell therapy regulatory documents demand high-precision information extraction capabilities from a Q&A system. The predominantly unstructured text challenges document parsing and knowledge chunking strategies, requiring that critical information remains intact. A low update frequency allows for relatively stable knowledge base construction and maintenance. However, new policies require rapid response and knowledge updates. Flowcharts and tables within documents can lose structural information during conventional text extraction, impacting the accurate identification of specific operational steps or quality control parameters during tool calls. Moreover, units and numerical values require the tool to identify and parse units for numerical comparisons or conditional judgments, preventing misinterpretations due to unit inconsistencies.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_length`	`500–800 characters`	Ensures each knowledge chunk contains sufficient context while avoiding redundancy or reduced model processing efficiency. This adapts to the paragraph structure of regulatory documents.
`recall_quantity`	`Top 8–12 items`	Given the precision requirements for regulatory Q&A, increasing the recall quantity improves coverage, addressing complex queries that may involve multiple relevant clauses.
`similarity_threshold`	`0.78–0.85`	Stem cell regulatory texts are highly specialized with many semantically similar expressions. A higher threshold filters for more precise knowledge fragment matches.
`rerank_return_quantity`	`Top 5 items`	After a high recall quantity, reranking further optimizes relevance, ensuring the most accurate knowledge items are presented to the model first.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Regulatory documents often contain large amounts of text and complex structures. Increasing the parsing timeout accommodates processing large PDF or Word files.
`maxContext`	`32000 tokens`	Ensures the model has sufficient contextual space to process lengthy regulatory clauses or multiple related knowledge fragments, satisfying complex procedural questions.

Common Pitfalls

"Connection Error" or "Service Unavailable" when calling the application: This typically indicates unstable network connectivity between the FastGPT application backend and the large language model (LLM) service, or incorrect LLM service interface configuration. Verify that API_URL and API_KEY are correct and accessible.
Knowledge base documents are uploaded, but API call results do not include knowledge base content: This may be due to an incorrect knowledgeBaseId in the API call parameters, or the model's answer generation process not triggering the knowledge base retrieval mechanism.
Incorrect judgment of a numerical value or time unit in the Q&A result: This usually occurs when document parsing fails to accurately identify and extract unit information from the text, or the tool calling logic does not account for unit conversion during numerical comparisons.

Verification Steps

Upload a stem cell therapy SOP document containing complex tables and flowcharts. Check the knowledge base chunk preview to confirm that key information from tables and flowcharts is effectively extracted and segmented.
Ask questions about precise numerical values and units (e.g., cell dosage, quality control indicators) within the document. Verify the accuracy of numerical values and units in the model's response against the original document.
Simulate an external API call. Check if the returned JSON structure includes the expected tool_code and tool_params fields, and validate if their values align with the tool calling rules defined in the document.
Upload newly released regulatory documents related to stem cell therapy and conduct Q&A tests. Evaluate the system's understanding and accuracy in answering questions about new policy clauses after a rapid knowledge update.

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.