Reference and Traceability for Stem Cell Therapy Regulations

Data Characteristics

Regulatory and SOP documents in the field of stem cell therapy originate primarily from regulations issued by national drug administration and health commissions, guidelines from industry associations, and clinical operating procedures developed by medical institutions. These documents are typically in PDF, Word, or scanned image formats. Content covers ethical review, clinical trial protocols, preparation and quality control standards, and clinical application guidelines. Update frequency is relatively low, usually tied to policy adjustments or technological advancements, with new versions released every few months to several years. Document structures are rigorous, including chapters, clauses, and annexes. Fields are mostly text descriptions, with a small amount of numerical data such as dosage, time, and batch, strictly adhering to pharmaceutical industry units like "mg/kg," "IU," and "%."

Constraints Imposed by These Characteristics on "Reference and Traceability"

The authoritative and rigorous nature of stem cell therapy regulatory documents makes accurate reference a core requirement. Due to the low update frequency of regulations and SOPs, the knowledge base needs careful version control and content comparison when ingesting new versions to ensure that references are to the latest and valid clauses. Structured information within documents, such as clause numbers and chapter titles, is crucial for precise traceability. The RAG system must identify and extract this metadata. Some documents may be scanned images, requiring strong OCR capabilities to avoid text recognition errors that lead to inaccurate references. For clauses involving numerical fields, such as "stem cell preparation dose is 5x10^6 cells/kg," the system must accurately reference the original numerical value and unit, avoiding confusion or alteration.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Segment Length`	`500–800 characters`	Ensures each segment contains a complete clause or SOP step, preventing semantic fragmentation.
`Recall count` (Recall Count)	`Top 5`	Given the logical coherence of regulatory documents, increasing the recall count helps cover relevant context.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Ensures strict relevance of recalled content, avoiding references to irrelevant regulatory clauses and improving accuracy.
`Rerank result count` (Reranked Return Count)	`Top 3`	Further selects the most relevant clauses to the user's question, reducing redundant information.
`Document Metadata Extraction`	Enable `Chapter Number`, `Publication Date`	Ensures references provide precise regulatory clause numbers and publication dates for user traceability.
`OCR Recognition Accuracy`	Calibrate based on actual measurements	Guarantees text recognition accuracy for scanned documents, preventing reference errors.

Common Pitfalls

The files cited in the knowledge base's output do not match the actual document content. This is due to OCR recognition errors causing text discrepancies or outdated document versions.
When calling FastGPT externally, the returned reference information is incomplete, missing datasetId or chunkId. This may be due to improper interface parameter configuration, where the detail field was not fully requested.
The displayed reference content does not match expectations, with reference text returned even when not explicitly enabled. This could be due to changes in default behavior after a system version upgrade. Check configuration items such as showReference.

How to Verify Configuration

Randomly select 10 stem cell therapy-related regulatory questions and verify that the system's cited sources precisely match the original document's chapters, clauses, and page numbers.
For SOP clauses containing numerical data, verify that the numerical values and units in the cited text precisely match the original document.
Simulate user queries and check if the system clearly displays the cited document name, version number, and specific clause number when returning answers.
Check logs for abnormal records caused by document parsing failures or OCR recognition errors, and optimize accordingly.

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.