Stability Study Regulations: Citation and Traceability

Data Characteristics

Stability study data comes from internal quality management system documents. These include stability study protocols, stability reports, batch production records, validation reports for testing methods, and regulatory guidelines such as ICH Q1 series. Documents are primarily PDF, Word, and Excel files. Some data resides in LIMS (Laboratory Information Management System) or QMS (Quality Management System) in structured or semi-structured formats. Data update frequency is low, typically changing with drug lifecycles or regulatory revisions. Document structures usually include titles, chapters, tables, and figures. Fields include batch number, production date, expiration date, test items, test results, storage conditions, and sampling time points. Units include ℃, %RH, months, g/mL, and mg/tablet.

Constraints on Citation and Traceability

Low update frequency of stability study documents allows for longer cache periods during knowledge base construction, reducing frequent re-indexing costs. Diverse file formats require robust heterogeneous document parsing capabilities, especially for accurate extraction of tables and figures from PDFs, to avoid information loss. Structured data in LIMS and QMS needs extraction via specific connectors and may require preprocessing for text embedding models. Key fields like batch numbers and storage conditions require precise matching during Q&A for accurate traceability, avoiding generalized answers. Precise unit information is crucial for understanding test results; parsing must retain this context.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Segment Length)	`500-800 characters` (characters)	Stability study documents often contain extensive continuous descriptive text and tabular data. Too short segments may lose context; too long segments introduce noise.
`Chunk Overlap Length` (Segment Overlap Length)	`80 characters` (characters)	Ensures sufficient contextual overlap between adjacent segments, especially when crossing tables or critical paragraphs.
`Index Model` (Indexing Model)	`text-embedding-3-large`	Improves semantic understanding of complex technical texts, enhancing recall accuracy.
`Recall count` (Recall Count)	`Top 5-8 entries` (top 5-8 items)	Stability study questions often require synthesizing multiple relevant paragraphs for a complete answer. Increasing recall count helps cover more comprehensive information.
`Similarity threshold` (Similarity Threshold)	Calibrate by actual measurement	Stability study regulation Q&A demands high accuracy. Too low a threshold may introduce irrelevant content; too high may miss critical information.
`Rerank result count` (Rerank Return Count)	`3 entries` (3 items)	After reranking, the top few most relevant document snippets are usually sufficient to support answer generation while controlling the amount of returned data.

Common Pitfalls

Q&A results lack batch numbers or specific storage conditions because key fields were not correctly identified or extracted during document parsing, leading to incomplete indexing information.
The system cited irrelevant regulatory clauses in its answer because the Similarity threshold (Similarity Threshold) was set too low, or Recall count (Recall Count) was too high, leading to recall of semantically similar but contextually irrelevant documents.
Knowledge base citations could not be obtained in the workflow, preventing answer traceability. This may be because the Knowledge Base Citation node's output variables were not correctly configured, or downstream nodes did not correctly reference upstream outputs.

Verification Steps

For typical questions (e.g., "What are the accelerated stability conditions for a specific batch of medicine?"), check if the Q&A result accurately mentions the batch number and storage conditions, and verify the cited original text snippets.
Randomly select a key paragraph from a stability study protocol. Ask a question to verify if the system can accurately recall that paragraph and its directly related context, and observe if the Similarity threshold (Similarity Threshold) is reasonable.
In the FastGPT workflow, check if the Knowledge Base Citation node's output variables include the expected document snippets and metadata (e.g., document name, page number) to ensure subsequent nodes can use them correctly.

The values provided are common starting points and should be measured against your 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.