Data Characteristics in this Category
Stability study data in pharmacovigilance primarily originates from continuous post-market drug monitoring. This data typically includes batch production records, retain sample observation reports, accelerated stability study reports, and long-term stability study reports. Data update frequency varies; accelerated stability data might update every 3-6 months, while long-term stability data could update annually. Document structure is usually a structured report, containing fixed fields such as title, study objective, test conditions, sample batch number, test items, test results, data charts, and conclusions. Common fields include "Batch No.", "Production Date", "Expiration Date", "Test Time Point", "Temperature", "Humidity", "Test Indicator" (e.g., content, dissolution, related substances), and "Unit" (e.g., %, mg/tablet, ppm). This data sometimes appears as tables embedded in PDF documents or stored in LIMS systems.
Constraints Imposed by These Characteristics on "Reference and Traceability"
The structured nature and fixed fields of stability study data require precise referencing to specific reports and key data points for traceability. Due to the low data update frequency, the knowledge base synchronization strategy can employ periodic full updates, reducing the complexity of incremental updates. Charts and tables within reports challenge text extraction and parsing capabilities, requiring effective indexing of non-textual information and referencing to its original location. Specific units and indicators, such as "related substances %", demand recognition and differentiation during retrieval and referencing to avoid confusion between different indicators or units. Furthermore, as data sources are typically internal systems or specific report formats, support for various file types is necessary to ensure information completeness.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Stability reports have moderate content density. This length helps maintain semantic completeness of paragraphs and reduces excessive fragmentation. |
Overlap Length | 100 characters | Ensures contextual continuity between adjacent paragraphs, especially when tables or key conclusions span across segments. |
Recall count | Top 5 entries | Queries in stability studies often focus on specific batches or indicators. A moderate number of recall items balances accuracy and response speed. |
Similarity threshold | Calibrate based on actual measurements | Requires iterative testing against actual data and query scenarios to ensure highly relevant results are recalled. |
File Parsing Timeouttime | 300 seconds | Stability reports may contain numerous tables and charts, leading to longer parsing times. Increasing the timeout prevents parsing failures. |
Maximum File Upload Size | 200 MB | Individual stability report files can be large, especially when containing high-resolution images or embedded data. |
Three Common Mistakes
- Query results cite irrelevant reports or data points: This occurs when the
Similarity threshold(similarity threshold) is set too low, leading to the recall of semantically mismatched document snippets. - Accurate numerical or unit information is unavailable in the conversation: This happens when the file parser fails to correctly identify and extract table data or fields with specific units from reports, resulting in the absence of this critical information in the knowledge base.
- When retrieving stability data for a specific batch number, information for other batch numbers is returned: This is due to an excessively long
Chunk size(segment length), which mixes independent information from different batches into the same segment, affecting retrieval precision.
How to Confirm Correct Configuration
- Select a stability report containing multiple batches and test indicators. Perform keyword queries and verify that the returned references precisely point to the relevant batch and indicator data within the report.
- For table data within the report, ask questions about specific numerical values. Verify that the data cited in the conversation's response exactly matches the values in the original report.
- Simulate a query for the trend of "related substances %" over time for a specific batch of medicine. Check that the cited data fully presents this trend and that all references originate from the stability report for that specific batch.
- Use FastGPT's reference traceability feature to click on citation links. Confirm that the link directly navigates to the corresponding location in the original file or displays the relevant text snippet.
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.