Stability Study Data Characteristics
Stability study data originates from long-term, accelerated, and intermediate condition test reports during drug development. These reports are typically structured PDF documents containing multi-page tables and charts. They record test results at various time points (e.g., 0, 1, 3, 6, 9, 12, 18, 24, 36, 48, 60 months). Data update frequency is relatively low, typically generated periodically as batches and time points progress. Document fields include batch number, test item (e.g., assay, related substances, dissolution, pH, moisture), test result, unit (e.g., %, mg/tablet, minutes), test method, acceptance criteria, and trend analysis in chart form. Key metrics may involve statistical processing, with results presented numerically or graphically.
Constraints on Model Integration and Configuration
Stability study data primarily consists of structured and semi-structured PDF documents. These documents contain numerous tables and embedded charts, demanding high document parsing capabilities from the model. Traditional text segmentation methods may struggle to effectively identify and extract row and column data from tables, leading to loss of critical values or incorrect associations. Furthermore, trend information in charts is difficult for RAG models to directly interpret, requiring image parsing capabilities or preprocessing. The low update frequency means that initial knowledge base construction typically involves importing large amounts of historical data. Subsequent incremental updates focus on new batches or time point data.
Parameter configuration must prioritize the depth and breadth of document parsing, especially for table structures and multi-page content continuity. Due to specialized terminology and units of measurement, the accuracy and unit consistency of model output are critical. This requires precise Chunk size (segment length) and Similarity threshold (similarity threshold) settings.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Stability reports are often large, containing many charts and detailed data. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDF parsing, especially with many tables, requires longer processing time. |
Chunk size | 800–1200 characters | Ensures completeness of table rows or key paragraphs, preventing cross-segment truncation. |
Recall count | 8–12 entries | Guarantees coverage of data associations across multiple batches and time points in stability studies. |
Similarity threshold | 0.75–0.85 | Improves the accuracy of recall results, filtering out irrelevant test data. |
maxContext | 4096 tokens (depends on selected model) | Ensures sufficient context information, especially for tabular data. |
Common Pitfalls
- The knowledge base fails to correctly parse tabular data from PDFs. This leads to incomplete results or missing fields when querying stability trends or specific time point values. This occurs if table parsing plugins are not enabled or configured correctly, or if
Chunk sizeis set too small, causing tables to be incorrectly segmented. - Connection failures or slow responses occur when integrating with external large models, preventing normal question-answering. This may be due to network environment restrictions or incorrect API Key configuration. Newer FastGPT versions have updated regional access policies for model providers.
- The model cannot extract data or trend information from stability charts uploaded as images, preventing answers based on chart content. This occurs because the FastGPT knowledge base does not directly parse image content by default. It requires additional image parsing model configuration or preprocessing.
Verification of Configuration
- Upload a stability study report PDF containing multi-page tables and charts. Query the percentage content for a specific batch at a given time point. Verify the numerical value and unit of the returned result for accuracy.
- Ask questions about stability trends (e.g., "changes in related substances over time"). Check if the model can recall and integrate data from different time points in the knowledge base to form a logical answer.
- Simulate a model integration failure and retry. Observe log output or interface prompts to confirm that the error handling mechanism functions as expected. For example, verify if parsing failure is reported correctly after
PARSE_FILE_TIMEOUT_SECONDStimeout. - Use different versions of the FastGPT client or API interface. Verify the compatibility and consistency of the knowledge base and model configuration across various environments.
The values provided are common starting points. Measure them 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.