Data Characteristics in This Category
Stability study data typically comes in batches. It covers drug storage data under long-term, accelerated, and intermediate conditions. Data sources are diverse, including laboratory analysis reports, environmental monitoring records, and retained sample observation records. Update frequency usually follows predefined sampling points, such as 0, 3, 6, 9, 12, 18, 24, 36, 48, and 60 months. Document structures are centered on batch numbers and include analysis results for multiple time points. These results cover content, dissolution, purity, pH, and moisture. Each indicator typically includes the detection method, instrument, and result units (e.g., %, mg/tablet, min, °C). Document formats are often PDF reports, Excel data sheets, or LIMS system export files.
Constraints from These Characteristics on Model Integration and Configuration
The time-series nature of stability study data requires the model to accurately identify and associate data from different time points when processing documents. Batch numbers are core identifiers. They require effective extraction and indexing during data preprocessing to ensure accurate cross-document querying. Diverse indicator units and value ranges demand strong data type recognition and unit conversion capabilities from the model. This prevents misinterpretation due to unit confusion. LIMS system export files may contain unstructured descriptions, requiring enhanced text parsing capabilities. Document update frequency dictates the knowledge base refresh strategy. It needs to support incremental updates to incorporate the latest stability data and ensure information timeliness.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
Chunk size (Segment Length) | 500-800 characters (characters) | The data block length for a single time point in stability reports is moderate, balancing semantic completeness and recall efficiency. |
Chunk Overlap Length (Segment Overlap Length) | 50 characters (characters) | Ensures critical information like batch numbers and drug names overlap in adjacent segments, maintaining contextual relevance. |
Similarity threshold (Similarity Threshold) | 0.75 | Stability data queries typically require high-precision matching to reduce false recall rates. |
Recall count (Number of Retrieved Items) | Top 8 entries (top 8 items) | Stability study reports often require synthesizing information from multiple batches or time points for judgment. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Provides sufficient parsing time when processing large PDF reports or Excel files with complex tables. |
maxContext | 32000 | Stability data analysis may involve comparing data from multiple batches and time points, requiring a longer context window. |
Three Common Pitfalls
- When the model extracts SQL, the returned SQL statements lack spaces or have incorrect formatting. This leads to database query failures. The reason is insufficient learning of SQL format specifications in the model's training data.
- Querying stability data for a specific batch results in mixed information from other batches. This appears as recalled content containing irrelevant batch numbers. The reason is that batch numbers were not effectively identified during document segmentation, leading to semantic confusion.
- File parsing times out, and the log reports a
PARSE_FILE_TIMEOUT_SECONDSerror. This occurs when processing PDF files containing many images or complex tables, and the default parsing time is insufficient.
How to Verify Configuration
- Upload typical stability study reports (PDF and Excel formats). Verify successful file parsing. Check that batch numbers, time points, and key indicators in the knowledge base's segmented content are accurate.
- Use FastGPT's debugging interface. Construct queries with batch numbers, time points, and indicator names. Verify accurate recall of relevant segments and check the completeness of the recalled content.
- Simulate real-world application scenarios. Pose comparative analysis questions for stability data across different batches and time points. Observe whether the model's answers accurately cite data from the knowledge base. Cross-check the correctness of data units and values.
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.