Data Characteristics
Stability study data originates from laboratory analysis reports, instrument output files, and batch production records. Data updates occur periodically, typically every 3, 6, or 12 months for sampling and testing. Document structures are primarily structured tables, including fields like batch number, sample ID, test item, test method, test result, unit, test date, and storage conditions. Some data may exist as unstructured batch reports or analysis chromatograms, requiring additional processing. Test results involve various units, such as mass (mg/mL), concentration (%), activity (IU/mg), and physical properties (pH value, turbidity).
Constraints on Deployment and Upgrade
Periodic updates of stability study data require configuring scheduled tasks or external triggers during FastGPT deployment. This ensures timely knowledge base synchronization. The coexistence of structured and unstructured data necessitates robust document parsing capabilities, especially for tabular data and specific report formats. Diverse fields and units require accurate extraction and standardization of key information during model training and prompt engineering. This prevents result discrepancies due to inconsistent units. The deployment environment needs sufficient storage for historical batch data and analysis reports. Strict data quality requirements emphasize the stability of data validation and cleansing modules during upgrades.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large analysis reports with numerous chromatograms and raw data, ensuring smooth file uploads. |
maxContext | 3000 Tokens | Stability studies involve multi-timepoint and multi-batch comparisons, requiring a longer context window for comprehensive analysis. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large PDF or Excel reports, where parsing time can be extended, preventing parsing failures due to timeouts. |
Chunk size | 800 characters | Ensures each text chunk contains sufficient information for semantic understanding while avoiding excessive length that could reduce recall efficiency. |
Recall count | Top 8 entries | Stability evaluation often requires referencing historical data from multiple batches or test items, increasing the recall scope. |
Similarity threshold | 0.75 | Requires a higher similarity match for numerical values and specialized terminology to ensure relevance. |
Common Pitfalls
- No statement output after a Q&A session, requiring details to be opened to view the message. This often results from inconsistent front-end rendering logic and back-end response timing, or the
messagefield being empty in specific states. - Uncaught exceptions in simple applications after an upgrade script. This typically stems from incompatible environment dependencies or functional module conflicts where old configurations or plugins were not updated with the FastGPT version.
- File upload errors during conversations after local deployment. This might be due to incorrect file storage path configuration or insufficient permissions for the FastGPT container to access the host's file system.
Verification Steps
- Upload a stability study report containing multiple tables and chromatograms. Verify correct parsing and extraction of key fields like batch, test item, and result. Compare extracted results with the original document.
- Formulate a question spanning different test time points, for example, "pH change trend for batch X at 6 and 12 months." Verify the model's ability to synthesize information from multiple documents and check if the answer references the correct batch and time point data.
- Simulate high-concurrency calls to the FastGPT API. Observe response times for acceptable performance and check logs for numerous timeouts or memory overflow warnings to assess system stability under high load.
- Perform a minor version upgrade. Run built-in health check scripts and verify core functionalities (e.g., knowledge base queries, file uploads) operate normally after the upgrade. Check for unexpected behavior.
The values given 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.