Data Characteristics
Stability study data originates from internal pharmaceutical quality control labs and contract research organizations (CROs). These CROs conduct accelerated and long-term stability tests. Reports are typically in PDF, Word, or structured formats like CSV or Excel. Data update frequency is irregular, driven by drug development phases, batch production schedules, and regulatory requirements, potentially quarterly, semi-annually, or annually.
Document structures are complex and varied. They often include charts, batch numbers, test dates, storage conditions (temperature, humidity, light), test items (content, dissolution, impurities, pH, moisture), test results, analysis methods, and conclusions. Field names can vary (e.g., "assay" or "Assay"), and units differ by test item (e.g., "%", "μg/mL", "min"). The core data tracks changes in drug quality attributes under various conditions to assess shelf life and storage requirements.
Deployment and Upgrade Constraints
The document-centric nature of stability study data requires FastGPT deployments to prioritize document parsing capabilities. Complex document structures demand robust text, table, and image OCR features from the file_parser module for accurate key field extraction.
Irregular data update frequencies mean cron task scheduling needs flexible configuration, potentially requiring manual triggers or event-driven updates instead of fixed cycles. The diversity of field names and units challenges knowledge base schema design. This requires either pre-processing to standardize data or building in enough flexibility to accommodate varied report terminology.
Sensitive drug quality information necessitates high security, access control, and data encryption for the deployment environment. This ensures the confidentiality of database connection parameters like PG_URL.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Stability reports often contain extensive charts and detailed data, leading to large file sizes. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex PDF document parsing can be time-consuming; this avoids timeouts. |
Chunk size | 800–1200 characters | Ensures contextual completeness, balancing retrieval efficiency and content density. |
Similarity threshold | 0.75 | Improves retrieval accuracy by filtering out irrelevant stability data. |
Rerank result count | Top 5 entries | Core issues typically focus on a few key reports and results. |
maxContext | 16384 token | Accommodates longer report segments, supporting complex trend analysis and comparisons. |
Common Pitfalls
- An empty knowledge base query result might indicate document parsing failure, leading to incorrect key information ingestion or inaccurate field extraction.
- Slow system response when processing numerous stability reports, with
OutOfMemoryErrorin logs, typically points to an insufficientFILE_PARSER_MEMORY_LIMITsetting, which cannot handle large files or high-concurrency parsing tasks. - FastGPT failing to start after a power outage, with
pgormongodbdatabase connection failures, occurs when Docker containers lack persistent storage configuration, resulting in data loss or corruption.
Verification Steps
- Upload a typical stability study report (PDF format). Verify that the knowledge base correctly parses key fields such as batch numbers, test items, and results.
- Parse a report containing charts. Monitor
fastgpt-servercontainer logs to confirm the absence ofOCR_FAILEDorPARSE_ERRORcodes. - Query in natural language about drug content changes under different storage conditions. Cross-reference the returned results to ensure accurate association with relevant report segments and evaluate their relevance threshold.
Note: The values provided are common starting points. Measure them against specific sample data.
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.