Data Characteristics
GMP-compliant quality documentation includes Standard Operating Procedures (SOPs), batch production records, inspection records, validation reports, deviation reports, and change control documents. These documents typically exist as PDFs, Word files, or scanned images, stored in internal document management systems or shared file servers.
Document update frequency is relatively stable. SOPs and validation reports may be revised annually or as regulatory requirements dictate. Batch production and inspection records are generated per batch. Document structure is rigorous, including fixed fields like version number, effective date, revision history, approver, content summary, detailed steps, and appendices. Batch production and inspection records contain extensive production parameters and test data, often including specific values, units (e.g., kg, L, ℃, pH, OD values), and identification information such as batch numbers and production dates.
Constraints on Deployment and Upgrade
GMP document compliance requires accurate, traceable content and strict version control. During deployment, FastGPT must reliably and efficiently ingest data from existing document systems. It must correctly parse various document formats, with accurate OCR for scanned images being critical.
The document update mechanism requires FastGPT to identify version changes and perform incremental updates, preventing redundant indexing. For documents with structured data, like batch production records, FastGPT needs to extract key parameters and units. This demands specific entity recognition capabilities during model training and configuration.
Document confidentiality and access control also impose requirements on the deployment environment's security and permission management. During upgrades, new FastGPT versions must be compatible with existing data structures and indexes. Smooth migration is essential to ensure service continuity and data integrity.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large validation reports or SOPs with extensive image content. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient time for OCR and content parsing of complex PDFs or scanned documents, preventing timeouts. |
Chunk size | 800–1200 characters | Balances contextual coherence with information density per segment, suitable for detailed SOP steps. |
Recall count | Top 8 entries | Increases the probability of recalling highly relevant, detailed quality record fragments from a large document set. |
Similarity threshold | 0.75 | Ensures recalled document fragments are highly relevant to the query intent, reducing false positives, especially for regulatory inquiries. |
Rerank result count | Top 5 entries | Further refines recall results, prioritizing the most critical compliance clauses or operational steps. |
Common Pitfalls
- Files remain in an "indexing" state for extended periods, but their content is not retrievable. This often indicates compatibility issues between FastGPT's embedding model and the deployment environment, or insufficient parsing capabilities of the selected model for specific document formats, leading to indexing stagnation.
- The
redisservice in the Docker Compose file fails to start or pull images. This can relate to Docker environment network configuration, image source access permissions, or incorrect image addresses configured indocker-compose.yml. - Query results for production batches or inspection data show numerical discrepancies or missing units. This suggests imprecise pattern recognition during document parsing and information extraction, failing to accurately capture numerical values and their associated units of measurement.
Verification Steps
- Upload typical GMP documents (e.g., SOPs, batch production records). Check if their indexing status shows "completed." Attempt keyword queries to verify accurate content retrieval.
- Index validation reports containing numerous tables and scanned images. Then, ask questions to verify FastGPT can correctly extract key conclusions and data from the reports.
- Simulate a real-world inspection scenario. Input specific compliance questions. Check if FastGPT's returned document fragments accurately point to relevant regulatory clauses or operational steps. Verify the version number is current.
- After a FastGPT upgrade, re-index a representative set of documents. Compare retrieval results with those from before the upgrade to ensure data consistency and no significant degradation in retrieval performance.
Note: 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.