Data Characteristics for This Category
Quality document data for pharmaceutical e-commerce primarily originates from batch inspection reports from drug manufacturers, registration approvals from regulatory bodies, qualification certificates from distribution channels, and sales records. This data typically exists as PDFs, scanned images, structured XML files, or database records. Update frequency varies: batch inspection reports are generated with each drug batch, qualification certificates are updated annually or upon specific events, and sales records are real-time. Document structures are standardized. Batch inspection reports usually include fields such as drug name, batch number, production date, expiry date, inspection items, results, and standards. Qualification certificates cover company name, license number, approval scope, and validity period. Data fields and units are highly standardized; for example, inspection results are often expressed as percentages, mg/L, or International Units (IU).
Constraints Imposed by These Characteristics on "Deployment and Upgrades"
The data characteristics of pharmaceutical e-commerce quality documents impose specific requirements on deployment and upgrades. Frequent updates and the large volume of batch inspection reports and qualification certificates demand that FastGPT's deployment includes efficient file parsing and vectorization capabilities to handle continuous data ingestion. The specialized terminology and structured data within documents require the model to accurately understand context and extract key information, influencing model selection and fine-tuning strategies. Document sensitivity and compliance requirements necessitate that the deployment environment meets strict data security and access control standards, often leading to private deployments. During upgrades, key considerations include compatibility with new document formats, the model's ability to recognize new drugs, and the stability of system integration with existing business systems.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 100 MB | Accommodates potentially large batch inspection reports and scanned qualification certificates for successful uploads. |
Chunk size (Segment Length) | 800 characters (characters) | Ensures complete inspection item descriptions are captured when processing inspection reports, while preventing excessive length that could lead to context loss. |
Recall count (Recall Count) | 10 entries (items) | Improves the accuracy of retrieving relevant batch information and qualification certificates by covering more potentially relevant documents. |
Similarity threshold (Similarity Threshold) | 0.75 | A higher similarity is required for critical fields like drug names and batch numbers to ensure the precision of recall results. |
Rerank result count (Rerank Return Count) | 5 entries (items) | Further filters the recalled results to identify the most relevant items for the query, improving the quality of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles the parsing time for complex PDFs or scanned documents, preventing document upload failures due to timeouts. |
Three Common Pitfalls
- Knowledge base data import failures or partial data loss. This manifests as "no relevant information found" or incomplete information when querying specific batch drug information. The cause is often insufficient compatibility of the file parser with specific formats (e.g., encrypted PDFs or low-quality scanned images), preventing correct data extraction.
- Model answers that do not align with actual document content or are "off-topic," especially when processing long batch inspection reports. This can be due to the default large model's insufficient understanding in specialized domains, or improper
maxContextand similar parameter settings leading to context loss with long text inputs. - System upgrades causing abnormal data interface behavior with existing business systems (e.g., ERP or LIMS). This manifests as data synchronization interruptions or format errors. The cause is often a failure to synchronize changes in new version interface protocols or insufficient integration testing.
How to Confirm Correct Configuration
- Randomly select at least 10 documents of different types (batch reports, qualification certificates), upload them to the knowledge base, and individually check FastGPT's backend parsing results to confirm complete and accurate extraction of text content and key fields.
- For the uploaded documents, construct queries containing core elements like drug name, batch number, and inspection items. Verify that FastGPT's answers accurately cite the original document text and align with the document content.
- Perform a complete system upgrade process in a simulated production environment. After the upgrade, execute end-to-end tests including file uploads, knowledge base queries, and API calls to ensure all functions operate correctly and data is accurate.
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.