Data Characteristics in this Category
Retail chain enterprises require diverse data types for registration and declaration document preparation. This includes procurement contracts for pharmaceuticals, medical devices, and health foods; GSP/GMP certification documents; quality management system documents; batch inspection reports; adverse reaction monitoring reports; employee training records; store layout diagrams; and scanned or electronic copies of various licenses. Data sources are dispersed across suppliers, drug regulatory authorities, internal quality management departments, and human resources departments. Data update frequencies vary; for example, batch inspection reports update with each new batch received, GSP/GMP certifications may update annually or every few years, and policy documents are released irregularly by regulatory bodies. Document structures include both standardized forms and extensive unstructured text, such as process descriptions in quality system documents and detailed descriptions in adverse reaction reports. Fields and units involve drug batch numbers, production dates, expiration dates, storage conditions, measurement units (e.g., mg, ml, tablets, boxes), and various certificate and approval numbers, all demanding strict accuracy and consistency.
Constraints Imposed by these Characteristics on "Deployment and Upgrades"
The characteristics of retail chain registration and declaration documents impose specific requirements on FastGPT's deployment and upgrade processes. Dispersed data sources with varying update frequencies mean the knowledge base must support multi-source data ingestion and incremental update mechanisms to avoid duplicate imports and data redundancy. The presence of extensive unstructured text requires FastGPT to have robust generalization capabilities in text chunking and embedding model selection to ensure accurate information extraction and retrieval. Registration and declaration documents demand high accuracy for fields, consistency for units, and meticulous version management. Therefore, knowledge base construction must prioritize data cleaning, standardization, and version control. Furthermore, due to sensitive commercial and compliance information, the security of the deployment environment, data isolation, and permission management must be strictly ensured during upgrades to prevent data leaks or unauthorized access. When upgrading the knowledge base, consider how to smoothly migrate old data and ensure compatibility and stable performance of new models with historical and newly added data.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large scanned PDFs or bulk uploads |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing complex or lengthy documents |
chunkSize | 800–1200 characters | Balances semantic completeness and retrieval efficiency for unstructured documents |
overlap | 100–150 characters | Ensures context continuity at chunk boundaries, reducing information loss |
recall_top_k | Top 10 entries | Improves recall rate, covering more relevant information |
maxContext | 4000 token | Adapts to longer paragraphs and complex descriptions in declaration documents |
Three Common Pitfalls
- Knowledge base search response times increase significantly due to unoptimized knowledge base indexing or embedding models mismatched with data characteristics.
- Some field values are missing or incorrectly formatted in retrieval results, caused by insufficient cleaning and standardization during data preprocessing.
- After a knowledge base upgrade, some documents fail to parse or retrieve correctly, indicating compatibility issues between old and new parsers or models.
How to Verify Configuration
- Select typical registration and declaration documents, upload and parse them into the knowledge base. Verify complete content import and assess the reasonableness of chunking.
- Query key regulatory terms, product parameters, or process descriptions. Validate the accuracy and completeness of retrieval results and check the source of returned knowledge snippets.
- Monitor the average response time for knowledge base retrieval. Compare it against pre-upgrade or baseline performance to ensure performance remains within acceptable limits.
- Check system logs for persistent error messages, especially those related to file parsing, database connections, or model inference.
The values provided are common starting points and should be measured against specific 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.