Data Characteristics
Retail chain registration document preparation involves data from internal management systems, supplier product information, regulatory databases, and notices from drug administration departments. Data updates are frequent, especially for regulatory changes and product batch information. Document structures typically include product manuals, production processes, quality standards, clinical trial reports, sales certificates, and store qualifications. Common fields include generic drug name, brand name, batch number, production date, expiration date, ingredient content, storage conditions, indications, adverse reactions, manufacturer, approval number, sales channels, store code, and regional division. Units strictly follow national standards, such as milligrams (mg), milliliters (mL), tablets (tablet), boxes (box), and degrees Celsius (°C).
Constraints on Model Integration and Configuration
Retail chain data characteristics impose specific requirements on model integration and configuration. Frequent updates to product batches and regulatory information require models to support incremental learning or periodic full updates to prevent outdated outputs. Diverse document structures and fields necessitate robust document parsing capabilities to accurately extract key information from unstructured text and effectively index structured data. Strict unit specifications mean precise identification and use of correct units during information extraction and generation to prevent registration errors due to unit confusion. Additionally, a large volume of store and product data challenges model recall efficiency and storage capacity, requiring optimized indexing strategies and vector database configurations.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Registration documents are often large, containing numerous images and scanned files. This ensures complete document uploads. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances context coherence and retrieval efficiency, accommodating the typical length of regulatory clauses and product manual paragraphs. |
Recall count (Number of Retrieved Items) | Top 10 entries (top 10) | Increases relevant information coverage to handle complex queries involving multiple regulatory clauses and product details. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures the precision of retrieved content and reduces interference from irrelevant information, especially when matching regulatory clauses. |
Rerank result count (Number of Reranked Items) | Top 5 entries (top 5) | Focuses on the most core and critical registration information, optimizing the quality of the final answer presented to the engineer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Accounts for the time required to parse large documents, providing sufficient time for processing and preventing timeouts. |
Common Pitfalls
- The
[FATAL] failed to get licenseerror during model startup typically indicates that the server lacks network connectivity for license verification. - When integrating with external dialogue platforms, the presence of numerous
#*symbols in the returned content indicates that the model's Markdown output is not rendering correctly on the target platform. - Missing product batch numbers or expiration dates in query results stem from the document parsing stage failing to correctly identify all numerical and date format fields, or from these fields not being included in the vectorized index.
Verification Steps
- Upload a complete product manual containing information for multiple product batches. Verify that all batch numbers and expiration date fields are accurately recalled through retrieval.
- Submit a query about a specific regulatory clause. Confirm that the model returns regulatory text consistent with the original document and free of formatting errors.
- Ask a question about a store qualification document. Check if the model accurately extracts and answers key information such as store name, address, and approval number.
- Simulate a complex query covering product ingredients, storage conditions, and indications. Evaluate the completeness and accuracy of the model's returned information, ensuring no critical details are omitted.
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.