Data Characteristics
Academic promotion registration data primarily includes pharmacology and toxicology reports, clinical study reports, package inserts, and product registration certificates. Data sources are official documents from regulatory bodies, hospitals, and research institutions. These documents have a relatively fixed update frequency, typically occurring during product listing applications, approvals, or significant changes. Document structures are rigorous, mostly non-structured text in PDF and Word formats. They contain extensive specialized terminology, dosage units, experimental data, and statistical charts. Key fields such as drug generic name, indications, dosage and administration, adverse reactions, and manufacturer often appear in standardized formats, but specific descriptions may vary.
Constraints Imposed by Data Characteristics on Deployment and Upgrade
The rigorous nature of academic promotion materials demands high accuracy in the model's understanding of specialized terminology, especially when handling dosages and unit conversions. The large volume of unstructured documents requires efficient document parsing capabilities during knowledge base construction to ensure accurate text extraction and chunking. The fixed update frequency means incremental update strategies for the knowledge base must be optimized to avoid redundant indexing. Due to sensitive drug information, higher security and compliance requirements are necessary for the model. Deployment must consider data isolation and access control. Furthermore, common statistical charts and complex tables in documents challenge image recognition and table structure extraction functions, impacting knowledge base retrieval quality.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Balances semantic completeness with retrieval efficiency, avoiding overly long or short chunks. |
Recall count (Recall Count) | Top 5 entries (Top 5) | Covers core information and prevents interference from irrelevant content. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances retrieval precision and recall rate, reducing false positives. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (600 seconds) | Accommodates parsing times for large PDF documents, preventing timeouts. |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the upload requirements for single large registration documents. |
maxContext | 8192 | Ensures the model can process longer context information and understand complex reports. |
Common Pitfalls
- The model returns a "400 Bad Request" error, triggered by specific phrases. This occurs when the model misinterprets certain specialized terms or abbreviations as non-compliant input.
- The debugging interface does not display code execution step outputs during workflow orchestration. This happens when the debugging log level is set too low, or when the code execution environment differs from the debugging environment, preventing information capture.
- Knowledge base query results show dosage unit confusion or calculation errors. This is due to incorrect recognition or standardization of various representations for the same unit across different documents during parsing, leading to inaccurate knowledge retrieval.
Verification Steps
- Upload a batch of typical registration documents. Observe the document parsing status to ensure all files are processed successfully.
- Query for key drug information. Verify that the model's returned dosage and administration, adverse reactions, and other fields match the source documents.
- Simulate real user query scenarios. Test the number of retrieved items and similarity scores under different query conditions. Evaluate if the thresholds are appropriate.
- Check the log system. Confirm that no timeouts or memory overflow errors occur when processing complex queries or large files.
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.