Deployment and Upgrade for Drug Registration Document

Data Characteristics for This Category

Drug registration documents primarily include drug inserts, clinical trial reports, pharmacokinetic data, quality standards, and stability study reports. This data originates from drug research and development and clinical trials. Files are mainly in PDF, Word, Excel, and image formats. They contain extensive specialized terminology, dosage units, experimental parameters, and charts. Data update frequency is relatively low, occurring mainly during new drug applications, supplemental applications, or drug insert revisions. Document structures are complex, deeply nested, and often contain cross-references. Fields and units must strictly adhere to regulatory specifications, such as dosage units like mg/kg, IU, time units like h, min, and various biological measurement units.

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The complex structure and specialized nature of drug registration documents impose specific requirements on RAG system deployment. First, deep document nesting and cross-references demand robust file parsing capabilities to ensure comprehensive knowledge base construction. Second, the low update frequency allows for less frequent full knowledge base rebuilds, but each update can be extensive, requiring efficient incremental update mechanisms. The presence of specialized terminology and standardized units requires accurate identification and semantic preservation during text embedding, preventing information loss due to tokenization or truncation. Furthermore, charts and image data necessitate advanced OCR and multimodal processing capabilities. Deployment must ensure the availability and performance of relevant components.

Configuration Settings

Configuration Item	Recommended Value	Rationale for This Value
`UPLOAD_FILE_MAX_SIZE`	`1024 MB`	Registration documents can have large individual files; this ensures large PDFs or Word documents can be uploaded.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex document structures and extensive text can be time-consuming; this prevents timeouts.
`Chunk size`	`800–1200 characters`	Balances context completeness and embedding model processing capacity, ensuring specialized terms and concepts are not fragmented.
`Similarity threshold`	`0.75`	Ensures precision of retrieval results, reducing interference from irrelevant or low-similarity segments, which is crucial for specialized domains.
`Rerank result count`	`Top 5 entries`	Further optimizes results through reranking based on high-similarity retrieval, improving relevance.
`maxContext`	`4000`	Ensures the model can handle sufficiently long contexts, addressing complex logic and relationships within registration documents.

Three Common Mistakes

After local deployment, team members cannot create knowledge bases or upload files. The interface shows "no permission" or disabled function buttons. This usually happens when the TEAM_MODE_ENABLED parameter is not correctly set to true, preventing team management features from being enabled.
After uploading large PDF documents, knowledge base construction progress stalls for an extended period or an "file parsing failed" error occurs. This often indicates that PARSE_FILE_TIMEOUT_SECONDS is set too low to process large files with many charts and complex layouts.
Query results show incorrect values or missing units for drug dosages or test indicators. This may be due to an overly aggressive text segmentation strategy that separates critical numerical values and units, leading to incomplete information during embedding and retrieval.

How to Confirm Correct Configuration

Upload a PDF drug insert containing multiple tables and images. Check if it can be fully parsed and successfully used to build a knowledge base. Observe if the knowledge base includes table content.
Use a query containing specific dosages (e.g., 10 mg/kg) and mechanisms of action. Verify that the retrieval results accurately present relevant numerical values and units, and check if their context is complete.
Monitor system logs during the file parsing process to confirm that no prolonged timeout warnings or parsing failure error codes appear.

Note: 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.