Data Characteristics for This Category
Bidding and listing regulation documents in the biomedical industry typically originate from policy documents, detailed rules, and operational guidelines issued by government agencies, procurement platforms, or industry associations. These documents are usually updated infrequently, often every few months or even years, coinciding with policy adjustments or periodic reviews. The document structure is complex, primarily in PDF format, containing extensive legal clauses, approval processes, qualification requirements, technical parameters, fee standards, and timelines. Fields and units commonly include generic drug names, dosages, specifications, manufacturers, registration numbers, procurement batches, winning bid prices (units: Yuan/box, Yuan/piece), and listing periods (units: year, month). Documents may also contain non-textual information such as tables and images (e.g., scanned qualification certificates, flowcharts).
Constraints Imposed by These Characteristics on Model Integration and Configuration
The complexity of bidding and listing regulation documents imposes specific requirements on model integration. First, advanced parsing capabilities are needed for tables and images within PDF documents to avoid information loss. Second, the low update frequency means that once parsed, the knowledge base will be stable, but initial construction requires ensuring data quality. The legal clauses and specialized terminology in the documents demand a high level of model understanding and accuracy, requiring precise semantic recall. Numerical fields like price and period may require range comparisons or unit conversions during Q&A, necessitating some numerical reasoning capability from the model. Furthermore, the authoritative nature of the document content dictates that Q&A results must be highly accurate, without vague or incorrect information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters (characters) | Ensures each segment contains sufficient context while avoiding information overload, facilitating model understanding of legal clauses. |
Recall count (Recall Count) | 8–12 entries (items) | Given the strong interrelation of regulation documents, increasing recall helps cover more comprehensive provisions and avoids missing critical information. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Regulation Q&A demands high accuracy; a higher threshold filters out less relevant segments, reducing the risk of incorrect answers. |
Rerank result count (Rerank Return Count) | 3–5 entries (items) | A reranking model can select the most relevant snippets from the recall results, providing more focused answers. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Complex PDF document parsing is time-consuming; increasing the timeout prevents failures due to incomplete parsing. |
maxContext | 3000 Tokens | Legal and regulatory Q&A requires a larger context window to understand complex logic and multiple related provisions. |
Three Common Mistakes
- Symptom: The model misunderstands or completely ignores tabular data in documents. Reason: PDF table parsing functionality is not enabled or incorrectly configured, causing table content to be treated as plain text or skipped.
- Symptom: When users ask questions involving specific numerical values (e.g., "lowest winning bid price"), the model provides vague answers instead of accurate numbers. Reason: Numerical entities are not identified and extracted in the RAG pipeline, or the model has not been fine-tuned for numerical reasoning.
- Symptom: The model cannot answer questions related to image content when processing documents containing scanned images. Reason: The invoked model does not support multimodal recognition, or multimodal functionality is not correctly enabled and configured.
How to Confirm Correct Configuration
- Upload a bidding and listing regulation PDF document containing complex tables and scanned images. Check if table data and image descriptions are correctly parsed into the knowledge base.
- Ask questions about a specific approval process or fee standard within the document. Verify if the model's answers match the original text, especially for accuracy of specialized terminology and numbers.
- Test the relevance of recall and rerank results at different similarity thresholds to ensure highly relevant segments are prioritized.
- Simulate user questions about key fields like "drug registration number" or "listing period." Check if the model can accurately extract and answer these.
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.