Data Characteristics
Tender bidding and listing data in the biomedical industry primarily originates from provincial and municipal public resource trading centers, centralized drug procurement platforms, and internal procurement systems of medical institutions. Data updates frequently, typically weekly or monthly, with new tender announcements, winning bids, and catalog adjustments. Document structures are complex, including tender announcements in PDF, quotation lists in Excel, and procurement documents in Word. Core fields include generic drug name, dosage form, specifications, manufacturer, listed price, purchasing unit, quantity, validity period, and distribution company. Price units can vary (e.g., CNY/box, CNY/syringe, CNY/tablet), and listed prices for the same drug differ by region.
Constraints on Multi-Turn Conversations and Prompts
Diverse and unstructured tender bidding data requires robust document parsing capabilities for accurate knowledge base construction. High-frequency updates necessitate a rapid incremental update mechanism to prevent outdated information. Varying price units and regional differences challenge the model's contextual understanding and precise numerical comparisons. Prompt design must guide the model to focus on specific price units and regional limitations. Documents contain numerous technical terms and abbreviations. The model must correctly identify and explain these in multi-turn conversations to meet engineers' detailed query needs. Due to information sensitivity, the conversation system must restrict the disclosure of non-public information. Prompts must include security policies to prevent the model from generating sensitive or unauthorized content.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Segment Length | 800–1200 characters | Tender bidding documents often contain lengthy descriptive text. This length helps preserve contextual integrity. |
Recall Count | Top 5 | Ensures retrieval of highly relevant information segments while balancing processing efficiency. |
Similarity Threshold | 0.75 | A higher threshold ensures retrieved results are highly relevant to the user query, reducing irrelevant information interference. |
Reranked Return Count | Top 3 | Further refines recall results, focusing on the most core answer segments. |
maxContext | 4096 tokens | Accommodates context accumulation in multi-turn conversations, supporting the understanding of complex queries. |
TEMPERATURE | 0.3 | A lower temperature value makes model output more stable and factual, reducing hallucinations. |
Common Pitfalls
- The conversation system confuses price units or provides incorrect regional prices when answering questions about a drug's listed price. This happens because the knowledge base does not clearly distinguish prices and units for different regions, and the model fails to identify them during retrieval and generation.
- A user asks about the detailed process of a specific tender project, but the system returns general information for other projects. This occurs due to an unreasonable knowledge base segmentation strategy, which fragments the contextual information of specific project processes, preventing the model from making accurate associations.
- In multi-turn conversations, the system fails to respond correctly to subsequent user questions with weak links to previous context. This is caused by setting the
maxContextparameter too low, truncating historical conversation information, and causing the model to lose necessary context.
Validation Steps
- Query listed prices for drugs across different regions and dosage forms. Compare the system's output price units with actual data sources and verify price values.
- Randomly select 10 tender announcements. Ask about key information such as the purchasing entity, quantity, and deadline for each, then verify the accuracy of the system's responses.
- Conduct complex multi-turn queries, including technical terms and abbreviations. Check if the system correctly understands and provides relevant explanations, and evaluate the reasonableness of the
TEMPERATUREparameter setting.
The values provided are common starting points. Measure them against your 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.