Data Characteristics for This Category
Bidding and listing data originates from provincial and municipal centralized drug procurement platforms, official medical insurance bureau websites, and announcements from relevant industry associations. This data updates frequently, typically weekly or monthly. Updates can be more frequent during urgent policy changes. Document structures primarily combine tables and unstructured text.
Table data includes fields such as generic drug name, dosage form, specification, manufacturer, winning bid price, listing status, and procurement cycle. Price units are typically "CNY/box" or "CNY/unit". Unstructured text often consists of policy interpretations, application requirements, and dispute resolution processes. This text involves extensive legal and medical terminology.
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
The high update frequency of bidding and listing data requires the knowledge base to quickly synchronize and index information. This ensures information timeliness in multi-turn conversations. Mixed data structures necessitate optimized document parsing strategies. This ensures effective extraction of critical values from tables and policy details from unstructured text.
Extensive specialized terminology and abbreviations demand more robust prompt construction. Contextual understanding and glossaries are needed to prevent ambiguity or misunderstanding in multi-turn conversations. Additionally, when retrieving numerical fields like price and specification, unit matching and numerical ranges require careful attention. This prevents result deviations due to inconsistent units.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 | Accommodates complex policy texts and multi-turn conversation context lengths, preventing information truncation. |
Chunk size (Segment Length) | 500–800 characters | Balances single-segment information completeness with retrieval efficiency, considering both tabular and text content. |
Recall count (Recall Count) | Top 8 | Ensures coverage of bidding announcements, policy interpretations, and related drug information, improving recall rate. |
Similarity threshold (Similarity Threshold) | 0.75 | Filters out low-relevance results, reducing noise. Suitable for highly specialized data. |
Rerank result count (Reranked Return Count) | Top 5 | Focuses on the most relevant information, enhancing model processing efficiency and response accuracy. |
Model Temperature (temperature) | 0.3 | Reduces model divergence, ensuring fact-based responses and minimizing hallucinations. |
Three Common Pitfalls
- Incorrect drug prices or listing statuses in conversations: This occurs when knowledge base data is not updated promptly, or when document parsing fails to correctly extract the latest values from tables.
- Model inability to understand user queries about "dispute appeal processes": This happens when legal terms and process descriptions in unstructured text are complex, and prompts fail to effectively guide the model to understand their logical relationships.
- 500 error when importing documents: This is typically due to incompatible file encoding or format, or when the file size exceeds the
UPLOAD_FILE_MAX_SIZElimit.
How to Verify Correct Configuration
- Simulate user queries for the latest listed drug prices and statuses based on recently updated bidding announcements. Check if responses align with source documents.
- Input complex questions involving policy interpretations and process inquiries. Evaluate the model's ability to accurately identify key information and provide clear, structured responses.
- Attempt to upload bidding announcements containing various table and text formats. Check if document import and segmentation are successful, with no error messages.
- Analyze conversation logs to assess the model's contextual understanding in multi-turn conversations and its ability to correctly handle specialized terminology.
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.