Data Characteristics
Tender quality documents originate from official websites, including drug administration agencies, medical insurance bureaus, and provincial/municipal public resource trading centers. Data updates frequently, especially with policy adjustments and new product launches. Some provinces update tender information weekly. Document formats vary, including PDF for tender specifications, product registration certificates, quality standards, and inspection reports. Excel or Word formats are used for product catalogs and price lists.
Document structures are relatively fixed. For example, a registration certificate typically includes fields like registration number, enterprise name, product name, specifications, and validity period. Price lists contain product codes, generic names, dosage forms, specifications, listed prices, and manufacturers. Common units include milligrams (mg), milliliters (ml), tablets, vials, and boxes. Unit expressions for the same product can differ by region.
Constraints on Multi-Turn Conversations and Prompts
The characteristics of tender quality documents impose specific requirements on multi-turn conversation and prompt design. High data update frequency necessitates frequent knowledge base updates. Prompts must guide the model to prioritize the latest data, avoiding outdated information.
Diverse document formats, containing both structured and unstructured data, require robust document parsing capabilities. Prompt design must extract key structured information (e.g., product registration numbers, listed prices) and understand unstructured descriptions (e.g., technical requirements in quality standards).
Regional variations in fields and units mean multi-turn conversations involving cross-regional comparisons require prompts to specify regional scope or perform unit conversions. This prevents misunderstandings due to inconsistent units. The specialized nature of document content requires prompts to precisely define terminology, ensuring the model understands specific meanings within the pharmaceutical domain.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Accommodates the context length needed for complex tender documents and multi-turn Q&A. |
Chunk size (Segment Length) | 600 characters | Balances semantic integrity of long documents with recall efficiency. |
Recall count (Recall Count) | Top 8 entries | Covers multiple relevant document segments, increasing information breadth. |
Similarity threshold (Similarity Threshold) | 0.78 | Filters for document segments highly relevant to the tender topic. |
Rerank result count (Rerank Return Count) | Top 5 entries | Refines final results, highlighting the most relevant information. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large PDF tender specifications and product catalog file sizes. |
Common Pitfalls
- The model cites outdated tender information in multi-turn conversations because the knowledge base did not synchronize with newly released tender announcements.
- A user asks "What is the price of product X in Guangdong?", and the model returns a national average price. This occurs when prompts do not explicitly limit the query region.
- After uploading a large PDF quality standard file, the system remains unresponsive or errors out for an extended period. This is because the
UPLOAD_FILE_MAX_SIZEparameter is set too low, causing file upload failures.
Validation Steps
- Simulate user queries about newly released tender announcements. Verify if the model's response cites the latest listed prices and policy terms.
- For a specific product, progressively narrow the query scope in multi-turn conversations to a specific province and dosage form. Verify if the model can accurately extract regional information.
- Upload a PDF document with a size close to the
UPLOAD_FILE_MAX_SIZElimit. Confirm that the document parses and indexes correctly. - Design complex questions involving specialized terminology and units of measurement. Check the model's understanding of these terms and the accuracy of unit conversions during multi-turn interactions.
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.