Data Characteristics for This Category
Tender listing data in the biomedical field primarily originates from government drug and medical device procurement platforms, provincial public resource trading centers, and medical institution procurement announcements. Data updates typically occur monthly or quarterly, covering new product listings, price adjustments, and bid award announcements. The documentation structure mainly consists of structured tables, such as Excel, CSV, or tables within scanned PDFs. However, unstructured notices and clarification documents are also present. Key fields include product name, generic name, manufacturer, registration number, dosage form and specification, listed price, purchasing unit, purchase quantity, bid status, and procurement cycle. Units involve monetary values (Yuan), quantities (boxes/syringes/tablets, etc.), and dates (year/month/day). Some data may have inconsistent field naming, missing units, or inconsistent units, especially when integrating across provinces or platforms.
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
Tender listing data primarily consists of structured tables, requiring precise targeting of specific field information in multi-turn conversations. Due to fixed update frequencies and large data volumes, the knowledge base update mechanism must support bulk imports and incremental updates to ensure query timeliness. The presence of unstructured notices necessitates text understanding capabilities to extract key information from complex contexts. Inconsistent field naming and units demand robust prompts, requiring synonym or alias mapping to improve recall rates. In multi-turn conversations, users may filter and compare based on fields such as listed price, manufacturer, or procurement cycle. This requires the system to maintain filtering conditions within the conversation context and combine them logically. For example, after a user asks, "What is the listed price of product X in province A?", they might follow up with, "What about province B?". The system needs to identify and reuse the product name as a core entity.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500 characters (characters) | Balances the completeness of table row information with the processing efficiency of text embedding models, avoiding segments that are too long or too short. |
Recall count (Recall Count) | Top 8 entries (top 8) | Tender listing data retrieval results are often numerous; increasing the recall count improves coverage and reduces omissions. |
Similarity threshold (Similarity Threshold) | 0.75 | For structured data, a higher similarity is required to ensure the accuracy of retrieval results and prevent interference from irrelevant information. |
maxContext | 2000 token | Ensures that multi-turn conversations can accommodate sufficient historical information and retrieval results, supporting complex conditional filtering. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | Reranks the initial recall results to select the most relevant entries, improving answer quality. |
enableVariable | true | Supports dynamic storage and updating of query conditions (e.g., product name, province) in conversations to enable multi-turn filtering. |
Three Common Pitfalls
- Scenario: A user asks, "What is the listed price of product X in Hunan?", but the system returns inaccurate prices, information from other provinces, or indicates that the knowledge base has no results. Reason: The prompt does not effectively guide the model to prioritize matching the province field, or the knowledge base segmentation strategy separates province information from price information.
- Scenario: When a user continuously asks for prices of different products within the same conversation, the system's response time significantly slows down, or it even times out. Reason: The conversation history context is too long, and each query carries excessive redundant information, increasing the model's processing load.
- Scenario: A user enters "XX injection solution", but the system fails to retrieve relevant listing information, even though the knowledge base contains records for "XX injectable". Reason: Insufficient synonym or alias mapping is configured, leading to a mismatch between the user's query term and the standard term in the knowledge base.
How to Verify Correct Configuration
- Select a batch of test cases containing core fields such as product name, manufacturer, dosage form and specification, and listed price. Verify if the system can accurately retrieve and return corresponding information in a single-turn conversation.
- Design conversation scenarios with two to three filtering conditions, such as "Query the listed price of product A in province B, then ask for the price in province C." Check if the system correctly inherits and updates query variables.
- Prepare a batch of query terms that include synonyms, typos, or abbreviations. Test if the system can successfully recall relevant tender listing data through synonym mapping or fuzzy matching.
- Simulate user queries at different times (e.g., after a knowledge base update). Verify that the data returned by the system is the latest version and check if the response time is within an acceptable range.
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.