Data Characteristics for This Category
Clinical trial data from public bidding platforms primarily originates from public resource trading platforms, medical institution websites, and third-party information platforms. Update frequency is typically high, with new projects or status updates occurring daily or weekly. Documents are mostly PDFs, with some Word documents or web pages. Content structure is relatively fixed. Key fields include project name, sponsor, research institution, indication, study phase, enrollment numbers, principal investigator, publication date, deadline, and detailed inclusion/exclusion criteria. Enrollment numbers are usually in "persons." Timeframes are in "days," "weeks," or "months." Medical parameters like dosage and biomarkers have diverse units such as "mg," "g/dL," or "U/L," requiring precise identification.
Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"
The characteristics of public bidding data impose specific requirements on multi-turn conversation and prompt design. First, the diverse sources and unstructured nature (PDF documents) of the data require robust document parsing capabilities to accurately extract key information. Second, high update frequency means the conversation system needs to regularly refresh its knowledge base to ensure users receive the latest information. Prompts should guide users to specify query timeframes. The fixed document structure, despite numerous fields, necessitates prompts that precisely guide users to focus on specific fields, for example, asking "Who is the sponsor?" or "What is the enrollment number range?" Furthermore, the complexity of medical parameter units requires prompts to recognize and process different units, preventing pre-screening deviations due to unit confusion. When user query conditions involve multiple complex medical indicators, multi-turn conversations must progressively guide users to complete all necessary information, avoiding excessive information in a single turn.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4096 tokens | Balances long document context and conversation turns, reducing truncation risk |
Chunk size (Segment Length) | 800 characters (characters) | Ensures each segment contains complete key information units |
Recall count (Recall Count) | Top 8 entries (top 8) | Covers more potentially relevant document snippets, improving recall rate |
Similarity threshold (Similarity Threshold) | 0.75 | Filters out low-relevance content, ensuring precision of recalled content |
Rerank result count (Rerank Return Count) | Top 3 entries (top 3) | Refines final results, focuses on most relevant information, improves response speed |
queryRewrite | true | Optimizes user colloquial queries, enhances search effectiveness |
Three Common Mistakes
- Query results are empty. This may occur due to document parsing failure or inaccurate key field extraction, leading to a mismatch between the knowledge base and the user query.
- In multi-turn conversations, the model fails to remember previous query conditions, leading to repetitive questions or information omissions. This usually happens if
maxContextis set too low or if the dialogue management logic does not correctly handle historical conversation states. - When querying a specific medical indicator, the model returns results with inconsistent or unrecognized units. This stems from insufficient standardization in the knowledge base's handling of medical units or from prompts not explicitly constraining units.
How to Verify Correct Configuration
- Upload clinical trial bidding documents of varying complexity. Check if key fields like "Sponsor," "Enrollment Numbers," and "Indication" are accurately extracted and stored.
- Conduct multi-turn conversation tests. Verify if the model remembers and applies conditions mentioned in previous turns, such as sponsor or study phase, for subsequent queries. Confirm the actual effect of
maxContext. - For queries involving specific medical parameters (e.g., "hemoglobin greater than 110 g/L"), check if the model correctly identifies and applies units for filtering. Verify if it can handle unit conversions.
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.