Data Characteristics for This Category
Biomedical tender listing data originates from government procurement platforms, medical institution procurement centers, and third-party information providers. This data updates frequently, typically weekly or monthly, with new tender announcements, award results, or product listing information. Document structures are primarily unstructured text and semi-structured tables, such as PDF tender announcements, Word document tender specifications, and Excel product listing sheets. Fields include product name, manufacturer, specifications, unit, listed price, procurement quantity, awarded institution, announcement date, and procurement cycle. In addition to common quantity units (boxes, vials, bottles), packaging units (large pack, small pack), dosage units (mg, IU), and unique identifiers like registration numbers and approval numbers are often present.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The high update frequency and diverse sources of tender listing data require tool calling to have efficient data crawling and synchronization capabilities to ensure information timeliness. The mix of unstructured and semi-structured documents challenges the robustness of information extraction tools, which must process various document formats and accurately extract key fields. For example, product names may have multiple aliases or abbreviations, and unit expressions might not be uniform. Tool calling parameter configurations must therefore account for fuzzy matching and unit standardization. Furthermore, the timeliness of tender listing information directly impacts business decisions. Tool calling execution results thus require clear retry mechanisms for failures and result validation processes to prevent misjudgments due to outdated data or extraction errors.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 | Ensures complete context when processing lengthy tender announcements or documents, preventing information truncation. |
Chunk size (Segment Length) | 500 characters (characters) | Balances semantic completeness and recall efficiency, adapting to varying paragraph lengths in tender texts. |
Recall count (Recall Count) | Top 10 entries (top 10) | Increases relevant information coverage to handle diverse queries for product names, specifications, etc. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances recall precision and recall rate, reducing irrelevant results while not omitting approximate matches. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | Refines initial recall results, highlighting the most relevant information to improve user experience. |
toolTimeout | 600 seconds (seconds) | Provides sufficient time for complex document parsing and data queries, accommodating network fluctuations or service delays. |
Common Pitfalls
- Observation: AI responses include excessive raw tool execution logs or unnecessary intermediate steps after a tool call. Reason: The tool calling module prints all execution results and intermediate processes by default, lacking fine-grained control over output content, leading to information redundancy.
- Observation: Queries for specific product listing prices return empty or inaccurate results. Reason: Incorrect character encoding settings in the database plugin connection configuration, such as
character_set_clientnot matching the actual database encoding, prevents correct recognition and querying of Chinese product names or units. - Observation: Tool call execution takes too long, or even times out. Reason: External data source interfaces respond slowly, or internal tool processing logic is not optimized. For instance, large files might not be streamed but instead loaded entirely at once.
Validation Steps
- Simulate user queries for different product names, specifications, and procurement regions to verify if tool calls accurately return key information such as listed prices and awarded companies.
- Review tool call logs to confirm if the
toolTimeoutconfiguration adequately covers actual execution times and to observe any failure records due to timeouts. - Use queries containing Chinese product names and units to verify if the database plugin correctly recognizes and returns results, confirming proper
character_setrelated configurations. - Evaluate whether AI responses contain only valuable information for the user, without redundant tool execution details, to assess the effectiveness of output filtering and formatting.
The values provided are common starting points and should be measured 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.