Data Characteristics for This Category
Tender listing data in the biomedical field primarily originates from government procurement platforms, hospital purchasing systems, and public information released by industry associations. Data updates typically occur monthly or quarterly, covering new product listings, price adjustments, and winning bid announcements. Document structures are predominantly structured data, commonly in Excel, CSV, or JSON formats. Some provinces may provide PDF format listing catalogs, which require OCR recognition and structured extraction. Key fields include product name, manufacturer, specifications, listed price, registration certificate number, medical insurance code, dosage form, and procurement region. For units, prices are usually in "CNY/box" or "CNY/unit," while specifications involve "mg/tablet" or "ml/bottle." Data may contain mixed measurement units, requiring unified processing.
Constraints Imposed by These Characteristics on "Deployment and Upgrade"
The structured nature of tender listing data necessitates a focus on precise field mapping and data cleansing during knowledge base construction. Frequent data updates demand a deployment solution that supports automated or semi-automated data synchronization and incremental update mechanisms to ensure the timeliness of consultation results. The presence of PDF documents requires OCR recognition capabilities and text extraction toolchains in the deployment environment, potentially requiring additional model deployment. Mixed measurement units necessitate unit normalization during knowledge base indexing and querying to prevent recall failures or result deviations due to inconsistent units. Furthermore, tender listing data involves sensitive commercial information, so data security and access control must be considered during deployment.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Considers that a single tender announcement or listing catalog file may be large, containing images or tables. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF files or documents requiring OCR recognition can be time-consuming. |
Chunk size (Segment Length) | 300 characters | Ensures critical tender listing information (e.g., product name, price, manufacturer) remains within the same segment for better recall. |
Recall count (Recall Count) | Top 10 entries (Top 10) | Tender listing data often requires multi-dimensional comparison; increasing recall count helps cover more comprehensive information. |
Similarity threshold (Similarity Threshold) | Calibrate by actual measurement | Product names and specifications in tender listings can be highly similar, requiring careful adjustment for differentiation. |
Rerank result count (Rerank Return Count) | Top 5 entries (Top 5) | After filtering by the reranking model, return a small number of the most relevant results to improve user experience. |
Common Pitfalls
- Knowledge base query results are empty or inaccurate. This manifests as the system returning "No relevant information found." The root cause is often incomplete data cleansing or unstandardized units, leading to a mismatch between query keywords and indexed content.
- After data updates, consultation results still display old information. This occurs when incremental update tasks are not configured or fail to trigger, meaning the knowledge base does not synchronize the latest listing data.
- After deployment, some PDF format tender announcements cannot be parsed correctly, showing garbled or missing content. This is due to the OCR service not being integrated or configured correctly, preventing non-text content from being converted into indexable text.
How to Verify Correct Configuration
- Upload a tender listing PDF file containing complex tables and various measurement units. Check if the knowledge base index content is complete and free of garbling, and verify that all key fields are correctly extracted.
- Perform a simulated incremental data update. Subsequently, query product information that changed both before and after the update. Verify that consultation results reflect the latest data and check the
last_updated_timefield. - Conduct consultation tests for different product names, specifications, and manufacturers. Verify that the system can accurately recall relevant tender listing information and set a qualification threshold based on feedback from business experts.
Note: The values provided are common starting points. It is crucial to measure against your own samples and adjust configurations accordingly.
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.