Data Characteristics
Tender listing data in the biomedical industry originates from regional drug and medical consumable procurement platforms and medical institution procurement announcements. This data updates frequently; some provincial platforms update daily, while municipal or hospital-level platforms update weekly or monthly. Document structures are typically structured or semi-structured, presented as PDFs, Excels, or web tables. Key fields include product name, manufacturer, registration number, specifications, listed price, procurement quantity, winning bidder, and effective date. Price fields may have various units (e.g., yuan/box, yuan/piece, yuan/tablet), and prices for the same product can vary by region. Some tender announcements also contain unstructured text descriptions, such as technical parameters and quality standards.
Constraints Imposed by These Characteristics on Workflow Orchestration
The high update frequency of tender listing data requires workflows to support automated fetching and incremental updates, avoiding reprocessing historical data. Diverse document formats necessitate flexible data parsing modules, such as OCR and table extraction for PDFs, and structured parsing for Excel and web tables. Inconsistent price units and regional variations mean the workflow's data cleaning stage must incorporate unit standardization and regional matching logic. Extracting unstructured technical parameters requires Natural Language Processing (NLP) capabilities to identify key technical indicators and descriptions from text and link them to structured data. Furthermore, data originating from disparate sources demands multi-source data integration modules within the workflow to ensure data completeness and consistency.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
FETCH_INTERVAL_HOURS | 4 hours | Matches update frequency of most provincial listing platforms, ensuring data timeliness. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large PDFs or complex tables. |
CHUNK_SIZE | 800 characters | Balances semantic completeness of long texts with vector retrieval efficiency. |
OVERLAP_SIZE | 100 characters | Ensures contextual continuity at chunk boundaries, improving recall accuracy. |
SIMILARITY_THRESHOLD | Calibrated by measurement | Ensures relevance of retrieved results; requires adjustment based on specific data distribution. |
MAX_DOCS_PER_QUERY | top 10 | Balances retrieval efficiency and coverage, reducing interference from irrelevant results. |
Common Pitfalls
- Data fetching module experiences connection timeouts or parsing failures due to updated anti-scraping mechanisms or changes in target website page structures.
- Workflow execution time significantly increases because database connection plugins are not optimized, leading to repeated connection establishments or inefficient queries.
- The AI model in the
classifymodule fails to correctly identify product categories because model training data does not adequately cover newly emerging or niche tender product types.
Verification Steps
- Check workflow log outputs to confirm all data fetching module status codes are
200and no connection timeout errors occurred. - Compare cleaned data with raw data, verifying the accuracy and consistency of key fields (e.g., product name, price, specifications).
- Run multiple simulated queries to confirm the agent's responses to different tender listing product inquiries are accurate and include expected key information.
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.