Data Characteristics
Tender bidding and listing regulation data in the biomedical sector primarily originates from national and provincial centralized drug and medical device procurement platforms, healthcare security administration websites, and provincial public resource trading centers. This data updates frequently, typically weekly or monthly, with new tender announcements, winning bids, or listing catalog adjustments. Document structures include PDF announcements, Excel lists, or structured XML files. PDF announcements contain key information such as approval numbers, generic names, dosages, specifications, manufacturers, maximum prices, and procurement cycles. This information often appears as unstructured text or image-based tables. Excel lists may provide structured data directly but often require processing for merged cells or inconsistent headers. Field names can vary across platforms; for example, "procurement price" might correspond to "winning bid price" or "medical insurance payment standard." Units also vary, including "CNY/box," "CNY/unit," or "CNY/tablet," requiring standardized handling.
Constraints from "HTTP Interface and External Systems"
The high update frequency of tender bidding data demands that external systems support scheduled fetching and incremental synchronization to maintain knowledge base timeliness. Diverse document structures, especially numerous unstructured PDF announcements, challenge HTTP interface data parsing capabilities. This requires complex document preprocessing after data retrieval from external systems, including OCR recognition, table extraction, and text parsing. Inconsistent field names and diverse units across platforms necessitate strict field mapping and unit standardization during data import or API calls to avoid semantic confusion. For instance, price fields require clear currency and drug quantity units to prevent data errors due to unit mismatches. Additionally, some platforms may implement anti-scraping mechanisms or access frequency limits, impacting HTTP interface stability and efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
HTTP_REQUEST_TIMEOUT_SECONDS | 60 seconds | Processing large PDF files or complex web parsing tasks can be time-consuming; this prevents request timeouts. |
MAX_FILE_SIZE_MB | 20 MB | Most tender announcement PDF files fall within this size range, ensuring successful upload and processing. |
CHUNK_SIZE_TOKENS | 800–1200 characters | This accommodates the length of regulatory clauses, balancing semantic completeness and recall efficiency. |
SIMILARITY_THRESHOLD | 0.75 | Tender bidding and listing regulation Q&A demands high accuracy, improving the relevance of recall results. |
RETRY_COUNT | 3 times | This addresses occasional network fluctuations or temporary unavailability of external interfaces. |
SCHEDULE_INTERVAL_CRON | 0 0 * * 1 | New tender announcements are typically released weekly, ensuring timely data updates. |
Common Pitfalls
- HTTP requests return a 403 Forbidden status code because external data sources may have User-Agent restrictions or IP access frequency limits.
- Parsed price fields are empty or numerically incorrect because price data in PDFs is image-based, leading to OCR recognition failure, or unit conversion logic is flawed.
- Query results do not include the latest policy changes because data fetching frequency is insufficient, or the external system's incremental update mechanism did not trigger correctly.
Verification Steps
- Regularly check the update timestamps of tender bidding regulations in the knowledge base to ensure consistency with external data source publication times.
- Randomly select 5-10 complex queries and compare FastGPT's responses with key information in the original regulation documents to verify field parsing and content accuracy.
- Monitor HTTP interface call success rates and response times through FastGPT's logging system to ensure interface stability and absence of significant delays.
Note: The values provided are common starting points. Measure them 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.