Data Characteristics for This Category
Bidding and listing product data primarily originates from public resource trading platforms at various levels, medical institution procurement information networks, and some third-party data service providers. This data typically publishes in structured or semi-structured formats, such as Excel spreadsheets, PDF documents, or web content. Update frequencies vary significantly; national platforms might update quarterly, provincial or municipal platforms monthly or weekly, and some urgent procurement information publishes in real-time. Document structures commonly include fields like product name, specifications, manufacturer, registration certificate number, listed price, purchasing unit, purchase quantity, and bid winning date. Price units are usually precise to two decimal places, and quantity units cover boxes, sticks, bottles, tablets, etc. Some fields might contain irregular characters or non-standard descriptions.
Constraints from These Characteristics on "Forms and Interactions"
The multi-source and non-uniform nature of bidding and listing data requires form designs to adapt to diverse data input formats, avoiding rigid limitations of a single template. The uncertain update frequency necessitates interaction flows that include clear data timeliness prompts, guiding users to the latest information and supporting periodic or on-demand data refresh mechanisms. Complex fields and units within documents demand higher validation logic for forms, such as format validation for registration certificate numbers, reasonableness checks for price ranges, and standardization of quantity units. Furthermore, non-standard descriptions in some fields can make precise matching difficult during queries. Therefore, fuzzy matching or synonym mapping interaction features are necessary to improve query success rates.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Bidding and listing files often contain large amounts of historical data or images, requiring a larger upload capacity. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large file parsing is time-consuming; this prevents parsing failures due to timeouts. |
Chunk size | 800–1200 characters | Balances information completeness with model processing efficiency, preventing overly long texts from hindering understanding. |
Recall count | Top 5 entries | Ensures the breadth of initial recall results, providing sufficient candidates for subsequent re-ranking. |
Similarity threshold | 0.75–0.85 | Balances recall precision and recall rate, reducing interference from irrelevant results. |
Rerank result count | Top 3 entries | Focuses on the core information most likely to be of interest to the user, reducing redundant display. |
Three Common Mistakes
- After uploading large Excel or PDF files, the system remains unresponsive for an extended period or reports a parsing failure. This occurs because the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too short, failing to cover the entire time required for large file processing. - When users query product names or registration certificate numbers, even if relevant data exists, they often receive a "no relevant information found" response. This might be due to a
Similarity thresholdset too high, or insufficient configuration of synonym mapping, leading to a mismatch between query terms and non-standard descriptions in the data. - After a user uploads a file and it is parsed, some critical fields like "listed price" or "purchase quantity" display as empty in the form. This happens because field names in the data source do not match the system's preset parsing rules, or multiple unit representations exist but have not been uniformly processed.
How to Confirm Proper Configuration
- Upload bidding and listing files from different sources and in various formats (Excel, PDF, web screenshots). Confirm the system parses them correctly and extracts all key fields.
- Simulate user interactions with real query terms (including typos, abbreviations, aliases) in multi-turn conversations. Evaluate the accuracy and relevance of the system's responses and observe for "not found" prompts.
- Check the display of parsed data in the form. Verify that numerical fields like price and quantity are extracted correctly, units are consistent, and no key fields are missing or parsed incorrectly.
- Select highly updated regional listing information. Test if the data refresh mechanism works as expected and if new data is promptly incorporated into the knowledge base for querying.
The values given are common starting points and should be measured against the reader's 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.