Knowledge Base Retrieval and Recall for Bidding and Listing

Data Characteristics for This Category

Bidding and listing product data in the biopharmaceutical sector originates from provincial and municipal drug procurement platforms, medical device procurement platforms, and hospital internal procurement systems. This data updates frequently; some provincial platforms update daily, and most update weekly. Document structures typically include fields such as product name, manufacturer, specifications, dosage form, registration certificate number, listed price, purchasing unit, bid award date, and expiration date. Data formats are primarily structured tables, such as Excel or CSV files, with a small number of announcements published as PDFs. Field units are highly standardized; for example, price units are "yuan/box" or "yuan/piece," and quantity units are "box," "piece," or "tablet." Registration certificate numbers follow the unified coding rules of the National Medical Products Administration.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The structured nature of bidding and listing data dictates that knowledge base segmentation should prioritize retaining field integrity to prevent the loss of critical information. High update frequency requires the knowledge base to have an efficient incremental update mechanism to ensure the timeliness of retrieval results. For example, if listed price updates are not synchronized promptly, consultation results may not align with the actual situation. The presence of numerous standardized fields in documents makes precise field-based matching and filtering an important means of improving recall accuracy. At the same time, product names and specifications often have high similarity, which demands strong differentiation capabilities from retrieval algorithms, requiring consideration of synonyms, aliases, and fuzzy matching strategies to handle diverse user queries.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size`	256–512 characters	Retains the integrity of individual product entries, preventing truncation of key fields.
`Chunk Overlap Length`	0 characters	Structured data typically does not require overlap, reducing redundancy.
`Recall count`	Top 10–20 entries	Covers enough potentially relevant products, balancing recall rate with subsequent processing overhead.
`Similarity threshold`	Calibrated by actual measurement	Adjusts based on business scenario requirements for precision, avoiding low-relevance results.
`Rerank result count`	Top 5 entries	Further refines the most relevant results, improving user experience.
`UPLOAD_FILE_MAX_SIZE`	50 MB	Accommodates the upload requirements for large procurement list files.

Common Pitfalls

Symptom: Retrieval results contain a large amount of outdated or expired bidding and listing product information. Reason: The knowledge base's incremental update or expired data cleanup mechanisms are not effectively executed, leading to insufficient data timeliness.
Symptom: When a user queries a specific product, product entries known to exist in the knowledge base are not recalled. Reason: The knowledge segmentation strategy is too aggressive, splitting critical fields like product names and specifications into different segments, affecting the completeness of retrieval matching.
Symptom: API calls to the knowledge base retrieval return a 400 Bad Request error, or the text field is empty. Reason: The file upload interface has specific requirements for document format or content encoding, which were not met, leading to file parsing failure.

How to Verify Configuration

Select a batch of representative expired and in-sale products. Simulate user inquiries and check if the recall results contain outdated product information.
Perform multiple query tests for products with synonyms, abbreviations, or different specifications in their names. Observe whether the recall results can accurately differentiate and return corresponding entries.
Upload a typical bidding and listing file containing various data formats (e.g., Excel, PDF) and complete field information. Check whether the knowledge base successfully imports and correctly parses all key fields.

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.