Knowledge Base Retrieval and Recall for Retail Chain

Data Characteristics for This Category

Biopharmaceutical retail chain product data originates from supplier product catalogs, instruction manuals, regulatory registration documents, and internal procurement and sales systems. Data updates are frequent, triggered by new product launches, batch changes, price adjustments, and inventory fluctuations. Updates can occur daily or even hourly. Product instruction manuals are often in PDF or image format. Their content includes drug ingredients, indications, dosage, contraindications, and adverse reactions, with a relatively low degree of structuralization. Product catalogs and inventory data are typically in Excel or CSV format. Fields include generic name, trade name, batch number, expiry date, manufacturer, retail price, unit (e.g., "box," "stick," "bottle"), and packaging specifications. The standardization of drug units and packaging specifications varies.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

Frequent data updates require the knowledge base to be real-time. Retrieved information must be the latest version to avoid providing outdated or incorrect product details. For unstructured PDF manuals, efficient OCR and text extraction technologies are necessary during knowledge base import. Complex layouts, including tables and text within images, must be handled to ensure content completeness and retrievability. In structured data, precise matching of fields like batch number and expiry date is critical for product inquiries. Fuzzy matching can lead to incorrect recommendations. Inconsistent units and packaging specifications increase query understanding difficulty. For example, if a user queries "a box of ibuprofen," the system needs to understand the specific quantity or specification corresponding to "box." These constraints collectively influence chunking, vectorization, and retrieval strategy choices for the knowledge base.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk Size`	`500–800 characters`	Balances paragraph completeness in product manuals with retrieval efficiency, avoiding overly long contexts.
`Chunk Overlap`	`50–100 characters`	Ensures semantic continuity between paragraphs, reducing the risk of critical information being split.
`Number of Retrieved Chunks`	`3–5 chunks`	Balances query response speed with information completeness, avoiding excessive irrelevant information.
`Similarity Threshold`	`0.75–0.85`	Ensures high relevance of retrieved results to user queries, filtering out low-quality matches.
`Reranked Results Count`	`2 chunks`	Further refines the most relevant items from retrieved results, improving final output quality.
`Max File Upload Size`	`100 MB`	Accommodates the upload requirements for large product manual PDF files, e.g., `UPLOAD_FILE_MAX_SIZE`.

Common Pitfalls

Symptom: After a user queries a drug batch number, the returned results show an empty or mismatched batch number field. Reason: The knowledge base failed to correctly identify and extract the batch number field from Excel or CSV during data import, preventing proper indexing or vectorization of that field.
Symptom: A user asks about product dosage, and the system returns information inconsistent with the latest version of the instruction manual. Reason: The knowledge base did not synchronize updated product manuals from the supplier in a timely manner, leading to the retrieval of outdated data.
Symptom: A user queries "how much is a box of ibuprofen," and the system cannot provide an accurate price. Reason: During knowledge base chunking, product names, specifications, and price information were separated into different data chunks, preventing simultaneous retrieval during the query.

How to Verify Configuration

Perform simulated queries for key products and frequent inquiries. Check if the retrieved results contain all necessary information and compare them with original data to verify accuracy.
Monitor knowledge base update logs to confirm that new product data, price changes, and other information are successfully imported and indexed at the expected frequency.
Randomly sample user queries. Check the Similarity Score and Number of Retrieved Chunks to assess if retrieval quality meets the expected threshold.
Verify that critical fields such as product name, batch number, and expiry date are complete and correct in the retrieved results.

The values provided 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.