Knowledge Base Retrieval and Recall for Pharmaceutical

Data Characteristics

Quality documents in pharmaceutical e-commerce originate from drug administration regulations, platform quality management system files, supplier qualification certificates, drug batch inspection reports, cold chain logistics monitoring records, and user feedback/complaint records. These data update frequently. Regulations may be revised annually, drug batch reports are generated in real-time upon procurement, and user feedback is continuous. Document structures vary: regulatory files are typically hierarchical PDFs or Word documents. Internal SOPs (Standard Operating Procedures) are often rich-text Feishu documents or Confluence pages, including flowcharts, form templates, and operation screenshots. Inspection reports are mostly structured or semi-structured Excel or PDF files, with fields like batch number, production date, expiry date, test items, and results, using units such as milligrams, milliliters, and percentages.

Constraints on Knowledge Base Retrieval and Recall

Frequent updates to pharmaceutical e-commerce quality documents require the knowledge base to support efficient document synchronization and incremental indexing, ensuring timely retrieval results. Diverse document formats, especially Feishu documents with images, tables, and flowcharts, challenge document parsers. Parsers must accurately extract text and preserve critical contextual information. Structured information in inspection reports, such as batch numbers, dates, and test results, requires the knowledge base to effectively capture these key entities during vectorization and support field-based precise filtering or aggregate queries during retrieval. Time-series data like cold chain records need time-range retrieval support to quickly locate abnormal records within specific periods. Additionally, for user feedback, the system must identify quality issues related to specific drugs or processes from unstructured text and link them to relevant quality documents.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size`	`500-800 characters`	Balances context completeness and vector recall accuracy. Avoids overly large segments that dilute key information or overly small segments that lose context.
`Chunk Overlap Length`	`80-120 characters`	Ensures semantic continuity between adjacent segments. Prevents critical information from being split due to segment truncation.
`Recall count`	`Top 10-15 entries`	Considers the complexity and diversity of pharmaceutical e-commerce documents. Increases recall quantity to improve relevance coverage.
`Similarity threshold`	`Calibrate by actual measurement`	Adjusts through test sets for specific document types and query scenarios. Ensures high recall while controlling false positives.
`Rerank result count`	`Top 5 entries`	Uses a Reranker model for fine-grained sorting of recalled results. Enhances the accuracy and user experience of the final output.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses the time required to parse large PDFs or Feishu documents with complex charts. Prevents parsing failures due to timeouts.

Common Pitfalls

Symptom: After knowledge base creation, some document content is missing or formatted incorrectly. Reason: The document parser has insufficient recognition capabilities for embedded images, complex tables, or flowcharts in Feishu documents, leading to incomplete text extraction or loss of structural information.
Symptom: Users cannot retrieve relevant inspection reports when querying for "inspection results of a specific drug batch." Reason: During the import of structured or semi-structured documents, the knowledge base did not effectively extract and index key fields like batch numbers or production dates. This prevents queries based on these fields from hitting relevant documents.
Symptom: Timeout errors or file upload failures occur when uploading large files. Reason: UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS parameters are set too low. They cannot support the upload and parsing of large regulatory files or batch reports common in pharmaceutical e-commerce.

Verification Steps

Select representative documents (e.g., regulations, SOPs, inspection reports). Import them into the knowledge base. Use the backend preview function to verify content completeness and correct formatting for each document.
Create test cases for different query types (e.g., regulatory clauses, SOP steps, drug batch numbers, anomaly feedback). Execute queries and evaluate the relevance and ranking of recall results. Ensure accurate targeting of relevant documents.
Simulate document upload and parsing operations under high concurrency. Monitor system logs and resource usage. Confirm that parameters like PARSE_FILE_TIMEOUT_SECONDS effectively handle actual business loads and that no file processing failures occur.

The values provided are common starting points and should be measured 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.