Knowledge Base Retrieval for Small Molecule Pharmaceutical

Data Characteristics

Small molecule pharmaceutical regulations and SOP data originate from internal quality management system documents, production process specifications, inspection operation standards, and registration submission materials. Updates to these documents typically align with drug lifecycle management, regulatory changes, and production process optimizations. Updates may occur annually or immediately following significant changes. Documents are usually structured PDFs or Word files. They contain titles, chapters, appendices, figures, and tables. Content includes detailed chemical structures, reaction conditions, quality standards, and analytical methods. Fields often include batch numbers, CAS numbers, content, and impurity percentages. Units include precise values like ppm, mg/mL, ℃, and kPa.

Constraints on Knowledge Base Retrieval and Recall

The structured nature of small molecule pharmaceutical regulation documents requires preserving chapter integrity during knowledge base segmentation. This avoids splitting critical information. Documents contain precise numerical values and specialized terminology. The tokenizer must correctly identify and index these elements for accurate retrieval. The update frequency is relatively fixed. The knowledge base synchronization strategy can combine periodic full updates with incremental updates. This avoids excessive re-indexing costs from frequent small modifications. Cross-references and upstream-downstream relationships between regulatory documents mean retrieval must support multi-document associative queries. This provides comprehensive contextual information. File sizes may exceed conventional limits due to numerous figures and attachments. This requires adjusting file upload configurations.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`1000 MB`	Accommodates regulation documents with many figures and attachments, preventing upload failures.
`Chunk size` (Segment Length)	`800–1200 characters`	Preserves the integrity of regulatory chapter content, reducing semantic fragmentation.
`Recall count` (Recall Count)	`Top 8 entries`	Ensures coverage of multi-faceted regulatory details and associated information.
`Similarity threshold` (Similarity Threshold)	`0.75`	Improves the precision of retrieval results, reducing interference from irrelevant content.
`Rerank result count` (Rerank Return Count)	`Top 5 entries`	Refines the final results presented to the user, improving readability.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient time for parsing large or complexly structured documents.

Common Pitfalls

Receiving an "file size exceeds limit" error when uploading regulatory documents indicates UPLOAD_FILE_MAX_SIZE is too small. It cannot accommodate documents with many figures or attachments.
Specific queries fail to recall relevant regulatory content, even when keywords are present. This may be due to segmentation strategies splitting critical information or the tokenizer failing to recognize specialized terminology.
After upgrading FastGPT, some regulatory Q&A performance degrades, showing inconsistent recall results. This may be because new version default configurations differ from older versions, requiring recalibration of Similarity threshold (Similarity Threshold) or Recall count (Recall Count).

Verification

Upload a typical small molecule pharmaceutical SOP document (e.g., a PDF with figures and attachments). Verify successful parsing and ingestion into the knowledge base. Observe the file processing status.
Perform retrieval tests for specific CAS numbers, batch numbers, or precise numerical values contained in the document. Verify accurate recall of paragraphs containing this information.
Test with complex questions spanning multiple chapters or involving several regulatory documents. Evaluate if recall results are comprehensive and contextually coherent. Adjust Similarity threshold (Similarity Threshold) based on actual needs.

The values provided are common starting points. Measure 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.