Knowledge Base Retrieval and Recall for Clinical Trial

Data Characteristics

CMC (Chemistry, Manufacturing, and Control) research data originates from internal R&D reports, production batch records, quality control reports, pharmaceutical research literature, and supplier technical documents. This data updates infrequently, typically with project phase progression or changes in regulatory requirements. Documents have a rigorous structure, often in PDF, Word, or structured database export formats. They contain numerous charts, chemical structures, process flow diagrams, and detailed experimental data. Fields and units are highly specialized, including compound names, CAS numbers, batch numbers, purity (%), impurity content (ppm), yield (%), stability data (e.g., degradation rate k-value), detection methods (e.g., HPLC, GC-MS), and equipment parameters (e.g., temperature °C, pressure MPa).

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The specialized and rigorous nature of CMC data requires that knowledge base segmentation preserves contextual completeness, preventing critical data from separating from its descriptions. The presence of non-textual information like charts and chemical structures challenges text extraction and semantic understanding, potentially leading to information loss or misinterpretation. Low update frequency means initial knowledge base construction requires ingesting a large volume of historical data at once, ensuring its accuracy. Highly specialized fields and units demand that the retrieval system recognizes and understands the precise meaning of these terms, distinguishing between similar but semantically different concepts. For example, purity data from different batches might require aggregation or comparison, which standard text segmentation might not effectively support.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Ensures each segment contains sufficient contextual information, covering experimental conditions, results, and conclusions.
`Recall count` (Recall Count)	`Top 8–12 items`	Given the complexity of CMC reports, increasing recall improves coverage for subsequent re-ranking.
`Similarity threshold` (Similarity Threshold)	Calibrate empirically	Adjust based on the term similarity within the specific dataset to avoid over-recall or under-recall.
`Rerank result count` (Re-ranked Return Count)	`Top 5 items`	Refines results through re-ranking while maintaining coverage, improving the quality of the final output.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides ample file parsing time when processing large PDF or Word documents.
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Supports uploading detailed research report files containing numerous charts and data.

Three Common Pitfalls

After file upload, some critical charts or chemical structures are not correctly recognized as text, leading to missing information during retrieval. This often occurs because images in PDF or Word documents are not OCR-processed, or the OCR results are of poor quality.
Retrieval results contain numerous irrelevant or low-relevance segments, making it difficult to locate effective information. This might stem from a Similarity threshold (Similarity Threshold) set too low, failing to effectively filter out noise, or a segmentation strategy that does not adequately capture the semantic boundaries of CMC data.
When users query for the purity or impurity content of a specific compound, retrieval results fail to precisely return the specific values for the corresponding batch. This often happens if Chunk size (Segment Length) is too large or too small, causing critical values to separate from their modifying context, or if metadata is not effectively used for filtering.

How to Confirm Proper Configuration

Select a batch of typical CMC reports, including charts, chemical structures, and key numerical values. Upload them to the knowledge base and verify that text extraction results are complete and accurate.
For multiple test queries, such as "stability data for compound X" or "impurity profile for product batch Y," perform retrieval operations and manually assess the relevance of results within the Recall count (Recall Count), determining if they contain the key information required by the query.
Use queries containing specific CAS numbers, batch numbers, or detection method names to verify that the retrieval system can precisely locate document fragments containing these specialized terms. Observe the impact of the Similarity threshold (Similarity Threshold) on the result set.
Test uploading large CMC report files with complex layouts. Check that the file parsing process completes smoothly without Request failed with status code 400 errors or timeouts.

The values provided are common starting points. Measure them 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.