Knowledge Base Retrieval and Recall for CSO Registration and

Data Characteristics

Contract Sales Organizations (CSOs) prepare registration and submission documents using diverse data sources. These include regulatory documents, guidelines, technical review opinions, drug registration approvals, clinical trial data reports, pharmaceutical research reports, and non-clinical research reports from drug regulatory authorities. Documents are typically in PDF, Word, or Excel formats. They have complex structures, containing extensive specialized terminology, charts, and tables. Regulatory documents update infrequently. However, specific product review opinions and supplementary materials may update frequently based on project progress. Fields and units strictly adhere to pharmaceutical, toxicological, and clinical medical standards. Examples include dosage units (mg/kg), concentration units (μg/mL), time units (h, day), and various pharmaceutical parameters (e.g., Cmax, Tmax, AUC).

Constraints on Knowledge Base Retrieval and Recall

CSO data characteristics impose multiple constraints on knowledge base retrieval and recall. First, the authority and rigor of regulatory documents demand extremely high accuracy in retrieval results; no deviations or misinterpretations are acceptable. Second, diverse document formats and complex internal structures (e.g., nested tables, text within images) challenge text extraction and preprocessing, requiring complete information capture. Varying update frequencies necessitate incremental updates and version management capabilities in the knowledge base to ensure retrieved information is current. Furthermore, highly specialized terminology and parameters, along with potential cross-references between documents, require the retrieval model to understand contextual semantics and associate information from different sources. Traditional keyword matching is insufficient; advanced vector retrieval and reranking mechanisms are necessary to improve recall quality.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Size`	`500–800 characters`	Balances semantic completeness and vector embedding efficiency. Avoids information dilution from excessively long chunks and context loss from excessively short ones.
`Chunk Overlap`	`50–100 characters`	Ensures contextual continuity, reducing semantic breaks caused by chunk boundaries, particularly for regulatory clauses.
`Recall Count`	`8–15 items`	Controls the large model's input length while ensuring coverage, reducing interference from irrelevant information.
`Similarity Threshold`	`0.75–0.85`	Increases the threshold for the rigor of regulatory documents, ensuring high relevance of recall results and reducing noise.
`Rerank Return Count`	`3–5 items`	Further refines initial recall results, focusing on the most core and high-quality segments to improve the precision of the final answer.
`PARSE_FILE_TIMEOUT_SECONDS`	`300–600 seconds`	Addresses parsing time for large PDF files or Word documents with complex charts, preventing parsing timeouts.

Common Pitfalls

Retrieval results contain numerous irrelevant regulations or outdated guidelines. This occurs when the knowledge base does not regularly clean invalid documents or when vector indexes are not updated promptly.
Key parameters or terminology are incorrectly cited in submitted declaration materials. This manifests as empty key fields or inaccurate values in model responses. This may be due to the document parsing failing to correctly identify text within tables or images, leading to an incomplete knowledge base index.
The knowledge base does not become effective immediately after creating a training order. This is because creating a training order only triggers an asynchronous data processing flow. Index construction and updates require time to complete and are not instantly available.

Verification Steps

Select several typical questions. Simulate an engineer's queries. Check if retrieval results accurately point to specific paragraphs in relevant regulations, approvals, or reports. Cross-reference with the original text.
Upload a PDF document containing complex tables and charts. Check the document's chunking in the knowledge base. Confirm that table content and chart text are correctly extracted and indexed.
Update an important regulatory document in the knowledge base. Immediately perform a search. Verify that the new version is preferentially recalled and that the old version no longer appears or has significantly lower priority.
Monitor the update status and timestamps of the knowledge base vector index. Ensure that the index is rebuilt or incrementally updated promptly after data changes.

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.