Knowledge Base Retrieval and Recall for Market Access

Data Characteristics

Market access registration and declaration documents in the biopharmaceutical sector originate from various sources. These include regulatory documents, guidelines, and technical review reports from regulatory bodies such as the National Medical Products Administration (NMPA), FDA, and EMA. They also encompass internal company submissions like clinical trial data, non-clinical study reports, and manufacturing process documents. Update frequencies vary; major regulatory document revisions might occur every few years, while technical guidelines or specific requirements could see annual minor adjustments. Document structures are complex, often in PDF, Word, or XML formats, containing numerous tables, figures, and specialized terminology. Fields and units are highly specialized, for example, pharmacokinetic parameters like Cmax, AUC, and t1/2 in pharmaceutical research, or NOAEL and LD50 in toxicology studies, alongside various dosage units (mg/kg, g/day) and time units (h, day, week).

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The highly specialized nature and complex document structures of market access materials challenge knowledge base chunking and vectorization. Extensive specialized terminology and acronyms require models with strong semantic understanding to prevent inaccurate recall due to lexical ambiguity or missing context. The timeliness of regulatory documents necessitates knowledge base support for incremental updates and version management, ensuring retrieved information is always current and valid. Key data within tables and figures, if segmented only as text, risks losing its structured information, affecting precise retrieval. Furthermore, different review agencies have varying emphases and format requirements for declaration documents. The knowledge base must differentiate and specifically recall relevant documents for a particular agency; for instance, a query for an FDA guideline should not confuse it with similar NMPA regulations.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`500–800 characters`	Balances the completeness of regulatory clauses with the information density of a single chunk, preventing excessive splitting that leads to loss of context.
`Chunk Overlap Length`	`50–100 characters`	Ensures semantic continuity between adjacent paragraphs, especially when handling long sentences and cross-paragraph concepts.
`Recall Count`	`10–15 items`	Covers a broader range of potentially relevant documents, balancing recall rate with the computational cost of subsequent re-ranking.
`Similarity Threshold`	Calibrated by actual measurement	Determined through A/B testing based on specific corpus and model performance, e.g., `0.75`.
`Re-ranked Return Count`	`3–5 items`	Focuses on a few highly relevant, high-quality results, reducing the processing burden on the subsequent language model.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient parsing time when processing large PDF or Word documents, preventing timeouts.

Three Common Mistakes

When calling via API, some questions fail to find knowledge base results, appearing as empty responses or generic replies. This occurs because the maxContext parameter is set too low, providing insufficient context to the language model to trigger knowledge base retrieval.
After abnormal knowledge base document training, attempting a one-click re-training might fail due to UPLOAD_FILE_MAX_SIZE limits or backend processing timeouts. This manifests as partial or complete failure of batch upload tasks, with logs indicating oversized files or 504 Gateway Timeout.
After uploading PDF files containing complex tables and figures, retrieval fails to accurately recall data from within the tables, only recalling descriptive text around them. This happens because the file parser does not effectively extract tabular data from unstructured text.

How to Confirm Correct Configuration

Select a batch of representative professional queries (e.g., "ICH Q7a requirements for manufacturing equipment," "FDA 21 CFR Part 11 electronic record compliance"). Validate the recall results on the debugging page, observing if the Recall Count matches expectations and checking the Similarity scores for each result.
Perform precise retrieval for documents containing complex tables and specialized parameters. Verify that the recalled results accurately include key data points within tables or figure descriptions.
Simulate query scenarios for different regulatory agencies, for example, simultaneously querying NMPA and EMA requirements for biological product manufacturing quality management. Verify that the knowledge base can differentiate and prioritize documents from the corresponding agency.
Regularly track newly added or updated regulatory documents. Import them into the knowledge base and test to confirm that indexing and recall functions for new content work correctly, without PARSE_FILE_TIMEOUT_SECONDS or other anomalies.

The values provided are common starting points. They 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.