Data Characteristics for this Category
CSO (Chief Scientific Officer) R&D documents in the biopharmaceutical field originate from various sources. These include experimental records, clinical trial reports, patent literature, regulatory documents, project proposals, internal research reports, meeting minutes, and external collaboration agreements. Documents update frequently, especially for projects in early R&D stages, where experimental data and analysis results are generated almost in real-time. Document structures are complex, often containing large amounts of unstructured text, charts, chemical formulas, gene sequences, and specialized terminology. Fields and units are highly specialized, such as dosage units (mg/kg), time units (h, day), concentration units (nM, μM), and various biological indicators and statistical parameters. Documents are typically long, often spanning tens to hundreds of pages.
Constraints on Knowledge Base Retrieval and Recall from these Characteristics
The complexity of CSO R&D documents presents significant challenges for knowledge base retrieval and recall. High update frequency requires the knowledge base to have efficient incremental indexing capabilities to ensure timely retrieval results. The large number of specialized terms and abbreviations in documents demands strong semantic understanding to prevent recall failures due to vocabulary mismatch. The presence of non-textual content like charts and chemical formulas means pure text vectorization is insufficient to capture all key information, requiring multimodal or enhanced text representation. Processing long documents requires a reasonable segmentation strategy to maintain contextual completeness while avoiding overly long segments that dilute key information. Furthermore, strict compliance requirements make the accuracy and traceability of recall results a core consideration; any incorrect recall could lead to R&D direction deviations or compliance risks.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Balances contextual completeness with vectorization efficiency, suitable for lengthy R&D documents. |
Recall count (Recall Count) | Top 5–8 items | Considering the high density of document content, slightly increase recall to cover more potentially relevant information. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Select a higher threshold to ensure recall accuracy, targeting semantic distinctiveness in specialized domains. |
Rerank result count (Reranked Return Count) | Top 3 items | Focus on the most critical key information while ensuring accurate recall. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates the upload requirements for large R&D reports and clinical trial documents. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the time required to parse complex documents (e.g., PDFs with many charts or nested objects). |
Three Common Pitfalls
- During knowledge base file upload, a file might fail due to a parsing timeout even if its size is within the limit. This typically occurs because the document structure is overly complex, containing numerous embedded objects or high-resolution images, leading to extended parsing times.
- Retrieval results show many irrelevant or low-relevance documents. This might be due to a
Similarity threshold(Similarity Threshold) set too low or an unreasonable segmentation strategy, resulting in poor vectorization quality. - Some critical information is not retrieved, even if explicitly present in the document. This often happens because specialized terms or abbreviations in the document are not effectively recognized and expanded, leading to insufficient matching between query vectors and text vectors.
How to Verify Configuration
- Upload various types and lengths of CSO R&D documents (e.g., experimental records, clinical reports). Check if files are successfully parsed and indexed, without parsing timeouts or format errors.
- Use query statements containing unique specialized terms and chemical formula names from the documents. Observe if recall results include the expected highly relevant documents and check if key information in the recalled documents is complete.
- Adjust the
Similarity threshold(Similarity Threshold) parameter. Observe changes in recall count and relevance until a balance between recall rate and accuracy is found. - Manually evaluate recall results. Confirm that the top-ranked documents provide direct answers or key information. Assess whether reranking effectively improves the ordering of the most relevant content.
Note: The values provided are common starting points. Measure against specific samples to determine optimal settings.
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.