Data Characteristics in This Category
R&D documents in metabolism and endocrinology draw from diverse sources. These include clinical trial reports, drug monographs, academic papers, patent literature, and internal research records. Document update frequencies vary. Clinical trial data or recent research findings may update monthly or even weekly. Drug monographs or foundational theoretical documents update less frequently. Documents have complex structures. They often contain numerous charts, formulas, unstructured text descriptions, and specific fields. Examples include subject baseline characteristics, dosage units (e.g., mg/kg, IU), and biomarker values (e.g., HbA1c%, mmol/L). Key information, such as dosage, efficacy indicators, and safety events, is often scattered across different sections.
Constraints Imposed by These Characteristics on "Multi-turn Conversations and Prompts"
The complex structure and specialized terminology of R&D documents in metabolism and endocrinology demand high accuracy for multi-turn conversations and prompt design. The various units of measurement and biomarkers in these documents require the system to accurately identify and compare numerical values. For example, when comparing the effects of different drugs on specific indicators (like blood glucose or insulin levels), the system must recognize unit differences. Inconsistent document update frequencies require the knowledge base to update dynamically and ensure conversations reference the latest versions. Extracting key information from unstructured text and understanding chart data also requires prompts that guide the model toward deep semantic analysis. This avoids merely matching surface-level text.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk size (Segment Length) | 500–700 characters | Metabolism and endocrinology documents often have long descriptive paragraphs. This length balances contextual completeness and recall efficiency. |
Recall count (Recall Count) | Top 8–12 entries | This ensures comprehensive coverage of complex concepts like metabolic pathways and drug mechanisms, preventing critical information loss. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Adjust this based on specific document content and query requirements to ensure high relevance between recall and queries. |
Rerank result count (Reranked Return Count) | Top 5 entries | In multi-turn conversations, refined search results improve user experience and information retrieval efficiency. |
maxContext | 3000 Tokens | Addressing complex questions in metabolism and endocrinology requires a longer context window to maintain conversational coherence. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Clinical trial reports or large review documents can be substantial in size, necessitating support for large file uploads. |
Three Common Mistakes
- The conversation system cannot directly read and parse content from a user-uploaded Feishu online document link. The system by default only supports direct file uploads or API-provided accessible document content. Online links require additional permission configuration or conversion.
- After enabling the "You might want to ask" feature, the conversation fails to provide relevant question suggestions. This may occur if the text segments recalled by the knowledge base are too short or semantically incomplete, preventing the model from effectively generating meaningful follow-up questions.
- Parsing fails when uploading large R&D documents via API (e.g., PDF files exceeding
200 MB). This is often due to aPARSE_FILE_TIMEOUT_SECONDSparameter set too low, causing the file to exceed the allocated parsing time.
How to Confirm Correct Configuration
- Select different types of metabolism and endocrinology R&D documents (e.g., clinical trial reports, drug monographs). Upload and parse them. Verify that all content is successfully indexed, especially key data in charts and tables.
- Design multi-turn conversation test cases for common professional terms, dosage units, and biomarkers found in the documents. Observe if the system can accurately identify, understand, and provide correct answers, such as querying the percentage reduction in
HbA1c. - Simulate actual R&D scenarios. Pose complex queries, such as "Compare the differences between drug A and drug B in terms of insulin resistance in diabetic patients." Check if the system's returned answers are comprehensive, accurate, and can cite the original source.
The values provided are common starting points and should be measured against the reader's 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.