Data Characteristics in this Category
R&D documents in academic promotion scenarios primarily originate from Clinical Study Reports (CSRs), Pharmacovigilance Safety Update Reports (PSURs), Investigator's Brochures (IBs), published academic papers, and conference abstracts. These documents are typically in PDF format and contain numerous figures, tables, and specialized terminology. Update frequency is relatively fixed; for example, PSURs are updated annually or semi-annually, while CSRs are released after trial completion. Document structure is highly standardized, adhering to international guidelines such as ICH GCP. For instance, CSRs have fixed chapter numbering and titles. Fields and units are specific to the biomedical domain, such as dosage (mg/kg), efficacy endpoints (ORR, PFS), and adverse event (AE) grades (CTCAE grades 1-5), often accompanied by complex statistical descriptions.
Constraints Imposed by these Characteristics on "Context and Tokens"
Standardized yet complex document structures require deep understanding of chapter logic and hierarchical relationships during parsing to ensure context completeness. The abundance of specialized terminology and abbreviations demands high precision from the LLM when processing tokens to avoid semantic loss due to improper word segmentation. Parsing figures and tables, once converted to text, significantly increases token counts, potentially exceeding single request limits. Fixed update frequencies make incremental updates and version management crucial, avoiding reprocessing large amounts of unchanged content. The rigor of medical data requires the system to accurately identify and link relationships between different fields, such as drugs, dosages, indications, and adverse reactions. This directly impacts RAG retrieval accuracy and LLM answer generation reliability.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Balances semantic completeness with LLM context window limits, preventing single segments from becoming too long and losing focus. |
Recall count (Recall Count) | 8–12 items | Ensures sufficient retrieval breadth to cover potentially relevant information and address the polysemy of specialized terms. |
Rerank result count (Rerank Return Count) | 3–5 items | Filters the most relevant segments, reducing the burden of redundant information on the LLM and improving response efficiency. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Requires balancing recall and precision based on the text similarity distribution of the specific dataset. |
maxContext | 32000 tokens | Ensures capacity for complex queries and multiple relevant document segments, accommodating the density of medical content. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses long parsing times for large clinical study reports and similar files, preventing parsing interruptions. |
Three Common Mistakes
- LLM response content is empty or incomplete, appearing as
LLM model response empty. This occurs whenmaxContextis set too low, causing input tokens to exceed limits and preventing the LLM from processing the full request. - Retrieval results are unexpected or contain incorrect associations. This happens when
Chunk size(Segment Length) is set too short, breaking up important medical concepts or statistical data and disrupting semantic connections. - Automatic continuous questioning fails, unable to merge outputs from multiple built-in prompts. This indicates that multiple LLM nodes are not correctly chained in the workflow configuration, preventing effective context transfer and aggregation.
How to Confirm Proper Configuration
- Test with clinical study reports containing complex tables and figure descriptions. Verify that the parsed text content is complete and retains key medical information.
- Conduct multiple rounds of questioning on critical fields such as drug dosage and adverse event grades. Check the accuracy and consistency of LLM responses, ensuring no numerical or unit errors.
- Simulate users asking questions from different angles on the same topic. Observe whether the system recalls comprehensive and relevant document segments. Verify if
Rerank result count(Rerank Return Count) effectively filters high-quality content.
The values provided are common starting points. Measure 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.