Data Characteristics
Psychiatric R&D documents draw from diverse, heterogeneous sources. Literature includes clinical trial reports, drug mechanism of action studies, patient behavioral observation records, imaging data analyses, and genetic research papers. Update frequencies vary; clinical guidelines or drug approval documents may revise annually, while basic research papers publish continuously. Document structures vary. Reports often follow fixed formats, such as Clinical Study Reports (CSR) under ICH GCP guidelines. Research papers adhere to journal standards, including abstract, introduction, methods, results, and discussion sections.
Data fields are highly specific. They include complex pathophysiological indicators (e.g., neurotransmitter levels, genetic polymorphisms, cognitive function scale scores), diagnostic criteria (e.g., DSM-5 or ICD-10/11 codes), and treatment regimen dosage units (mg/day, μg/kg). Documents often contain extensive qualitative descriptions, such as patient interview records and behavioral observation notes, which frequently lack standardized units.
Constraints on Multi-Turn Conversations and Prompts
The complex data characteristics of psychiatric R&D documents impose specific constraints on multi-turn conversation and prompt design. First, heterogeneous document sources and varying update frequencies require the knowledge base to support efficient incremental updates. This ensures the conversational model always uses the latest information. Second, extensive qualitative descriptions and non-standardized units in documents demand refined semantic analysis capabilities for the model to understand and extract key information. For example, for descriptions like "patient reported low mood, accompanied by loss of appetite," the model must accurately associate these with depression-related symptoms.
Third, the specialized nature of diagnostic criteria and pathophysiological indicators requires prompt design to guide the model to focus on specific terminology and relationships, avoiding generalized answers. In multi-turn conversations, users may inquire about the relationship between a gene mutation and the efficacy of a specific drug. This requires the conversational system to remember context and extract relevant data from complex genetic reports. Furthermore, analyzing drug unit conversions and interactions across different dosages requires the model to perform accurate numerical reasoning and unit matching when generating responses.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8000 | Psychiatric documents are long and contain extensive specialized terminology. Increase the context window to maintain semantic coherence. |
Chunk size (Segment Length) | 500 characters (characters) | Balances semantic completeness and recall efficiency. Avoids long paragraphs diluting key information. |
Recall count (Recall Count) | 8–12 entries (items) | Given the strong relevance in specialized documents, increase recall count to cover more potentially related information. |
Similarity threshold (Similarity Threshold) | 0.75 | Domain-specific terms often have high similarity. Increase the threshold to ensure precision of recalled content. |
Rerank result count (Reranked Return Count) | 5 entries (items) | After reranking, take the top few most relevant items to avoid redundancy and information overload. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Large file parsing is time-consuming. Extend the timeout to process large clinical reports or literature sets. |
Common Pitfalls
- Redundant output from multiple AI conversation nodes in a workflow: If multiple
AI Conversationnodes in a workflow are not configured withis_debug_modeset totrueor other mechanisms to suppress unnecessary node output, all node responses will appear in the final chat dialogue, leading to information overload. - Model responses lacking professionalism or exhibiting hallucinations: Prompts that do not sufficiently emphasize specific psychiatric diagnostic criteria (e.g., DSM-5) or drug mechanisms of action can lead the model to generate generalized or inaccurate answers.
connection error: A model service connection interruption or timeout can result from network issues, API rate limiting, or high load on the model service backend. This is particularly common when processing a large number of complex queries.
Verification Steps
- Ask multi-turn questions about core symptom descriptions. Check if the model accurately associates them with corresponding disease diagnostic criteria and treatment plans, and correctly identifies drug dosage units.
- Upload a new clinical trial report. Verify that after the knowledge base updates, the model can cite the latest data from this report in a conversation. This confirms the effectiveness of the knowledge base's incremental update mechanism.
- Test queries involving complex biomarkers or genetic information. Observe if the model can precisely extract and interpret relevant fields from structured documents and perform simple logical reasoning, such as the correlation between genotype and drug metabolizing enzyme activity.
Note: 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.