Context and Tokens for Structured Analysis of Psychiatric

Data Characteristics

Psychiatric R&D document data originates from clinical trial reports, drug mechanism studies, patient medical records, follow-up records, medical imaging analysis reports, and genomic data. These documents update infrequently, typically aligning with clinical trial phases or new drug approval cycles. Document structures are complex, containing extensive unstructured text, tables, and charts. Specific fields include psychiatric scale scores (e.g., HAM-D, PANSS), neuroimaging indicators (e.g., fMRI signal intensity), gene polymorphism sites, and pharmacokinetic parameters. Units involve measurement units (mg/kg), time units (weeks, months), rating scales, and gene sequence identifiers.

Constraints on Context and Tokens

The complex structure and specialized fields of psychiatric R&D documents challenge context understanding. Long clinical trial reports and medical records, if used directly as context, can exceed the model's max_tokens limit. High-density information like scale scores and gene sequences requires the model to precisely identify and associate data. Low document update frequency means higher initial data cleaning and model training costs, but lower subsequent maintenance burden. Furthermore, integrating multimodal data (text, tables, images) increases token encoding and context building complexity, requiring more refined chunking strategies and information extraction techniques.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`800–1200 characters`	Balances semantic completeness of long texts with single-chunk token limits; prevents truncation of critical information.
`Recall count` (Recall Count)	`Top 5–8 items`	Covers dispersed but critical information like psychiatric scales and gene sites, improving recall accuracy.
`Similarity threshold` (Similarity Threshold)	`0.78`	Ensures semantic relevance of recalled content, filters noise, and focuses on specialized terminology.
`Rerank result count` (Reranked Return Count)	`Top 3 items`	Refines the final input context, prioritizing the most core clinical trial data and results.
`maxContext`	`4096`	Adapts to mainstream large language model context windows, accommodating more historical conversations and document snippets.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses parsing demands for large clinical trial reports or complex structured documents.

Common Mistakes

AI dialogue output is unusually brief or lacks critical information. This occurs when maxContext is set too low, preventing the model from receiving the full context.
OneAPI error logs show FATAL] 202. This typically indicates an upstream model API call failure, possibly due to an incorrect API Key configuration or network interruption.
The number of contexts displayed in the workflow is much lower than expected, or specific entity fields are empty. This indicates an improper document chunking strategy, such as Chunk size (Chunk Size) being too small, leading to semantic fragmentation, or pre-processing failing to effectively recognize specific data formats like psychiatric scales.

Verification

Use FastGPT's debugging interface to check the input tokens and output tokens for each Q&A. Ensure they are within the expected range and do not exceed the model's context window limit.
Verify the input Token and output token consumption reported by OneAPI. Evaluate if costs align with expectations based on actual usage.
Conduct multiple rounds of Q&A tests using typical psychiatric R&D documents. Confirm the model accurately extracts key information such as scale scores, gene sites, and drug mechanisms of action.

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.