Data Characteristics in this Domain
Mental health registration documents draw from diverse sources. These include clinical trial reports, non-clinical study reports, pharmaceutical research data, ethical approval documents, and regulatory compliance statements. Data typically combines structured and unstructured formats. Examples are PDF clinical study reports, Word expert consensus documents, image-format brain imaging data with text descriptions, CSV or Excel trial results, and some pure text supplementary notes.
Data update frequency varies. Clinical trial data might update in real-time during a trial, while regulatory documents could revise annually or as regulators require. Document structures are complex, containing extensive specialized terminology, abbreviations, charts, and cross-references. Field and unit specificities demand precise descriptions of disease diagnostic criteria (e.g., DSM-5, ICD-11), scale scores (e.g., HAM-D, PANSS), drug dosage units (e.g., mg/kg), pharmacokinetic parameters (e.g., Cmax, T1/2), and strict statistical indicators (e.g., p-value, confidence interval).
Constraints on Vector Models and Indexing from these Characteristics
Complex data characteristics in mental health registration documents impose specific constraints on vector models and indexing. First, multimodal data (text, image descriptions, tables) requires vector models to effectively fuse different information types, ensuring comprehensive indexing. Second, extensive use of specialized terms and abbreviations (e.g., "SSRIs," "MDD") demands strong domain knowledge understanding from the model to prevent incorrect recalls due to lexical ambiguity or missing context.
Internal document cross-references and complex section structures (e.g., "see Table 3.2.1" or "per Section 4.1 content") mean simple text chunking can disrupt information integrity. Indexing must consider inter-chunk relationships. Additionally, asynchronous data updates, especially frequent revisions to clinical trial reports, necessitate an indexing system that supports efficient incremental updates to maintain knowledge base timeliness. Precision requirements for scale scores and statistical indicators mean vector representations must distinguish subtle numerical differences and statistical significance; otherwise, critical judgment errors could occur.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Size | 500–800 characters | Mental health documents are highly specialized with strong contextual relevance. Chunks that are too short easily break semantic flow; chunks that are too long introduce irrelevant information. |
Overlap Size | 100–150 characters | This ensures sufficient contextual information is retained at chunk boundaries, especially for discussions involving complex logic. |
Recall Count | 8–12 items | The rigorous and multi-dimensional nature of mental health registration data requires a higher recall count to cover comprehensive information. |
Similarity Threshold | Calibrate by measurement | This requires multiple rounds of testing based on the specific vector model and data distribution to balance recall and precision, avoiding omissions or false positives. |
Rerank Return Count | 3–5 items | After initial recall, a secondary reranking focuses on a small number of highly relevant, high-quality items to improve the usability of the final result. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Clinical report files for mental health are often large and contain complex charts and tables, which can lead to longer parsing times. |
Three Common Mistakes
- Indexing progress stalls, showing "processing." This occurs when parsing large PDF files or files with many images times out, preventing chunking completion.
- Knowledge base query results show deviations in numerical information regarding specific scale scores or drug dosages. This happens because the vector model treats numerical data as ordinary text during vectorization, failing to distinguish units or statistical significance.
- After upgrading FastGPT, some previously functional CSV files fail during indexing. This typically results from changes in the new version's file parser or chunking strategy, leading to incompatibility with older data formats or specific encodings.
How to Verify Configuration
- Select different types and lengths of registration documents. Perform a full index to verify all documents are successfully chunked and stored, with no error logs.
- Execute multiple retrievals for queries containing specialized terms, scale scores, and drug dosages. Check if recall results include the desired precise information and assess contextual completeness.
- Randomly select indexed documents from the knowledge base. Use the system interface to review their chunking, ensuring chunk size and overlap strategies meet expectations, especially for boundary handling of charts, tables, and text descriptions.
- Compare the number and relevance of retrieval results at different
Similarity Thresholdvalues. This identifies a threshold range that effectively balances recall and precision.
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.