Vector Models and Indexing for Mental Health Products

Data Characteristics

Product and reagent consultation data in the mental health domain primarily originates from pharmaceutical companies' clinical trial reports, drug inserts, academic papers, review reports published by the National Medical Products Administration (NMPA), and approval documents from international authorities like the FDA and EMA. This data updates infrequently, typically quarterly or annually, coinciding with new drug launches, expanded indications, or clinical research advancements. Document structures are complex, containing extensive unstructured text such as treatment descriptions, side effect lists, and drug interactions. Common fields and units include dosage units (mg, g, ml), frequency units (times/day, week), treatment duration units (weeks, months), and mental health scale scores (e.g., HAM-D, PANSS).

Constraints on Vector Models and Indexing

The complexity and specialized nature of mental health product data impose specific requirements on vector models and indexing. Long texts, polysemy, and dense technical terminology make traditional keyword matching inefficient for retrieval. For example, the same mental symptom may have subtle differences in various contexts. Therefore, vector models must capture deep semantic associations and distinguish subtle contextual nuances. Infrequent document updates mean that after initial index construction, incremental update strategies are crucial to avoid re-indexing large amounts of unchanged content. Additionally, the mix of structured data like mental health scales and unstructured text requires vector indexing to effectively integrate different information types, considering both numerical and textual semantics during retrieval.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters` (characters)	Paragraphs in mental health literature often contain complete concepts. Overly short chunks can lose context, while overly long ones add irrelevant information.
`Chunk Overlap Length` (Chunk Overlap Length)	`100–150 characters` (characters)	Ensures contextual continuity between adjacent chunks, especially when describing treatment procedures or side effects.
`Recall count` (Recall Count)	`8–12 entries` (items)	Given the complexity of disease descriptions and product characteristics, increasing the recall quantity appropriately improves coverage.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	The domain's high specialization requires a higher similarity to ensure the precision of recall results.
`Rerank result count` (Rerank Return Count)	`3–5 entries` (items)	After a higher recall count, the reranking model filters out the most relevant few results.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Ensures sufficient time for file parsing when processing large clinical reports or drug inserts.

Common Pitfalls

Query results show a large amount of irrelevant drug information. This manifests as returned document snippets deviating significantly from the user's query topic. The reason is that the vector model's training data is insufficient to distinguish subtle differences between mental health drugs and other medications, leading to poor generalization.
Some professional terms or scale names cannot be effectively recalled. This manifests as user queries for specific terms failing to retrieve documents, even when the terms are present. The reason might be that the tokenization strategy is not optimized for the psychiatric domain, or synonymy and different forms of vocabulary were not fully considered during indexing.
Knowledge base disk usage is abnormally high. This manifests as the vector_storage_size value returned by the GET /api/v1/kb/stats interface exceeding expectations. The reason is that file chunking granularity is too fine, generating too many redundant vector blocks, or historical versions were not effectively cleaned up.

How to Verify Configuration

Construct typical question-answer pairs for core mental illnesses (e.g., depression, schizophrenia) and their common medications. Test the relevance and accuracy of recall results, and evaluate the professional quality of the recalled content with domain experts.
Select documents containing complex information such as mental health scale data and clinical trial results. Perform queries at different granularities to check if the vector index can capture both numerical and textual semantics, and verify that the returned results include key data points.
Monitor the vector_storage_size metric to ensure the knowledge base's disk usage is within a reasonable range. Regularly check the ratio of chunk_count to file_count to determine if the chunking strategy is appropriate.

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.