Vector Model and Indexing for Mental Health Regulations

Data Characteristics

Mental health regulation and SOP documents originate from internal medical institution rules, guidelines from national and local health commissions, and clinical practice standards from industry associations. These documents update infrequently, typically a few times a year, with concentrated revisions during policy changes or clinical practice updates. Document structures primarily consist of hierarchical chapters and clauses, often containing extensive specialized terminology, acronyms, and clinical pathway descriptions. Beyond standard dates and version numbers, fields and units include disease classification codes (e.g., ICD-10), drug dosage units (mg, ml), and treatment durations (days, weeks, months). Precision and standardization are critical for numerical values.

Constraints on Vector Models and Indexing

The hierarchical structure and high density of specialized terminology in mental health regulation documents challenge vector model recall accuracy. Complex hierarchies mean simple text segmentation can break context, leading to information loss. The abundance of specialized terms and acronyms requires vector models to deeply understand domain-specific semantics; general models may struggle to capture subtle differences. Document updates are infrequent, but each update can involve critical clause revisions, necessitating an indexing mechanism that efficiently handles partial updates to reduce full re-indexing resource consumption. Furthermore, the need for precise matching of standardized fields like disease classification codes and drug dosages means relying solely on semantic similarity for recall is insufficient. Keyword matching or structured information extraction must supplement it.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Balances contextual completeness with vector model processing efficiency, preventing dilution of key information by overly long texts.
`Chunk overlap` (Segment Overlap)	`100–200 characters`	Ensures contextual continuity at segment boundaries, reducing semantic fragmentation caused by segmentation.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adjust based on actual recall performance to ensure highly relevant documents are retrieved while filtering out irrelevant content.
`Recall count` (Recall Count)	`Top 5–8 items`	Balances recall breadth with the efficiency of subsequent re-ranking, ensuring initial coverage of key information.
`Rerank result count` (Re-ranked Return Count)	`3 items`	Focuses on the most relevant core information, reducing user reading burden and improving answer precision.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accommodates the parsing requirements of potentially large or complex format documents, preventing parsing timeouts.

Common Pitfalls

Knowledge base document upload fails to parse or times out: This often happens when the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, failing to adequately process large or complex regulatory documents.
Answers contain significant irrelevant or low-relevance content after user queries: This may occur if the Similarity threshold (Similarity Threshold) is set too low, leading the vector recall to retrieve many document snippets loosely related to the query.
System fails to provide precise answers for queries on specific disease classification codes or drug dosages: This indicates the vector model's insufficient understanding of such highly structured, exact-match fields, and the indexing strategy does not incorporate keyword matching or structured information extraction.

Verification Steps

Upload a batch of documents covering various mental health conditions and regulation types. Verify that all documents parse and index successfully without errors.
Test with several typical queries, such as "What is the ICD-10 code for schizophrenia?" or "What is the initial dosage for common antidepressant medications?". Check the relevance of the recall results and the accuracy of the answers.
Use FastGPT's knowledge base debugging feature to inspect the vector recall results and re-ranked items for specific queries. Evaluate if the Recall count (Recall Count) and Rerank result count (Re-ranked Return Count) are 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.