Vector Models and Indexing for Respiratory System

Data Characteristics

Registration dossiers for respiratory system diseases primarily come from clinical trial reports, pharmacology and toxicology study reports, manufacturing process documents, quality standards, non-clinical study reports, and post-market safety data. Data update frequency is relatively low, focusing on new drug development and periodic post-market reports. Document structures are highly standardized, adhering to ICH guidelines and national regulatory agency format requirements, such as the CTD (Common Technical Document) format. Documents contain extensive specialized terminology, abbreviations, dosage units (e.g., mg/kg, mcg/mL), time units (e.g., hours, days, weeks), and statistical indicators. Charts and tables are common data presentation forms, containing key efficacy and safety data.

Constraints Imposed by These Characteristics on Vector Models and Indexing

The standardized structure and high density of specialized terminology in respiratory disease dossiers require vector models to deeply understand and differentiate medical terms. This avoids inaccurate information retrieval due to synonyms or near-synonyms. The precision of dosages and units in documents means chunking must carefully preserve the association between critical values and units. Failure to do so impacts subsequent question-answering accuracy. Extensive chart and table content challenges document parsing capabilities, requiring complete text extraction. The lower data update frequency allows for longer index reconstruction cycles, but each update must ensure incremental index accuracy. Additionally, the prevalence of long documents requires vector models to effectively handle long texts and identify logical relationships between different sections to improve retrieval relevance.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size`	`800–1200 characters`	Paragraphs in respiratory system dossiers are typically long, containing complete clinical data or research conclusions. Overly short chunks can fragment context.
`Chunk overlap`	`100–200 characters`	Ensures sufficient contextual overlap between adjacent chunks to preserve semantic information across paragraphs.
`Recall count`	`Top 5–8 entries`	The rigor of dossier data requires recalling enough relevant snippets to cover potential answer information.
`Similarity threshold`	`0.75–0.85`	For highly specialized texts with strict semantic precision requirements, a higher similarity threshold ensures retrieval accuracy.
`embeddingModel`	`text-embedding-ada-002` or `bge-large-zh-v1.5`	Balances understanding of medical terminology and Chinese processing effectiveness, considering local deployment feasibility.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Dossier files are often large, requiring longer parsing times. Extending the timeout prevents parsing failures.

Three Common Mistakes

503 error or text-embedding model call failure: This usually results from misconfigured One API or other proxy services, preventing normal access to the vector model interface. Check API Key, model name, and group permissions.
Critical values and units separated in retrieval results: An overly aggressive document chunking strategy truncates critical information like 10 mg/kg, affecting data accuracy.
Some table content not indexed: The file parser fails to correctly extract table data from PDF or Word documents, leading to missing information. Optimize the file preprocessing workflow.

How to Confirm Correct Configuration

Upload a clinical trial report containing complex tables and specialized terminology. Check if the knowledge base correctly chunks and indexes all critical information.
Ask questions about specific dosages and efficacy data from the report. Verify if the retrieved snippets completely include values and units and can answer accurately.
Simulate queries on the mechanisms of action for various respiratory system disease drugs. Verify if retrieval results cover relevant pharmacological concepts and clinical study conclusions, and check the effect of Similarity threshold.

Note: The values provided are common starting points. Measure performance 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.