Vector Model and Indexing for siRNA Nucleic Acid Drug

Data Characteristics

siRNA nucleic acid drug clinical trial data originates from clinical trial protocols, subject screening logs, medical records, laboratory test reports, and adverse event reports. These documents are typically in PDF, DOCX, or structured database export formats. They contain extensive unstructured text, tabular data, and medical terminology. Data updates are frequent, especially during an ongoing trial, as subject enrollment, test results, and adverse event information are continuously recorded. Document structures vary and are complex. For example, a clinical trial protocol may include sections like background, objectives, inclusion/exclusion criteria, and dosing regimens. Laboratory test reports contain specific test items, result values, and units, such as ng/mL or IU/mL. Patient genetic background, specific gene expression levels, and viral load are critical fields for screening.

Constraints on Vector Models and Indexing

The complexity and diversity of siRNA nucleic acid drug clinical trial data impose specific requirements on vector model and indexing construction. The large number of medical terms and abbreviations in documents requires vector models with strong domain understanding to avoid semantic drift or information loss. Diverse document structures mean text chunking must consider the boundaries of sections, tables, and lists to ensure contextual completeness. For example, inclusion/exclusion criteria are often presented as lists. Chunking too short might truncate individual conditions, affecting recall accuracy. Furthermore, continuous data updates require an indexing system capable of efficient incremental updates and re-embedding. This prevents outdated data from affecting the validity of screening results. High update frequency and potential model replacement needs also necessitate batch re-embedding of the knowledge base.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Length)	`512 characters` (512 characters)	Balances semantic completeness of long texts with vector model processing efficiency
`Overlap Length`	`64 characters` (64 characters)	Maintains contextual continuity and reduces loss of edge information
`Recall count` (Recall Count)	`10 entries` (10 items)	Balances recall breadth with computational resource consumption, covering potentially relevant information
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures high relevance of recall results to query intent, reducing noise
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (600 seconds)	Accommodates parsing time for large PDF documents, preventing timeout interruptions
`embedding_model`	Calibrate by actual measurement	Requires evaluating model performance in the biomedical domain, e.g., `bge-large-zh`

Common Pitfalls

Knowledge base files show "Indexing" for an extended period after upload: This usually indicates that the selected embedding_model is unsupported or has compatibility issues with system version v4.9.11, leading to model loading or invocation failures.
Knowledge base index disappears automatically: This might occur if an unhandled exception happens during file parsing or vector writing, preventing index data from being persisted or causing it to be erroneously deleted by background cleanup tasks.
Significant drop in recall rate in a multilingual environment: This suggests the current embedding_model performs poorly in handling non-English medical terminology, failing to effectively capture the semantic features of multilingual text.

How to Verify Configuration

Upload clinical trial protocols containing different sections and tables. Check if the document is reasonably chunked under the Chunk size (Chunk Length) and Overlap Length configurations, ensuring semantic units are not broken.
Use query statements containing specific gene names, drug dosage units (mg/kg), and inclusion/exclusion criteria. Verify if the recall results include the expected relevant document snippets and evaluate the reasonableness of the Similarity threshold (Similarity Threshold).
Simulate high-frequency data update scenarios. Observe the execution time of knowledge base incremental indexing tasks. Check if the PARSE_FILE_TIMEOUT_SECONDS configuration meets processing requirements and that existing index data is not accidentally deleted.
After performing batch re-embedding on the knowledge base, compare the accuracy of query results before and after to assess the improvement provided by the new embedding_model.

The values provided are common starting points and should be measured against specific 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.