Knowledge Base Retrieval and Recall for siRNA Nucleic Acid

Data Characteristics

siRNA nucleic acid drug regulatory documents typically originate from the National Medical Products Administration, Pharmacopoeia Commission, industry associations, and internal Standard Operating Procedures (SOPs). These documents include regulations, guidelines, technical requirements, and internal SOPs. They are primarily in PDF, Word, or structured text formats. Update frequency is high; regulations may be revised annually, and SOPs are updated periodically due to technological advancements or production process optimizations.

Document structure is complex. It contains extensive specialized terminology, abbreviations, technical parameters, dosage units, experimental method descriptions, and references. For example, measurement units like "nM," "μg/mL," and "OD260/280" appear frequently. Different documents may have unit conversions or differing expressions. Documents often include multi-level headings, charts, and appendices. Some SOPs present operational steps as flowcharts.

Constraints on Knowledge Base Retrieval and Recall

The complex structure and specialized nature of siRNA nucleic acid drug regulatory documents pose challenges for knowledge base retrieval and recall.

First, frequent specialized terms and abbreviations require the tokenizer to recognize medical domain vocabulary. This prevents core concepts from being split.

Second, multi-level headings and flowcharts mean that pure text segmentation can compromise information integrity. This is especially true when an operational step spans multiple paragraphs or pages.

Third, differing units and conversion requirements across documents necessitate that the retrieval system identifies and associates these concepts. This improves recall accuracy.

Additionally, the frequency of regulatory updates demands an efficient incremental update mechanism for the knowledge base. This ensures the timeliness of retrieval results. Precise localization of specific chapters or appendices also requires knowledge block granularity to support such detailed recall.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Balances text integrity and retrieval efficiency. It prevents context loss while controlling the amount of content per segment.
`Chunk overlap` (Segment Overlap)	`100–200 characters`	Ensures the relevance of key information across paragraphs, especially for specialized terminology and process descriptions.
`embedding_model`	`text-embedding-ada-002` or domain-specific models	Enhances understanding of biomedical professional vocabulary and concepts, improving semantic matching.
`Recall count` (Number of Retrieved Items)	`Top 5-8 items`	Provides sufficient relevant context, considering document complexity and the breadth of user queries.
`Similarity threshold` (Similarity Threshold)	Calibrated by actual measurement	Adjusted based on actual recall effectiveness and false positive rates. Typically between `0.75-0.85`.
`trainingType`	`qa` or `chunk`	`qa` is suitable for structured question-answer pairs. `chunk` is suitable for general retrieval of unstructured documents.

Common Pitfalls

Symptom: A user asks "What is the siRNA purity standard?" The retrieval results do not include key paragraphs related to "OD260/280" or "HPLC." Reason: The tokenizer failed to recognize professional abbreviations or specific detection methods, leading to semantic matching failure.
Symptom: After uploading an SOP document with multi-level headings, a user queries a specific operational step. The system retrieves text fragments lacking complete context. Reason: Chunk size (Segment Length) was too short or Chunk overlap (Segment Overlap) was not set appropriately. This broke the integrity of the operational steps during segmentation.
Symptom: The knowledge base creation interface returns a 400 Bad Request error, indicating an incorrect format for the data-raw field. Reason: The trainingType option does not match the actual data structure passed. For example, passing plain text while selecting the qa type.

Verification of Configuration

Select representative siRNA nucleic acid drug regulatory documents. Submit queries containing key terms, abbreviations, and process steps from these documents. Check if the retrieved results include correct core information and relevant context.
Upload documents of different structural types (e.g., plain text SOPs, guidelines with charts) via the API interface. Observe if the system correctly parses and generates knowledge blocks. Check the completeness of the knowledge block content.
Perform incremental update tests. Upload a revised version of an existing regulatory document. Confirm that the knowledge base content is correctly replaced or updated and that old version information is no longer retrieved.
For query results, evaluate the accuracy of professional terminology and measurement unit recognition in the retrieved segments. This assesses the appropriateness of the embedding_model selection.

Note: The values provided are common starting points. They 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.