Data Characteristics
siRNA nucleic acid drug R&D documents originate from early-stage research. These include experimental reports, patent applications, preclinical study data, and internal compound synthesis and screening records. Document updates are infrequent, typically occurring after project milestones or significant experimental results. Documents are primarily unstructured text, containing extensive experimental methods, results, charts, descriptions, and references. Key fields include siRNA sequence, target gene, delivery system, in vitro/in vivo activity data (e.g., IC50, KD values), toxicology indicators, synthesis batch, and purity. Units are commonly biochemical units like nanomolar (nM), microgram (µg), and milliliter (mL), often accompanied by specific experimental condition descriptions.
Constraints Imposed by These Characteristics on Vector Models and Indexing
The specificity of siRNA sequences, the complexity of target genes, and the diversity of experimental conditions require vector models to capture subtle semantic differences. Structured data embedded within unstructured text demands segmentation strategies that effectively identify and preserve critical information. For example, associating siRNA sequences with their corresponding activity data and delivery systems challenges traditional text segmentation methods. Infrequent document updates allow for a periodic full-rebuild indexing strategy to ensure data consistency. Numerical values and units in experimental data require vector models to understand their dimensional relationships, preventing information distortion from simple word frequency counts. Additionally, specialized terminology and abbreviations common in patents and reports necessitate comprehensive vocabulary and pre-trained models covering the biomedical domain.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Size | 500–800 characters | Preserves contextual semantics while preventing individual chunks from becoming too long and diluting key information, especially sequence and activity data. |
Recall Count | Top 10–15 chunks | Ensures coverage of multiple potentially relevant experimental report segments, improving the accuracy of subsequent re-ranking. |
Similarity Threshold | Calibrate by measurement | Determine through grayscale testing based on actual recall effectiveness and false positive rates. A range of 0.75-0.85 is typically suggested. |
Rerank Return Count | Top 5 chunks | Balances response speed with result quality, focusing on the most relevant experimental data and conclusions. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates large experimental reports or patent documents, ensuring sufficient time for parsing. |
Vector Model | text-embedding-v3-large or equivalent | Captures complex semantic information such as biological sequences, targets, and experimental conditions, improving embedding quality. |
Three Common Mistakes
- During multimodal embedding model testing, logs show
{"error":{"code":"Invalid Parameter"}}. This indicates incorrect API key or model name configuration, resulting in request parameters that do not meet the model provider's requirements. - After a version upgrade, older vector database data is not recognized by the new system or query results are inaccurate. This occurs when data migration or index rebuilding is not performed, leading to incompatible data formats or index structures between versions.
- Query results are missing critical siRNA sequence or activity data fields. This happens when key information is truncated during document segmentation or not effectively associated with surrounding context.
How to Confirm Proper Configuration
- For typical queries (e.g., "siRNA sequences and IC50 data for a specific target gene"), verify that recall results include key segments from multiple relevant documents and check the completeness of fields like
siRNA sequence,target gene, andIC50 value. - Use the FastGPT interface to check the indexing status of imported documents in the knowledge base. Confirm all files are successfully parsed and vector indexes created, with no errors or timeouts.
- Randomly select 5–10 queries. Compare FastGPT's recall results with manual search results. Evaluate the accuracy and relevance of the top 5 results and determine an acceptable threshold based on actual business needs.
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.