Knowledge Base Retrieval and Recall for Peptide Drug

Data Characteristics

Peptide drug data originates primarily from preclinical reports, clinical trial protocols, Investigator's Brochures (IB), Clinical Study Reports (CSR), and publicly available regulatory approval documents. Data update frequencies vary. Some regulatory documents may update annually, while clinical trial data generates continuously as trials progress. Document structures are diverse. PDF reports often contain numerous charts and unstructured text, while clinical trial databases are primarily structured tables. Key fields include drug name, sequence information (e.g., amino acid sequence), target, indications, pharmacokinetic (PK) parameters, pharmacodynamic (PD) parameters, toxicology data, and adverse event (AE) incidence rates. Units include mg/kg or μg/kg for dosage, nM or μg/mL for concentration, and hours, days, weeks for time.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The diversity of peptide drug data places specific demands on knowledge base construction and retrieval. Documents mixing unstructured text and charts require advanced document parsing capabilities to ensure accurate extraction of key information. For example, extracting PK parameters from tables within PDFs, or identifying specific adverse events from experimental results sections of reports. Continuous data updates necessitate incremental update and version management mechanisms in the knowledge base to avoid data redundancy or outdated information. The specificity of peptide sequence information means traditional keyword matching may be insufficient to capture semantic relevance, requiring the introduction of sequence similarity comparison or embedding-based vector retrieval. Furthermore, the mixture of different fields and units requires the ability to identify and distinguish this information during retrieval, such as differentiating between nM and μg/mL concentration units to avoid misinterpretation. For multi-source heterogeneous data, a unified schema integration is required to ensure consistency of retrieval results.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Accommodates longer experimental results descriptions and background information in peptide drug reports while ensuring semantic completeness of segments.
`Chunk Overlap Length` (Segment Overlap Length)	`100 characters`	Ensures contextual continuity, especially in cross-paragraph descriptions of drug mechanisms of action or adverse events.
`Recall count` (Recall Count)	`top 5 entries`	Balances retrieval efficiency and coverage. Recalling more items initially addresses complex queries and ambiguity.
`Similarity threshold` (Similarity Threshold)	Calibrate by measurement	Determine by manually evaluating recall results for typical queries, especially for unique fields like peptide sequences and targets.
`Rerank result count` (Reranked Return Count)	`3 entries`	After initial recall, further refine to the most relevant items, reducing redundant information for downstream agents.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles parsing time for large clinical study reports (e.g., CSRs up to hundreds of pages), preventing parsing failures due to timeouts.

Common Pitfalls

After a knowledge base update, retrieval results may not reflect the latest data in a timely manner, potentially leading the agent to provide outdated information. This typically occurs due to incomplete knowledge base index refreshes or improper incremental synchronization mechanism configuration.
When searching for pharmacokinetic parameters of a specific peptide drug, results may contain a large amount of irrelevant information or miss critical values. This can happen if the document parsing fails to effectively identify numerical fields or units in tables, leading to semantic information loss during vector embedding.
Encountering a 413 Payload Too Large error when inserting documents via API indicates that the single upload file size exceeds the UPLOAD_FILE_MAX_SIZE configuration limit. Adjust the parameter or upload in batches.

Verification of Configuration

Select a series of queries containing core information such as peptide sequences, targets, and adverse events. Verify the recall rate and accuracy of the retrieval results.
Randomly select multiple peptide drug-related documents in different formats (e.g., PDF, tables). Observe if segmentation in the knowledge base is reasonable and if key fields are correctly extracted and indexed.
Simulate high-concurrency query scenarios. Monitor the knowledge base's response time to ensure no long delays in actual applications. Compare with configured parameters like maxContext.
For data sources with different update frequencies in the knowledge base, regularly check if the latest data has been successfully synchronized and is retrievable. For example, compare the latest publication date on regulatory agency websites with the update time of corresponding documents in the knowledge base.

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