Knowledge Base Retrieval and Recall for Structured Analysis

Data Characteristics

Peptide drug R&D data comes from various sources. These include internal lab records, synthesis reports, mass spectrometry analysis reports, pharmacokinetic (PK)/pharmacodynamic (PD) study documents, and public databases like UniProt, PDB, and ClinicalTrials.gov. Data update frequencies vary; lab records might update daily, while clinical trial data releases are phased. Documents often contain semi-structured or unstructured text, such as experimental procedures, result descriptions, batch information, sequence details, modification sites, activity data, and toxicity reports. Fields may include amino acid sequences, molecular weight, purity, half-life, IC50, and EC50. Units cover Dalton (Da), moles per liter (mol/L), nanomoles (nM), and micrograms per milliliter (µg/mL), often with specific modifiers or annotations.

Constraints on Knowledge Base Retrieval and Recall

Peptide sequence specificity requires precise or approximate matching of sequence fragments. Traditional keyword matching may not capture biological activity or structural features. Extensive specialized terminology and abbreviations, along with varied phrasing across different experimental reports, complicate semantic understanding. Critical numerical information, such as activity and toxicity data, often appears in tables or charts and requires structured extraction for effective retrieval. Additionally, the long R&D cycle for peptide drugs leads to large historical data accumulation, demanding high storage capacity and retrieval efficiency from the knowledge base. Subtle differences across batches or experimental conditions can significantly alter peptide properties. Therefore, retrieval results need contextual relevance to avoid misinterpreting isolated information. Strict requirements for units and dimensions necessitate considering numerical accuracy and consistency during retrieval.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size`	`800–1200 characters`	Balances contextual completeness and embedding model processing capability for long experimental reports.
`Recall count`	`Top 10–15 entries`	Ensures coverage of sufficient potentially relevant document segments, especially with low peptide sequence similarity.
`Similarity threshold`	`0.75–0.85`	Accommodates precise matching needs for peptide sequences while allowing for some variation.
`Rerank result count`	`Top 5 entries`	Reduces downstream language model processing load and focuses on the most relevant peptide data or experimental results.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Addresses parsing time requirements for large experimental reports, mass spectrometry data, or historical literature.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates PDF documents containing numerous charts, images, or raw data.

Common Pitfalls

In the knowledge base orchestration workflow, even with the final AI answer plugin's optimization option disabled, answers may still deviate from expectations. This might occur if the knowledge base plugin's problem optimization was activated earlier, affecting recall results and preventing correction at the final stage.
After using a Rerank model, the interface might show "Result Reranking" as disabled or marked with an "X". This could be due to incorrect Rerank service configuration, model loading failure, or network connectivity issues with the knowledge base service. Check service logs for details.
After importing JSON format documents, the knowledge base selection interface might appear empty. This usually indicates that the imported data structure does not match FastGPT's expected JSON format, or critical fields (like content or metadata) are missing or incorrectly named.

Verification Steps

Upload different types of peptide drug R&D documents (e.g., experimental records, sequence analysis reports) and check if segmentation is reasonable, especially for the completeness of key information such as sequences and activity data.
Perform searches for specific peptide sequences or activity metrics. Verify that the recalled results include the expected documents and assess the relevance of recalled items to the query, paying close attention to accurate capture of numerical and unit information.
In the knowledge base orchestration workflow, test various query scenarios (e.g., finding toxicity data for specific modified peptides). Observe if the final AI answer accurately cites relevant information from the knowledge base and confirm that key fields in the answer align with the source documents.

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.