Conversation Logs and Auditing for Structured Analysis of

Data Characteristics

Data generated during peptide drug R&D primarily originates from electronic lab notebooks (ELN), mass spectrometry reports, nuclear magnetic resonance spectra, chromatography data, synthesis route maps, in vitro activity screening reports, and preclinical study documents. These documents are typically stored in formats like PDF, DOCX, and XLSX, with some proprietary formats. Data updates frequently, especially during early screening and optimization, with experimental records and analysis reports generated almost in real-time. Document structures are complex, including standardized experimental report templates and extensive unstructured experimental notes and observations. Fields include amino acid sequences, molecular weight, purity, solubility, IC50, and EC50. Units are diverse, such as nM, μM, mg/mL, and %, often accompanied by complex experimental condition descriptions.

Constraints Imposed by These Characteristics on "Conversation Logs and Auditing"

The complexity and diversity of peptide drug R&D documents place specific demands on conversation logs and auditing. High-frequency data updates require logs to support high throughput and real-time processing to ensure a complete audit trail. Extracting key information from unstructured data, such as peptide sequences, experimental conditions, and results, requires precise logging of each parsing parameter and identified entity for traceability and correction. Diverse fields and units require logs to clearly differentiate data types and provide context during auditing, for example, specifying whether the IC50 unit is nM or μM. When parsing fails, logs must detail the reasons for failure, such as incompatible document formats, missing key information, or the parsing model's misinterpretation of specific experimental conditions. This is crucial for subsequent model optimization and problem diagnosis.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`LOG_LEVEL`	`INFO`	Records key operations and errors, balancing performance with auditing requirements.
`MAX_LOG_AGE_DAYS`	`90 days` (90 days)	Covers a typical R&D cycle, facilitating traceability of historical conversations and document parsing processes.
`PARSE_FAILURE_DETAIL`	`true`	Records detailed parsing failure reasons, including error line numbers or matching patterns, for debugging.
`RETENTION_POLICY_TYPE`	`By time`	Cleans up logs based on time periods, complying with regulatory requirements.
`MAX_RESPONSE_TOKENS`	`2000 tokens`	Ensures complex information like peptide structures, experimental conditions, and results are fully presented in conversation responses.
`ENABLE_AUDIT_TRAIL`	`true`	Enables detailed audit tracking, recording user queries, system responses, and knowledge base call details.

Three Common Pitfalls

Conversation response times significantly increase, and knowledge base retrieval takes too long. This occurs because the knowledge base index is not optimized for peptide sequences or experimental conditions, leading to inefficient retrieval.
After parsing some R&D documents, key fields (e.g., IC50 values) are missing or displayed as null in conversations. This is typically due to inconsistent formatting of the field in the document or the parsing model failing to correctly identify its unit and value.
The system is inaccessible after startup, and logs show database connection failures. This might be due to an incorrect DATABASE_URL configuration or the database service not starting correctly, preventing FastGPT from establishing a connection.

Verification of Configuration

Submit a PDF document containing complex peptide sequences and various experimental conditions. Check if the conversation logs fully record the document parsing input parameters, extracted key entities (e.g., sequences, IC50 values), and corresponding units.
Simulate a network outage or database connection error. Check if system logs accurately record FATAL or ERROR level error messages, including specific error codes or exception stacks.
In a conversation, ask for specific data from an experimental report, such as "What is the EC50 of compound XYZ in report EXP-001?". Verify that the system's response matches the original document content and check if the conversation logs record the knowledge base's recall path and matched document snippets.
Periodically check log storage space usage to ensure that log retention policies (e.g., MAX_LOG_AGE_DAYS configuration) are effective as expected, and old log files are correctly archived or deleted.

The values provided are common starting points and should be measured against the reader's 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.