Conversation Log and Audit for R&D Document Structural

Data Characteristics

Stability study data primarily originates from experimental reports, analytical certificates, batch production records, and quality standard documents. These documents typically have a low update frequency, with updates occurring periodically or per batch (e.g., after each production batch or at the end of a stability study period). Document structures are highly standardized, often adhering to guidelines like ICH Q1A/Q1B. They include fixed fields such as batch number, production date, expiry date, storage conditions, test items, test methods, test results (e.g., content, purity, degradation products), and descriptions of anomalies. Test results usually include clear units (e.g., %, ppm, μg/mL, °C, %RH) and involve data sequences across multiple time points.

Constraints Imposed by These Characteristics on "Conversation Log and Audit"

The highly standardized structure and low update frequency of stability study documents make the completeness and traceability of conversation logs core requirements. The logging system must accurately record every access, query, and parsing operation on document content. This includes user identity, operation time, query content, and the system's parsing results. Since documents contain extensive sequence data and critical metrics, logs must accurately capture requests for data at specific time points or for specific test items. Fixed document structures mean parsing errors or data extraction anomalies are easier to identify. Therefore, logs should detail the parser's operational status and any warning messages. The low update frequency reduces the need for real-time log analysis but increases the importance of long-term auditing and compliance reviews, requiring log data to support long-term storage and efficient retrieval.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`logLevel`	`INFO`	Captures critical operations and parsing results, avoiding excessive log volume that impacts performance.
`maxLogRetentionDays`	`3650` (10 years)	Meets long-term compliance audit requirements in the biopharmaceutical industry.
`Chat historyMaximum Storage Entries`	`200`	Ensures complete traceability of a single conversation process, covering complex query scenarios.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Stability report files are often large, requiring sufficient parsing time.
`Audit logs Storage Backend`	`MongoDB`	Provides flexible document storage and powerful query capabilities for complex audits.
`knowledge base document Version Control`	Enable	Records document modification history, ensuring parsing results are based on specific versions.

Common Pitfalls

Conversation records are missing in the storage backend. This appears as an empty history when querying past conversations. The cause is often an abnormal connection to the storage service or insufficient write permissions, preventing log data persistence.
The parsing log lacks extraction records for critical fields. This means an audit cannot confirm if a specific metric was queried. This can happen if the parser configuration (parserConfig) does not cover all important fields, or if field mapping (fieldMapping) is incorrect.
When querying data for a specific batch or time point, log records show inaccurate results. This appears as discrepancies between the logged return content and the actual document. This is usually due to outdated knowledge base indexes or a recall strategy (recallStrategy) not optimized for time-series data.

Verification Steps

Randomly select several historical conversations. Verify that corresponding complete conversation records exist in the log storage backend (e.g., MongoDB's chat_history collection). Check if the content of the request and response fields matches.
Simulate a query for the content metric for a specific batch and time point (e.g., T=12Units months) in a stability report. Then, check if the parsing log accurately records the query request, the extracted field values, and their units.
Export audit logs via the FastGPT administration interface. Confirm that the log file downloads successfully and its content format meets expectations, including key information such as user, time, operation type, and relevant document IDs.
Attempt to upload a stability report document containing a known error. Observe if parsing failure or warning messages appear in the log, and verify if the error code (errorCode) matches expectations.

Note: 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.