HTTP Interface and External Systems for Process Validation

Data Characteristics

Process validation pharmacovigilance data originates from batch reports, quality control records, deviation investigation reports, and adverse event (AE) reports during drug manufacturing. This data typically exists in structured or semi-structured documents, such as PDFs, Word documents, Excel spreadsheets, or CSV files exported from Laboratory Information Management Systems (LIMS). Update frequency varies, from several times daily to weekly or monthly, depending on production batches and adverse event occurrences. Document structures are complex, including fields like production date, batch number, process parameters, test results, adverse event descriptions, severity, and causality assessments. Process parameters, such as temperature, pressure, and time, often include specific units. Adverse event descriptions are frequently free-text, requiring natural language processing.

Constraints Imposed by "HTTP Interface and External Systems"

Diverse data sources and varying update frequencies for process validation data require flexible file upload and parsing capabilities from HTTP interfaces to handle multiple document formats. Extensive free-text adverse event descriptions necessitate robust data cleansing and entity extraction from external systems. This ensures accurate identification of critical information, such as drug names, symptoms, and timestamps. Differences in units and numerical ranges for process parameters require standardization during data transmission to prevent misinterpretation. Furthermore, batch reports and adverse event reports are interconnected. External systems must support multi-source data correlation to build a comprehensive pharmacovigilance chain and ensure traceability.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`chunkSize`	`800–1200 characters`	Balances context completeness and model processing efficiency, adapting to paragraph lengths in process validation documents.
`overlapRate`	`15%`	Ensures sufficient contextual overlap between adjacent text chunks, preventing critical information from being cut off.
`maxContext`	`32k tokens`	Accommodates complex process flows and adverse event descriptions, providing ample context for model analysis.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accounts for parsing time of large batch reports and historical adverse event data, preventing timeouts.
`similarityThreshold`	`0.75`	Recalls relevant information while filtering out background information not strongly related to process validation.
`recallQuantity`	`10 items`	Ensures retrieval of sufficient process parameters, batch records, and adverse event details to support comprehensive analysis.

Common Pitfalls

Issue: After an API call, the user field in the conversation log is empty or shows as anonymous. Reason: The caller did not correctly pass the chatId or userId parameter in the request header or body.
Issue: API call returns {"code":514,"statusText":"unAuthApiKey","message":"common:code_error.e error. Reason: The API Key used in the request is invalid, expired, or not correctly configured in the FastGPT instance.
Issue: Uploaded process validation report files are not parsed correctly, leading to missing knowledge base content. Reason: The file format (e.g., encrypted PDF) or internal structure (e.g., scanned document without OCR) does not meet parser expectations, or PARSE_FILE_TIMEOUT_SECONDS is set too short, causing large file parsing to be interrupted.

Verification Steps

Upload a typical process validation report. Check if corresponding text chunks are generated in the knowledge base and if chunk content is accurate and complete.
Simulate an adverse event report query. Observe if recalled knowledge entries include relevant process parameters, batch information, and historical adverse event records. Evaluate the relevance of the recalled entries.
Check the FastGPT backend conversation logs. Confirm that each API call correctly records chatId or userId and that no authentication failure error codes appear.

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.