Data Characteristics
Contract Development and Manufacturing Organizations (CDMOs) gather data from diverse and heterogeneous sources for registration dossier preparation. This data primarily includes laboratory records, analysis reports, batch production records, quality control data from the R&D phase, and clinical trial data. Data exists in both structured formats (e.g., test results from LIMS, bills of material from SAP) and unstructured formats (e.g., PDF trial reports, Word SOPs, scanned raw records). Data updates frequently, especially during critical R&D and production stages, continuously generating new experimental data and batch records. Document structures are complex, often adhering to international regulatory guidelines like ICH and FDA. They contain detailed chapters, sub-sections, and appendices with various fields such as batch number, test item, specification, result, and units (mg/mL, ppm, % etc.).
Constraints on HTTP Interface and External Systems
The data characteristics of CDMO registration dossier preparation impose specific requirements on HTTP interfaces and external systems. Frequent updates and heterogeneous data sources mean interfaces must support various input data formats, including structured JSON/XML and binary file streams (PDF, Docx). The complex document structures and extensive unstructured content require robust document parsing capabilities to extract key information from PDFs and Word files. Diverse fields, units, and strict compliance requirements make data validation and standardization critical. Interfaces need to support custom validation rules and data transformation logic. Furthermore, due to data sensitivity and compliance, interface security, audit logs, and version control are essential to ensure data transmission and processing integrity and traceability.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Accommodates large research reports and batch production records, which often include numerous charts and attachments. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Ensures sufficient time for complex PDF or Word documents to complete content parsing and structuring. |
CHUNK_SIZE | 800–1200 characters | Balances semantic integrity of text blocks with model processing efficiency, suitable for dossier paragraph lengths. |
METADATA_EXTRACT_FIELDS | batch number, item name, Test Date | Ensures precise extraction of core metadata from documents for subsequent retrieval and association. |
HTTP_REQUEST_TIMEOUT | 120 seconds | Addresses potential response delays from external LIMS or ERP systems, ensuring stable data synchronization. |
MAX_RETRIES_ON_FAIL | 3 times | Reduces data synchronization failures due to transient network fluctuations or temporary unavailability of external systems. |
Common Pitfalls
- Symptom: After uploading a CSV file, the data displayed by the system does not match the original file content, or some fields are missing. Reason: The interface's default CSV parser might not correctly identify the file encoding or delimiter, leading to data parsing errors, especially when CSV files contain complex nested structures or non-standard characters.
- Symptom: An HTTP 400 error is returned when making an HTTP request to an external system, indicating an incorrect request body format. Reason: The request body's data format (e.g., JSON, XML) or field names do not match the target external system API's expectations, or necessary header information, such as
Content-Type, is missing. - Symptom: Key information from a document cannot be retrieved from the knowledge base after uploading it via API, or retrieval results are inaccurate. Reason: The document parsing or chunking strategy is not suitable for CDMO dossier characteristics. For example, the system might fail to correctly identify tables or image text within the document, or chunks are too short, leading to context loss and impacting semantic understanding.
Configuration Verification
- Upload a PDF batch production record containing complex tables and multi-level headings via API. Then, check the parsing results in the knowledge base to ensure all key information (e.g., batch, test results, units) is correctly identified and retrievable.
- Use Postman or another API testing tool to simulate an external LIMS system sending a JSON request with structured test data to FastGPT. Verify that FastGPT correctly receives and processes the data and synchronizes it to the specified knowledge base or Agent.
- Configure an HTTP callback interface to trigger FastGPT's knowledge base update when external system data changes. Monitor FastGPT's logs to confirm the callback request is successfully received and the knowledge base content is incrementally updated as expected.
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.