Data Characteristics
Supplier audit quality documents in the biopharmaceutical sector have distinct characteristics. Data primarily originates from audit reports, CAPA (Corrective and Preventive Action) records, supplier qualification documents (e.g., GMP certificates, production licenses), and historical audit data. These documents are typically in PDF, Word, or scanned image formats, with low structural consistency. However, they contain critical information such as audit findings, risk levels, non-conformance descriptions, corrective action plans, and completion status.
Updates are usually periodic, such as annually or irregularly based on risk levels. New audits or supplier qualification updates trigger these updates. Common fields include "Audit Date," "Non-conformance ID," "Risk Rating (High/Medium/Low)," "Correction Deadline," and "Responsible Person." Time units are typically year, month, and day, while risk ratings are often enumerated values.
Constraints Imposed by HTTP Interface and External Systems
The semi-structured nature of supplier audit quality documents means HTTP interface integration must focus on content extraction and parsing capabilities. Since document updates are not frequent, the interface design should prioritize batch processing and asynchronous uploads. This accommodates importing large volumes of historical documents at once, preventing timeouts from excessively large single requests.
Diverse document formats require the interface to support multiple file types and trigger backend file preprocessing. Specific fields and units necessitate strict validation and standardization before data ingestion. For example, date formats must be uniform, and risk ratings must conform to predefined enumerations. This requires parameter constraints at the interface layer or cleansing at the data processing layer. Additionally, documents may contain sensitive information, so authentication and authorization mechanisms must be robust to ensure data transmission security.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 50 MB | Audit reports and qualification documents can be large, but individual files typically do not exceed this limit. This balances upload efficiency with server resources. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing complex or scanned PDF documents can be time-consuming. This provides sufficient time to prevent parsing interruptions. |
maxContext | 2000 characters | Audit findings and corrective action descriptions are often extensive. This ensures key information is effectively segmented and indexed. |
Chunk size | 500 characters | Each segment contains sufficient semantic information for subsequent retrieval and Q&A, avoiding excessive fragmentation. |
Recall count | Top 5 entries | Supplier audit scenarios demand high accuracy. Recalling a small number of the most relevant document snippets improves precision. |
similarity_threshold | 0.75 | Filters for highly relevant content, excluding noise, to ensure the professionalism of search results. |
Common Pitfalls
- After file upload, the system remains unresponsive for an extended period or returns a 504 Gateway Timeout error. This typically occurs when
PARSE_FILE_TIMEOUT_SECONDSis set too low, and parsing large or complex PDF documents does not complete before the interface times out. - Uploaded file content cannot be parsed correctly, resulting in empty or incomplete documents in the knowledge base. This may stem from non-standard document formats or non-text content (e.g., images) if the preprocessing service is not optimized for these cases.
- When batch importing historical audit records via the interface, some critical fields (e.g., "Correction Deadline") have inconsistent formats or are missing after ingestion. This usually happens due to a lack of strict parameter validation at the interface layer or if the data processing layer does not standardize diverse date formats.
Verification
- Select various formats (PDF, Word, scanned images) and sizes of supplier audit documents. Upload them one by one via the HTTP interface. Check if the corresponding document content in the knowledge base is complete and searchable.
- Simulate a batch upload of 100 audit reports. Observe system processing time and resource usage to ensure parsing completes within
PARSE_FILE_TIMEOUT_SECONDS. - For uploaded documents, use FastGPT's retrieval function to query using key fields (e.g., "Non-conformance ID," "Risk Rating") from the document. Verify that the recalled document snippets are accurate and highly relevant.
- Check the metadata of imported supplier audit documents in the knowledge base. Ensure all custom fields (e.g., audit date, responsible person) are correctly extracted and standardized.
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.