Data Characteristics
SMO (Site Management Organization) quality documents include clinical trial protocols, informed consent forms, ethics approvals, investigator brochures, standard operating procedures (SOPs), training records, and quality control/assurance reports. These documents are often unstructured text, stored as PDFs, Word files, or scanned images. Data sources are diverse, including sponsors, research centers, ethics committees, and internal SMO generation. Document update frequencies vary; protocol files update before project initiation and during amendments, while SOPs update annually or due to regulatory changes. Documents contain extensive specialized terminology, abbreviations, and critical metadata such as dates, version numbers, and signatures.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The unstructured nature of SMO quality documents requires HTTP interfaces to support various file formats for upload and parsing when integrating with external systems. Effective text content extraction and indexing are also necessary. Document update frequency is irregular, and trigger conditions are complex. External systems need event-driven or scheduled polling mechanisms to synchronize the latest versions and ensure knowledge base timeliness. Specialized terminology and metadata, such as protocol_id, version_number, and effective_date, require precise identification and structuring via the interface during import for subsequent knowledge retrieval and association. Documents often contain sensitive information, so HTTP interfaces must support encrypted transmission (HTTPS) and authentication mechanisms to meet compliance requirements.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
MAX_FILE_SIZE_MB | 200 MB | SMO documents, especially PDFs with charts, can be large, requiring support for large file uploads. |
PARSE_TIMEOUT_SECONDS | 300 seconds | Parsing complex PDFs or Word documents can be time-consuming; this avoids parsing failures due to timeouts. |
CHUNK_SIZE_CHARACTERS | 800–1200 characters | Balances context completeness with retrieval efficiency, ensuring RAG can get enough information for questions. |
METADATA_FIELDS | protocol_id, doc_type, version, effective_date, author | Stores key metadata in a structured way for precise filtering and retrieval. |
API_KEY_AUTH_ENABLED | true | Ensures the security of external system API calls, preventing unauthorized access. |
AIPROXY_API_ENDPOINT | Address of the deployed proxy service | Specifies the proxy service address for FastGPT's communication with large models; requires precise configuration for local deployments. |
Common Pitfalls
- An HTTP request returns a
401 Unauthorizederror becauseAIPROXY_API_TOKENor other authentication credentials are misconfigured or expired. - After a document upload, some critical fields in the knowledge base (e.g.,
effective_date) are empty because the document parser failed to correctly identify or extract the field. - Document updates pushed by an external system are not synchronized to the knowledge base in a timely manner, possibly due to incorrect Webhook configuration or an excessively long polling interval.
Verification Steps
- Upload an SMO SOP file containing typical metadata. Check if the
METADATA_FIELDSfor this document in the knowledge base are complete and correctly populated. - Call the knowledge base via the HTTP interface, using metadata like
protocol_idfor filtered retrieval. Verify the accuracy of the returned results. - Configure an external system to trigger a document update. Observe if the version number of the corresponding document in the knowledge base refreshes as expected and if the updated content takes effect.
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.