Data Characteristics for This Category
Patient Assistance Program (PAP) data originates primarily from pharmaceutical companies, charities, and healthcare providers. This data is typically structured or semi-structured, including patient diagnoses, medication records, program application statuses, approval results, and drug distribution and follow-up information. Data updates occur frequently, often daily or weekly in batches, triggered by new patient enrollments, changes in medication cycles, or program policy adjustments. Document structures vary, encompassing PDF application forms, Excel patient lists, and patient records within databases. Fields are highly specific, such as drug_batch_number, assistance_period_months, and patient_id_document. Fields involving monetary values and quantities require precision down to individual units, milligrams, or Chinese Yuan.
Constraints from These Characteristics on Deployment and Upgrade
High update frequency requires the FastGPT deployment to support efficient data synchronization and index rebuilding to ensure knowledge base timeliness. Diverse document structures, especially PDF application forms, challenge text extraction and parsing robustness, necessitating specialized parser configurations. Sensitive patient information fields, such as patient_id_document, demand strict adherence to privacy regulations during data processing. Deployment must prioritize data anonymization and access control policies. Precise units for monetary values and quantities require the model to accurately identify and process this numerical information during comprehension and response generation, preventing misunderstandings due to unit confusion. Deployments must reserve sufficient storage and computing resources to handle data growth and complex query demands.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accommodates longer parsing times for large PDF application forms, preventing timeouts. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Accounts for individual patient data packages potentially containing multiple scanned documents or detailed reports. |
Chunk size (Segment Length) | 800 characters | Ensures each text segment contains sufficient context while avoiding excessive length that could disperse semantics. |
Similarity threshold (Similarity Threshold) | 0.78 | Improves the relevance of recall results, reducing inaccurate patient information matches. |
Rerank result count (Reranked Return Count) | 5 items | Performs a secondary ranking on initial recall results, providing more precise consultation suggestions. |
mcpserverproxyendpoint | http://localhost:8080 or http://[internal_IP]:port | Points to the actual address and port where the local MCP server is running. |
Three Common Mistakes
- Login failures or incorrect account passwords after an upgrade: This usually stems from database structure upgrades or authentication mechanism changes. Check the upgrade guide for database migration steps or reset the administrator password.
- Text content extraction function failure: This might occur if the new version updates file parsing libraries or configurations, leading to incompatibility with older parsers. Check the
log_parse_error.txtfor specific error messages. - Query results do not match actual data or critical fields are missing: This typically indicates a failed data synchronization task or an incomplete knowledge base index rebuild, preventing the model from accessing the latest or complete patient assistance data.
How to Confirm Correct Configuration
- Upload a PDF application form containing complex tables and multiple pages. Verify that FastGPT accurately extracts all text content and correctly identifies key fields like
drug_batch_number. - Perform query tests via API or interface. Input general patient inquiries and verify that the returned results include the latest program policies, medication information, and application statuses. Check that monetary values in responses have correct units.
- Monitor data synchronization task logs to ensure daily or weekly data updates complete as scheduled and that the knowledge base index rebuilds promptly after data updates.
- Check system logs for error messages such as
PARSE_FILE_TIMEOUTorINDEX_BUILD_ERRORto confirm the system handles large files without anomalies.
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.