Deployment and Upgrade for Medical Information (MI) Response

Data Characteristics for This Category

Medical Information (MI) response traceability data primarily originates from internal compliance recording systems within healthcare institutions, product inquiry logs from pharmaceutical and medical device companies, and patient safety event reports. Data update frequency is relatively stable, typically occurring in batches, such as weekly or monthly imports of new response records. Document structures are often structured or semi-structured text, including inquirer details, timestamps, inquiry content, MI department responses, audit records, and final feedback. Fields include, but are not limited to, request_id (unique request identifier), query_text (original inquiry text), answer_text (MI department response text), reviewer_id (reviewer ID), timestamp (timestamp, precise to milliseconds), product_code (product code), disease_code (disease code), and adverse_event_flag (adverse event flag, boolean). Timestamps typically use Unix timestamps or ISO 8601 format, and text length is measured in characters.

Constraints Imposed by These Characteristics on Deployment and Upgrade

The structured and semi-structured nature of response traceability data requires configuring appropriate parsers during knowledge base deployment. This ensures accurate extraction of key fields like query_text and answer_text. The batch processing update model means deployment must consider scheduled tasks or event-driven incremental synchronization mechanisms. This avoids excessive system load from full updates. Fields containing product_code and disease_code require corresponding metadata indexing within the knowledge base. This supports precise retrieval and filtering. Boolean fields like adverse_event_flag directly influence recall strategy weighting and safety compliance alert logic. During upgrades, new version adjustments to specific field parsing rules may require compatibility handling or re-indexing of historical data. This ensures data consistency and query accuracy.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TYPE_LIST`	`["json", "csv", "txt"]`	Common formats for response traceability data, ensuring multi-source compatibility
`CHUNK_SIZE`	`500-800 characters`	Balances context completeness and retrieval efficiency, suitable for MI response text length
`OVERLAP_SIZE`	`50 characters`	Ensures contextual continuity between segments, preventing important information from being cut off
`MAX_KNOWLEDGE_BASE_SIZE`	`100 GB`	Reserves sufficient storage for historical data accumulation and future growth
`BATCH_INSERT_INTERVAL`	`600 seconds`	Accommodates batch import rhythm, reducing instantaneous write pressure
`EMBEDDING_BATCH_SIZE`	`32`	Balances embedding model computational resource consumption and processing speed

Three Common Mistakes

After a new version upgrade, some historical response records' product_code fields fail to match during retrieval. The new version parser's product code format validation rules changed, causing historical data to map incorrectly.
The knowledge base scheduled task for importing new batches of response traceability data experiences a timeout error. This occurs because the batch data volume is too large, and BATCH_INSERT_INTERVAL or PARSE_FILE_TIMEOUT_SECONDS parameters are not sufficiently configured.
The client displays some empty response results, and logs report DeserializationError. This happens because the frontend container was not updated to a version compatible with the backend during deployment, leading to a mismatch in data serialization and deserialization protocols.

Verification Steps

Upload a test data file containing query_text, answer_text, and product_code through the administration interface. Confirm all fields parse and ingest correctly.
Execute a full or incremental knowledge base synchronization. Observe system logs to confirm no abnormal errors and that data import time meets expectations.
In the FastGPT client, perform a retrieval using a query statement that includes product_code. Verify the recall accuracy and relevance of the returned results. Check that metadata like adverse_event_flag filters correctly.

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.