Standard Answer Library: HTTP Interface and External Systems

Data Characteristics of This Category

Data for standard answer libraries, specifically for Medical Information (MI) responses in the biopharmaceutical domain, typically originates from authoritative sources. These sources include clinical trial reports, drug inserts, medical guidelines, and professional literature. The data exists in structured or semi-structured formats. Examples include Frequently Asked Questions (FAQ) pairs, descriptions of drug mechanisms, lists of adverse reactions, and dosage instructions.

Update rhythms closely align with drug lifecycles and medical advancements. For instance, new indication approvals, safety information updates, and clinical study releases drive updates. Updates may occur quarterly or semi-annually, with critical updates released immediately. Document structures are highly standardized. Common fields include a question, a standard answer, reference links, an update date, and potentially product codes (product_id) or disease codes (disease_code). This ensures the accuracy and traceability of medical information.

Constraints on "HTTP Interface and External Systems" from These Characteristics

The data characteristics of standard answer libraries impose specific constraints on HTTP interfaces and external system integration. First, the authoritative nature and update frequency of the data sources demand high reliability and transactional capabilities from the interface. This ensures complete data synchronization, for example, by preventing partial data writes during updates.

Second, the structured and semi-structured document characteristics mean interface design must support complex data models, such as nested JSON objects. This allows accurate transmission of complete responses, including questions, answers, references, and metadata. Field standardization, such as question_id, answer_text, and reference_url, requires strict adherence to predefined protocols for interface parameters and return bodies.

Furthermore, due to the sensitive nature of medical information, interfaces must support strict authentication and authorization mechanisms. Data transmission encryption is also essential, for example, through HTTPS. For update operations, idempotency must be considered to prevent data inconsistencies caused by duplicate submissions.

Configuration Settings

Configuration Item	Recommended Value	Basis for Recommendation
`requestTimeout`	`60000 ms`	Accommodates large external system data volumes or network latency to ensure complete responses.
`maxRetries`	`3 times`	Addresses transient network fluctuations or temporary unavailability of external services, improving robustness.
`batchSize`	`500 items/batch`	Balances external system processing capabilities with data transfer efficiency.
`authHeaderName`	`Authorization`	Common industry standard, easy to integrate.
`contentType`	`application/json`	Standard format for structured data transmission, good compatibility.
`updateFrequency`	`Daily 02:00 UTC`	Ensures daily synchronization of the latest medical information, avoiding peak business hours.

Three Common Mistakes

Symptom: The external system returns an HTTP 500 Internal Server Error, and logs show Database connection timeout. Reason: The requestTimeout parameter in FastGPT is set too short, failing to wait for the external database to complete complex queries.
Symptom: MI response data submitted via API appears as duplicate records in the knowledge base, or some field content is missing. Reason: API calls lack idempotency handling, or batchSize is too large, leading to incomplete retry mechanisms after partial data processing failures.
Symptom: After knowledge base content updates, users querying FastGPT still receive old MI responses. Reason: The external system update trigger mechanism is not configured correctly, or the updateFrequency interval is too long, causing knowledge base synchronization delays.

How to Confirm Correct Configuration

Through the FastGPT administration interface, randomly select 5-10 newly synchronized MI responses from the knowledge base. Verify that key fields like answer_text and reference_url match the external source system.
Execute a complete HTTP interface data synchronization operation. Monitor FastGPT's log output to confirm no HTTP 4xx or HTTP 5xx error codes appear. Also, confirm that data processing time is within the requestTimeout threshold.
Simulate user queries for several recently updated MI responses. Verify that FastGPT accurately retrieves and provides the correct, latest response. Check that the returned source field points to the correct knowledge base entry.

Note: 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.